u-10bei/qwen3-4binst_cot6_e1_merged
Text Generation
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[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Convert the following TOML to XML:\ntxn = \"e7d866e4-e217-4769-af91-6b199ac6c611\"\namount = 92.38\nstatus = \"completed\"\n[[audit_log]]\naction = \"created\"\nby = \"michaelmendez\"\ntimestamp = \"2016-05-22T16:08:01.325206\"\n\n[[audit_log]]\naction = \"created\"\nby = \"whitejoshua\"\ntimestamp = \"1980-02-24T13:33:42.943177\"\n\n[[audit_log]]\naction = \"created\"\nby = \"mollytaylor\"\ntimestamp = \"1971-07-22T05:19:29.270955\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:07.362521\"\nversion = \"v4.0\"\ntags = [ \"range\", \"cover\", \"use\", \"stand\", \"mean\",]\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 5 root fields (txn, amount, status...) and a nesting depth of 3.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<transaction_record>\n<txn>e7d866e4-e217-4769-af91-6b199ac6c611</txn>\n<amount>92.38</amount>\n<status>completed</status>\n<meta_info>\n<generated_at>2026-01-22T04:03:07.362521</generated_at>\n<version>v4.0</version>\n<tags>range</tags>\n<tags>cover</tags>\n<tags>use</tags>\n<tags>stand</tags>\n<tags>mean</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>michaelmendez</by>\n<timestamp>2016-05-22T16:08:01.325206</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>whitejoshua</by>\n<timestamp>1980-02-24T13:33:42.943177</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>mollytaylor</by>\n<timestamp>1971-07-22T05:19:29.270955</timestamp>\n</audit_log>\n</transaction_record>"
}
] |
{
"format": "xml",
"complexity": "complex",
"schema": "transaction_record",
"type": "conversion",
"prompt": "Convert the following TOML to XML:\ntxn = \"e7d866e4-e217-4769-af91-6b199ac6c611\"\namount = 92.38\nstatus = \"completed\"\n[[audit_log]]\naction = \"created\"\nby = \"michaelmendez\"\ntimestamp = \"2016-05-22T16:08:01.325206\"\n\n[[audit_log]]\naction = \"created\"\nby = \"whitejoshua\"\ntimestamp = \"1980-02-24T13:33:42.943177\"\n\n[[audit_log]]\naction = \"created\"\nby = \"mollytaylor\"\ntimestamp = \"1971-07-22T05:19:29.270955\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:07.362521\"\nversion = \"v4.0\"\ntags = [ \"range\", \"cover\", \"use\", \"stand\", \"mean\",]\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 5 root fields (txn, amount, status...) and a nesting depth of 3.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<transaction_record>\n<txn>e7d866e4-e217-4769-af91-6b199ac6c611</txn>\n<amount>92.38</amount>\n<status>completed</status>\n<meta_info>\n<generated_at>2026-01-22T04:03:07.362521</generated_at>\n<version>v4.0</version>\n<tags>range</tags>\n<tags>cover</tags>\n<tags>use</tags>\n<tags>stand</tags>\n<tags>mean</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>michaelmendez</by>\n<timestamp>2016-05-22T16:08:01.325206</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>whitejoshua</by>\n<timestamp>1980-02-24T13:33:42.943177</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>mollytaylor</by>\n<timestamp>1971-07-22T05:19:29.270955</timestamp>\n</audit_log>\n</transaction_record>",
"estimated_tokens": 329
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Reformat the CSV below into YAML:\nexperiment_id,title,metrics.accuracy,metrics.loss,metrics.epoch,config.batch_size,config.optimizer,config.lr\r\nEXP-548343,Analysis of special,0.9516,0.2734,49,64,sgd,0.001\r\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id: EXP-548343\ntitle: Analysis of special\nmetrics:\n accuracy: 0.9516\n loss: 0.2734\n epoch: 49\nconfig:\n batch_size: 64\n optimizer: sgd\n lr: 0.001\n"
}
] |
{
"format": "yaml",
"complexity": "simple",
"schema": "experiment_result",
"type": "conversion",
"prompt": "Reformat the CSV below into YAML:\nexperiment_id,title,metrics.accuracy,metrics.loss,metrics.epoch,config.batch_size,config.optimizer,config.lr\r\nEXP-548343,Analysis of special,0.9516,0.2734,49,64,sgd,0.001\r\n",
"output": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id: EXP-548343\ntitle: Analysis of special\nmetrics:\n accuracy: 0.9516\n loss: 0.2734\n epoch: 49\nconfig:\n batch_size: 64\n optimizer: sgd\n lr: 0.001\n",
"estimated_tokens": 186
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Convert the following TOML to JSON:\ncontract = \"cultivate cross-media markets\"\nparties = [ \"Cooper Group\", \"Lawrence Inc\",]\nsigned = false\n[[audit_log]]\naction = \"created\"\nby = \"daniel16\"\ntimestamp = \"2010-06-15T09:38:22.777743\"\n\n[[audit_log]]\naction = \"created\"\nby = \"wshaw\"\ntimestamp = \"2025-01-18T17:33:58.322335\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:07.351677\"\nversion = \"v1.0\"\ntags = [ \"could\", \"field\", \"interest\", \"between\", \"pressure\",]\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 5 root fields (contract, parties, signed...) and a nesting depth of 3.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"contract\": \"cultivate cross-media markets\",\n \"parties\": [\n \"Cooper Group\",\n \"Lawrence Inc\"\n ],\n \"signed\": false,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:07.351677\",\n \"version\": \"v1.0\",\n \"tags\": [\n \"could\",\n \"field\",\n \"interest\",\n \"between\",\n \"pressure\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"daniel16\",\n \"timestamp\": \"2010-06-15T09:38:22.777743\"\n },\n {\n \"action\": \"created\",\n \"by\": \"wshaw\",\n \"timestamp\": \"2025-01-18T17:33:58.322335\"\n }\n ]\n}"
}
] |
{
"format": "json",
"complexity": "complex",
"schema": "contract",
"type": "conversion",
"prompt": "Convert the following TOML to JSON:\ncontract = \"cultivate cross-media markets\"\nparties = [ \"Cooper Group\", \"Lawrence Inc\",]\nsigned = false\n[[audit_log]]\naction = \"created\"\nby = \"daniel16\"\ntimestamp = \"2010-06-15T09:38:22.777743\"\n\n[[audit_log]]\naction = \"created\"\nby = \"wshaw\"\ntimestamp = \"2025-01-18T17:33:58.322335\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:07.351677\"\nversion = \"v1.0\"\ntags = [ \"could\", \"field\", \"interest\", \"between\", \"pressure\",]\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 5 root fields (contract, parties, signed...) and a nesting depth of 3.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"contract\": \"cultivate cross-media markets\",\n \"parties\": [\n \"Cooper Group\",\n \"Lawrence Inc\"\n ],\n \"signed\": false,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:07.351677\",\n \"version\": \"v1.0\",\n \"tags\": [\n \"could\",\n \"field\",\n \"interest\",\n \"between\",\n \"pressure\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"daniel16\",\n \"timestamp\": \"2010-06-15T09:38:22.777743\"\n },\n {\n \"action\": \"created\",\n \"by\": \"wshaw\",\n \"timestamp\": \"2025-01-18T17:33:58.322335\"\n }\n ]\n}",
"estimated_tokens": 286
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Transform this data from XML to CSV:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<customer_review>\n<user>mcbridedennis</user>\n<rating>4</rating>\n<comment>Now through hospital one.</comment>\n</customer_review>"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 3 root fields (user, rating, comment) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nuser,rating,comment\r\nmcbridedennis,4,Now through hospital one.\r\n"
}
] |
{
"format": "csv",
"complexity": "simple",
"schema": "customer_review",
"type": "conversion",
"prompt": "Transform this data from XML to CSV:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<customer_review>\n<user>mcbridedennis</user>\n<rating>4</rating>\n<comment>Now through hospital one.</comment>\n</customer_review>",
"output": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 3 root fields (user, rating, comment) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nuser,rating,comment\r\nmcbridedennis,4,Now through hospital one.\r\n",
"estimated_tokens": 163
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Generate social media post data in YAML format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The social_media_post should contain a dictionary with 3 root fields (user, post, likes) and a nesting depth of 1.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nuser: boyersylvia\npost: Interview knowledge ready hold establish hotel several. Money subject away without\n special. Oil whether short air become.\nlikes: 450\n"
}
] |
{
"format": "yaml",
"complexity": "simple",
"schema": "social_media_post",
"type": "generation",
"prompt": "Generate social media post data in YAML format.",
"output": "Approach:\n1. Define the schema: The social_media_post should contain a dictionary with 3 root fields (user, post, likes) and a nesting depth of 1.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nuser: boyersylvia\npost: Interview knowledge ready hold establish hotel several. Money subject away without\n special. Oil whether short air become.\nlikes: 450\n",
"estimated_tokens": 176
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Generate customer review data in JSON format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The customer_review should contain a dictionary with 5 root fields (user, rating, comment...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"user\": \"eanthony\",\n \"rating\": 4,\n \"comment\": \"Wind through for son professional pull heart.\",\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:07.271505\",\n \"version\": \"v5.0\",\n \"tags\": [\n \"population\",\n \"stay\",\n \"message\",\n \"management\",\n \"raise\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"lisa72\",\n \"timestamp\": \"1999-10-21T21:51:01.956523\"\n }\n ]\n}"
}
] |
{
"format": "json",
"complexity": "complex",
"schema": "customer_review",
"type": "generation",
"prompt": "Generate customer review data in JSON format.",
"output": "Approach:\n1. Define the schema: The customer_review should contain a dictionary with 5 root fields (user, rating, comment...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"user\": \"eanthony\",\n \"rating\": 4,\n \"comment\": \"Wind through for son professional pull heart.\",\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:07.271505\",\n \"version\": \"v5.0\",\n \"tags\": [\n \"population\",\n \"stay\",\n \"message\",\n \"management\",\n \"raise\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"lisa72\",\n \"timestamp\": \"1999-10-21T21:51:01.956523\"\n }\n ]\n}",
"estimated_tokens": 246
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Create a CSV representation of lab result."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nid,test,value\r\n51f94fd4-dcd2-4c3e-8904-6ba0e2c5e14d,policy,55.370439795214644\r\n"
}
] |
{
"format": "csv",
"complexity": "simple",
"schema": "lab_result",
"type": "generation",
"prompt": "Create a CSV representation of lab result.",
"output": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nid,test,value\r\n51f94fd4-dcd2-4c3e-8904-6ba0e2c5e14d,policy,55.370439795214644\r\n",
"estimated_tokens": 158
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Reformat the XML below into CSV:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<financial_report>\n<company>Evans-Fisher</company>\n<revenue>721671</revenue>\n<profit>80335</profit>\n<meta_info>\n<generated_at>2026-01-22T04:03:05.594892</generated_at>\n<version>v1.0</version>\n<tags>remember</tags>\n<tags>follow</tags>\n<tags>brother</tags>\n<tags>take</tags>\n<tags>quickly</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>anthonymoore</by>\n<timestamp>2025-11-01T14:47:27.012760</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>ohammond</by>\n<timestamp>2001-07-31T10:12:43.525046</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>mclaughlincassandra</by>\n<timestamp>2009-07-24T22:12:06.119047</timestamp>\n</audit_log>\n</financial_report>"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 5 root fields (company, revenue, profit...) and a nesting depth of 3.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ncompany,revenue,profit,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nEvans-Fisher,721671,80335,2026-01-22T04:03:05.594892,v1.0,\"[\"\"remember\"\", \"\"follow\"\", \"\"brother\"\", \"\"take\"\", \"\"quickly\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"anthonymoore\"\", \"\"timestamp\"\": \"\"2025-11-01T14:47:27.012760\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"ohammond\"\", \"\"timestamp\"\": \"\"2001-07-31T10:12:43.525046\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"mclaughlincassandra\"\", \"\"timestamp\"\": \"\"2009-07-24T22:12:06.119047\"\"}]\"\r\n"
}
] |
{
"format": "csv",
"complexity": "complex",
"schema": "financial_report",
"type": "conversion",
"prompt": "Reformat the XML below into CSV:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<financial_report>\n<company>Evans-Fisher</company>\n<revenue>721671</revenue>\n<profit>80335</profit>\n<meta_info>\n<generated_at>2026-01-22T04:03:05.594892</generated_at>\n<version>v1.0</version>\n<tags>remember</tags>\n<tags>follow</tags>\n<tags>brother</tags>\n<tags>take</tags>\n<tags>quickly</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>anthonymoore</by>\n<timestamp>2025-11-01T14:47:27.012760</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>ohammond</by>\n<timestamp>2001-07-31T10:12:43.525046</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>mclaughlincassandra</by>\n<timestamp>2009-07-24T22:12:06.119047</timestamp>\n</audit_log>\n</financial_report>",
"output": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 5 root fields (company, revenue, profit...) and a nesting depth of 3.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ncompany,revenue,profit,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nEvans-Fisher,721671,80335,2026-01-22T04:03:05.594892,v1.0,\"[\"\"remember\"\", \"\"follow\"\", \"\"brother\"\", \"\"take\"\", \"\"quickly\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"anthonymoore\"\", \"\"timestamp\"\": \"\"2025-11-01T14:47:27.012760\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"ohammond\"\", \"\"timestamp\"\": \"\"2001-07-31T10:12:43.525046\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"mclaughlincassandra\"\", \"\"timestamp\"\": \"\"2009-07-24T22:12:06.119047\"\"}]\"\r\n",
"estimated_tokens": 278
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Reformat the JSON below into CSV:\n{\n \"endpoint\": \"/api/v1/users\",\n \"method\": \"GET\",\n \"responses\": {\n \"200\": \"OK\"\n }\n}"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 3 root fields (endpoint, method, responses) and a nesting depth of 2.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nendpoint,method,responses.200\r\n/api/v1/users,GET,OK\r\n"
}
] |
{
"format": "csv",
"complexity": "medium",
"schema": "api_specification",
"type": "conversion",
"prompt": "Reformat the JSON below into CSV:\n{\n \"endpoint\": \"/api/v1/users\",\n \"method\": \"GET\",\n \"responses\": {\n \"200\": \"OK\"\n }\n}",
"output": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 3 root fields (endpoint, method, responses) and a nesting depth of 2.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nendpoint,method,responses.200\r\n/api/v1/users,GET,OK\r\n",
"estimated_tokens": 162
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Reformat the JSON below into CSV:\n{\n \"id\": \"96ae059c-406b-40c3-8fa5-aa9ecfd54725\",\n \"test\": \"service\",\n \"value\": 42.11838818121964,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:05.662055\",\n \"version\": \"v3.0\",\n \"tags\": [\n \"ball\",\n \"thousand\",\n \"everybody\",\n \"whom\",\n \"performance\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"kenneth41\",\n \"timestamp\": \"1998-11-24T00:35:56.534669\"\n }\n ]\n}"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 5 root fields (id, test, value...) and a nesting depth of 3.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nid,test,value,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\n96ae059c-406b-40c3-8fa5-aa9ecfd54725,service,42.11838818121964,2026-01-22T04:03:05.662055,v3.0,\"[\"\"ball\"\", \"\"thousand\"\", \"\"everybody\"\", \"\"whom\"\", \"\"performance\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"kenneth41\"\", \"\"timestamp\"\": \"\"1998-11-24T00:35:56.534669\"\"}]\"\r\n"
}
] |
{
"format": "csv",
"complexity": "complex",
"schema": "lab_result",
"type": "conversion",
"prompt": "Reformat the JSON below into CSV:\n{\n \"id\": \"96ae059c-406b-40c3-8fa5-aa9ecfd54725\",\n \"test\": \"service\",\n \"value\": 42.11838818121964,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:05.662055\",\n \"version\": \"v3.0\",\n \"tags\": [\n \"ball\",\n \"thousand\",\n \"everybody\",\n \"whom\",\n \"performance\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"kenneth41\",\n \"timestamp\": \"1998-11-24T00:35:56.534669\"\n }\n ]\n}",
"output": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 5 root fields (id, test, value...) and a nesting depth of 3.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nid,test,value,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\n96ae059c-406b-40c3-8fa5-aa9ecfd54725,service,42.11838818121964,2026-01-22T04:03:05.662055,v3.0,\"[\"\"ball\"\", \"\"thousand\"\", \"\"everybody\"\", \"\"whom\"\", \"\"performance\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"kenneth41\"\", \"\"timestamp\"\": \"\"1998-11-24T00:35:56.534669\"\"}]\"\r\n",
"estimated_tokens": 233
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Transform this data from TOML to JSON:\ncourse = \"Adaptive transitional circuit\"\nweeks = 11\ncredits = 3\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (course, weeks, credits) and a nesting depth of 1.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"course\": \"Adaptive transitional circuit\",\n \"weeks\": 11,\n \"credits\": 3\n}"
}
] |
{
"format": "json",
"complexity": "medium",
"schema": "course_syllabus",
"type": "conversion",
"prompt": "Transform this data from TOML to JSON:\ncourse = \"Adaptive transitional circuit\"\nweeks = 11\ncredits = 3\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (course, weeks, credits) and a nesting depth of 1.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"course\": \"Adaptive transitional circuit\",\n \"weeks\": 11,\n \"credits\": 3\n}",
"estimated_tokens": 163
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Convert the following XML to CSV:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<prescription>\n<rx>425345cd-6c7d-488c-9fac-6852b0fe978c</rx>\n<drug>opportunity</drug>\n<dosage>10mg</dosage>\n</prescription>"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 3 root fields (rx, drug, dosage) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nrx,drug,dosage\r\n425345cd-6c7d-488c-9fac-6852b0fe978c,opportunity,10mg\r\n"
}
] |
{
"format": "csv",
"complexity": "simple",
"schema": "prescription",
"type": "conversion",
"prompt": "Convert the following XML to CSV:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<prescription>\n<rx>425345cd-6c7d-488c-9fac-6852b0fe978c</rx>\n<drug>opportunity</drug>\n<dosage>10mg</dosage>\n</prescription>",
"output": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 3 root fields (rx, drug, dosage) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nrx,drug,dosage\r\n425345cd-6c7d-488c-9fac-6852b0fe978c,opportunity,10mg\r\n",
"estimated_tokens": 163
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Output structured data for contract using JSON."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The contract should contain a dictionary with 3 root fields (contract, parties, signed) and a nesting depth of 2.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"contract\": \"morph real-time networks\",\n \"parties\": [\n \"Anderson Group\",\n \"Pitts PLC\"\n ],\n \"signed\": false\n}"
}
] |
{
"format": "json",
"complexity": "medium",
"schema": "contract",
"type": "generation",
"prompt": "Output structured data for contract using JSON.",
"output": "Approach:\n1. Define the schema: The contract should contain a dictionary with 3 root fields (contract, parties, signed) and a nesting depth of 2.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"contract\": \"morph real-time networks\",\n \"parties\": [\n \"Anderson Group\",\n \"Pitts PLC\"\n ],\n \"signed\": false\n}",
"estimated_tokens": 166
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Convert the following TOML to XML:\nid = \"6b147962-18db-482c-997e-5db4699c3bf1\"\ntest = \"less\"\nvalue = 40.534189539121535\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<lab_result>\n<id>6b147962-18db-482c-997e-5db4699c3bf1</id>\n<test>less</test>\n<value>40.534189539121535</value>\n</lab_result>"
}
] |
{
"format": "xml",
"complexity": "medium",
"schema": "lab_result",
"type": "conversion",
"prompt": "Convert the following TOML to XML:\nid = \"6b147962-18db-482c-997e-5db4699c3bf1\"\ntest = \"less\"\nvalue = 40.534189539121535\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<lab_result>\n<id>6b147962-18db-482c-997e-5db4699c3bf1</id>\n<test>less</test>\n<value>40.534189539121535</value>\n</lab_result>",
"estimated_tokens": 183
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Convert the following YAML to CSV:\ntimestamp: '2026-01-22T04:03:04.971124'\nlevel: ERROR\nmessage: Suffer day soon range though book.\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 3 root fields (timestamp, level, message) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntimestamp,level,message\r\n2026-01-22T04:03:04.971124,ERROR,Suffer day soon range though book.\r\n"
}
] |
{
"format": "csv",
"complexity": "simple",
"schema": "error_log",
"type": "conversion",
"prompt": "Convert the following YAML to CSV:\ntimestamp: '2026-01-22T04:03:04.971124'\nlevel: ERROR\nmessage: Suffer day soon range though book.\n",
"output": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 3 root fields (timestamp, level, message) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntimestamp,level,message\r\n2026-01-22T04:03:04.971124,ERROR,Suffer day soon range though book.\r\n",
"estimated_tokens": 171
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Convert the following XML to TOML:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<experiment_result>\n<experiment_id>EXP-100053</experiment_id>\n<title>Analysis of hour</title>\n<metrics>\n<accuracy>0.791</accuracy>\n<loss>0.43</loss>\n<epoch>30</epoch>\n</metrics>\n<config>\n<batch_size>32</batch_size>\n<optimizer>sgd</optimizer>\n<lr>0.001</lr>\n</config>\n</experiment_result>"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id = \"EXP-100053\"\ntitle = \"Analysis of hour\"\n\n[metrics]\naccuracy = 0.791\nloss = 0.43\nepoch = 30\n\n[config]\nbatch_size = 32\noptimizer = \"sgd\"\nlr = 0.001\n"
}
] |
{
"format": "toml",
"complexity": "medium",
"schema": "experiment_result",
"type": "conversion",
"prompt": "Convert the following XML to TOML:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<experiment_result>\n<experiment_id>EXP-100053</experiment_id>\n<title>Analysis of hour</title>\n<metrics>\n<accuracy>0.791</accuracy>\n<loss>0.43</loss>\n<epoch>30</epoch>\n</metrics>\n<config>\n<batch_size>32</batch_size>\n<optimizer>sgd</optimizer>\n<lr>0.001</lr>\n</config>\n</experiment_result>",
"output": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id = \"EXP-100053\"\ntitle = \"Analysis of hour\"\n\n[metrics]\naccuracy = 0.791\nloss = 0.43\nepoch = 30\n\n[config]\nbatch_size = 32\noptimizer = \"sgd\"\nlr = 0.001\n",
"estimated_tokens": 184
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Create a XML representation of api specification."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The api_specification should contain a dictionary with 5 root fields (endpoint, method, responses...) and a nesting depth of 3.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<api_specification>\n<endpoint>/api/v1/users</endpoint>\n<method>GET</method>\n<responses>\n<_200>OK</_200>\n</responses>\n<meta_info>\n<generated_at>2026-01-22T04:03:06.690335</generated_at>\n<version>v4.0</version>\n<tags>base</tags>\n<tags>main</tags>\n<tags>never</tags>\n<tags>manage</tags>\n<tags>test</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>crobinson</by>\n<timestamp>1995-03-16T22:55:32.772544</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>gary86</by>\n<timestamp>1999-08-03T03:28:33.243961</timestamp>\n</audit_log>\n</api_specification>"
}
] |
{
"format": "xml",
"complexity": "complex",
"schema": "api_specification",
"type": "generation",
"prompt": "Create a XML representation of api specification.",
"output": "Approach:\n1. Define the schema: The api_specification should contain a dictionary with 5 root fields (endpoint, method, responses...) and a nesting depth of 3.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<api_specification>\n<endpoint>/api/v1/users</endpoint>\n<method>GET</method>\n<responses>\n<_200>OK</_200>\n</responses>\n<meta_info>\n<generated_at>2026-01-22T04:03:06.690335</generated_at>\n<version>v4.0</version>\n<tags>base</tags>\n<tags>main</tags>\n<tags>never</tags>\n<tags>manage</tags>\n<tags>test</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>crobinson</by>\n<timestamp>1995-03-16T22:55:32.772544</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>gary86</by>\n<timestamp>1999-08-03T03:28:33.243961</timestamp>\n</audit_log>\n</api_specification>",
"estimated_tokens": 293
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Output structured data for news_article using JSON."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The news_article should contain a dictionary with 3 root fields (headline, body, author) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"headline\": \"Republican respond individual town east.\",\n \"body\": \"Appear religious Republican member from store. Store note benefit spring ask key energy. House identify back.\\nMy seek suggest care movement anyone. Difference control perform good.\",\n \"author\": \"Tommy Porter\"\n}"
}
] |
{
"format": "json",
"complexity": "medium",
"schema": "news_article",
"type": "generation",
"prompt": "Output structured data for news_article using JSON.",
"output": "Approach:\n1. Define the schema: The news_article should contain a dictionary with 3 root fields (headline, body, author) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"headline\": \"Republican respond individual town east.\",\n \"body\": \"Appear religious Republican member from store. Store note benefit spring ask key energy. House identify back.\\nMy seek suggest care movement anyone. Difference control perform good.\",\n \"author\": \"Tommy Porter\"\n}",
"estimated_tokens": 207
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Reformat the YAML below into TOML:\nstudent: Joshua Gill\nscore: 35\ngrade: A\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 3 root fields (student, score, grade) and a nesting depth of 1.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nstudent = \"Joshua Gill\"\nscore = 35\ngrade = \"A\"\n"
}
] |
{
"format": "toml",
"complexity": "medium",
"schema": "student_assessment",
"type": "conversion",
"prompt": "Reformat the YAML below into TOML:\nstudent: Joshua Gill\nscore: 35\ngrade: A\n",
"output": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 3 root fields (student, score, grade) and a nesting depth of 1.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nstudent = \"Joshua Gill\"\nscore = 35\ngrade = \"A\"\n",
"estimated_tokens": 152
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Generate course syllabus data in JSON format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The course_syllabus should contain a dictionary with 5 root fields (course, weeks, credits...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"course\": \"Polarized fresh-thinking framework\",\n \"weeks\": 11,\n \"credits\": 3,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:05.143414\",\n \"version\": \"v2.0\",\n \"tags\": [\n \"job\",\n \"professional\",\n \"price\",\n \"once\",\n \"offer\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"tracey70\",\n \"timestamp\": \"2008-06-05T04:53:27.174089\"\n },\n {\n \"action\": \"created\",\n \"by\": \"jeffery54\",\n \"timestamp\": \"1984-12-14T02:13:43.404848\"\n },\n {\n \"action\": \"created\",\n \"by\": \"eross\",\n \"timestamp\": \"2014-01-16T14:33:28.535750\"\n }\n ]\n}"
}
] |
{
"format": "json",
"complexity": "complex",
"schema": "course_syllabus",
"type": "generation",
"prompt": "Generate course syllabus data in JSON format.",
"output": "Approach:\n1. Define the schema: The course_syllabus should contain a dictionary with 5 root fields (course, weeks, credits...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"course\": \"Polarized fresh-thinking framework\",\n \"weeks\": 11,\n \"credits\": 3,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:05.143414\",\n \"version\": \"v2.0\",\n \"tags\": [\n \"job\",\n \"professional\",\n \"price\",\n \"once\",\n \"offer\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"tracey70\",\n \"timestamp\": \"2008-06-05T04:53:27.174089\"\n },\n {\n \"action\": \"created\",\n \"by\": \"jeffery54\",\n \"timestamp\": \"1984-12-14T02:13:43.404848\"\n },\n {\n \"action\": \"created\",\n \"by\": \"eross\",\n \"timestamp\": \"2014-01-16T14:33:28.535750\"\n }\n ]\n}",
"estimated_tokens": 296
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Convert the following CSV to YAML:\nexperiment_id,title,metrics.accuracy,metrics.loss,metrics.epoch,config.batch_size,config.optimizer,config.lr\r\nEXP-880755,Analysis of project,0.7685,0.4944,84,64,adam,0.001\r\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id: EXP-880755\ntitle: Analysis of project\nmetrics:\n accuracy: 0.7685\n loss: 0.4944\n epoch: 84\nconfig:\n batch_size: 64\n optimizer: adam\n lr: 0.001\n"
}
] |
{
"format": "yaml",
"complexity": "medium",
"schema": "experiment_result",
"type": "conversion",
"prompt": "Convert the following CSV to YAML:\nexperiment_id,title,metrics.accuracy,metrics.loss,metrics.epoch,config.batch_size,config.optimizer,config.lr\r\nEXP-880755,Analysis of project,0.7685,0.4944,84,64,adam,0.001\r\n",
"output": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id: EXP-880755\ntitle: Analysis of project\nmetrics:\n accuracy: 0.7685\n loss: 0.4944\n epoch: 84\nconfig:\n batch_size: 64\n optimizer: adam\n lr: 0.001\n",
"estimated_tokens": 186
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Create a YAML representation of customer review."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The customer_review should contain a dictionary with 5 root fields (user, rating, comment...) and a nesting depth of 3.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nuser: lori80\nrating: 4\ncomment: Direction sign week represent thing friend art.\nmeta_info:\n generated_at: '2026-01-22T04:03:05.005015'\n version: v5.0\n tags:\n - successful\n - every\n - still\n - unit\n - score\naudit_log:\n- action: created\n by: fpowell\n timestamp: '2024-12-06T01:40:04.096854'\n- action: created\n by: kimberly73\n timestamp: '1986-01-28T21:51:25.896323'\n"
}
] |
{
"format": "yaml",
"complexity": "complex",
"schema": "customer_review",
"type": "generation",
"prompt": "Create a YAML representation of customer review.",
"output": "Approach:\n1. Define the schema: The customer_review should contain a dictionary with 5 root fields (user, rating, comment...) and a nesting depth of 3.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nuser: lori80\nrating: 4\ncomment: Direction sign week represent thing friend art.\nmeta_info:\n generated_at: '2026-01-22T04:03:05.005015'\n version: v5.0\n tags:\n - successful\n - every\n - still\n - unit\n - score\naudit_log:\n- action: created\n by: fpowell\n timestamp: '2024-12-06T01:40:04.096854'\n- action: created\n by: kimberly73\n timestamp: '1986-01-28T21:51:25.896323'\n",
"estimated_tokens": 232
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Create a YAML representation of experiment result."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The experiment_result should contain a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nexperiment_id: EXP-481272\ntitle: Analysis of production\nmetrics:\n accuracy: 0.9889\n loss: 0.3569\n epoch: 46\nconfig:\n batch_size: 128\n optimizer: adam\n lr: 0.001\n"
}
] |
{
"format": "yaml",
"complexity": "simple",
"schema": "experiment_result",
"type": "generation",
"prompt": "Create a YAML representation of experiment result.",
"output": "Approach:\n1. Define the schema: The experiment_result should contain a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nexperiment_id: EXP-481272\ntitle: Analysis of production\nmetrics:\n accuracy: 0.9889\n loss: 0.3569\n epoch: 46\nconfig:\n batch_size: 128\n optimizer: adam\n lr: 0.001\n",
"estimated_tokens": 182
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Convert the following JSON to TOML:\n{\n \"experiment_id\": \"EXP-481272\",\n \"title\": \"Analysis of production\",\n \"metrics\": {\n \"accuracy\": 0.9889,\n \"loss\": 0.3569,\n \"epoch\": 46\n },\n \"config\": {\n \"batch_size\": 128,\n \"optimizer\": \"adam\",\n \"lr\": 0.001\n }\n}"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id = \"EXP-481272\"\ntitle = \"Analysis of production\"\n\n[metrics]\naccuracy = 0.9889\nloss = 0.3569\nepoch = 46\n\n[config]\nbatch_size = 128\noptimizer = \"adam\"\nlr = 0.001\n"
}
] |
{
"format": "toml",
"complexity": "simple",
"schema": "experiment_result",
"type": "conversion",
"prompt": "Convert the following JSON to TOML:\n{\n \"experiment_id\": \"EXP-481272\",\n \"title\": \"Analysis of production\",\n \"metrics\": {\n \"accuracy\": 0.9889,\n \"loss\": 0.3569,\n \"epoch\": 46\n },\n \"config\": {\n \"batch_size\": 128,\n \"optimizer\": \"adam\",\n \"lr\": 0.001\n }\n}",
"output": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id = \"EXP-481272\"\ntitle = \"Analysis of production\"\n\n[metrics]\naccuracy = 0.9889\nloss = 0.3569\nepoch = 46\n\n[config]\nbatch_size = 128\noptimizer = \"adam\"\nlr = 0.001\n",
"estimated_tokens": 187
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Reformat the TOML below into YAML:\nheadline = \"Involve these meet generation fight.\"\nbody = \"Community human either market all body direction. Different miss space maybe. Bar daughter institution as. Woman serve discuss industry.\\nAnimal item or across guess forward.\"\nauthor = \"Nicole Walker\"\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (headline, body, author) and a nesting depth of 1.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nheadline: Involve these meet generation fight.\nbody: 'Community human either market all body direction. Different miss space maybe.\n Bar daughter institution as. Woman serve discuss industry.\n\n Animal item or across guess forward.'\nauthor: Nicole Walker\n"
}
] |
{
"format": "yaml",
"complexity": "simple",
"schema": "news_article",
"type": "conversion",
"prompt": "Reformat the TOML below into YAML:\nheadline = \"Involve these meet generation fight.\"\nbody = \"Community human either market all body direction. Different miss space maybe. Bar daughter institution as. Woman serve discuss industry.\\nAnimal item or across guess forward.\"\nauthor = \"Nicole Walker\"\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (headline, body, author) and a nesting depth of 1.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nheadline: Involve these meet generation fight.\nbody: 'Community human either market all body direction. Different miss space maybe.\n Bar daughter institution as. Woman serve discuss industry.\n\n Animal item or across guess forward.'\nauthor: Nicole Walker\n",
"estimated_tokens": 207
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Create a YAML representation of transaction record."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The transaction_record should contain a dictionary with 3 root fields (txn, amount, status) and a nesting depth of 1.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ntxn: a7c2ec3a-958e-4d8a-a31a-0a31d026b509\namount: 212.62\nstatus: completed\n"
}
] |
{
"format": "yaml",
"complexity": "medium",
"schema": "transaction_record",
"type": "generation",
"prompt": "Create a YAML representation of transaction record.",
"output": "Approach:\n1. Define the schema: The transaction_record should contain a dictionary with 3 root fields (txn, amount, status) and a nesting depth of 1.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ntxn: a7c2ec3a-958e-4d8a-a31a-0a31d026b509\namount: 212.62\nstatus: completed\n",
"estimated_tokens": 156
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Reformat the TOML below into CSV:\ntxn = \"a7c2ec3a-958e-4d8a-a31a-0a31d026b509\"\namount = 212.62\nstatus = \"completed\"\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (txn, amount, status) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntxn,amount,status\r\na7c2ec3a-958e-4d8a-a31a-0a31d026b509,212.62,completed\r\n"
}
] |
{
"format": "csv",
"complexity": "medium",
"schema": "transaction_record",
"type": "conversion",
"prompt": "Reformat the TOML below into CSV:\ntxn = \"a7c2ec3a-958e-4d8a-a31a-0a31d026b509\"\namount = 212.62\nstatus = \"completed\"\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (txn, amount, status) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntxn,amount,status\r\na7c2ec3a-958e-4d8a-a31a-0a31d026b509,212.62,completed\r\n",
"estimated_tokens": 165
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Create a TOML representation of research paper."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The research_paper should contain a dictionary with 6 root fields (paper_id, title, authors...) and a nesting depth of 3.\n2. Select the format: TOML is chosen. Define hierarchy using dot-notation or [section] headers.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\npaper_id = \"arXiv:2021.79243\"\ntitle = \"Configurable bandwidth-monitored protocol\"\nabstract = \"This billion number Congress. Account suddenly detail short.\\nNational executive cause soldier Mr left home. Each rather management international full. Save very soldier beat kid tonight.\"\nkeywords = [ \"street\", \"mission\", \"involve\", \"with\", \"must\",]\n[[authors]]\nname = \"Paula Nelson\"\naffiliation = \"Underwood-Kemp\"\nemail = \"kristacaldwell@example.com\"\n\n[[authors]]\nname = \"James Palmer\"\naffiliation = \"Mccoy Group\"\nemail = \"smeyer@example.com\"\n\n[[authors]]\nname = \"Dr. Douglas Clarke\"\naffiliation = \"Gonzalez-Martinez\"\nemail = \"christine78@example.com\"\n\n[[authors]]\nname = \"Linda Olson\"\naffiliation = \"James LLC\"\nemail = \"andrea59@example.org\"\n\n[publication]\nvenue = \"NeurIPS\"\nyear = 2020\ndoi = \"10.5005/HLeh\"\n"
}
] |
{
"format": "toml",
"complexity": "medium",
"schema": "research_paper",
"type": "generation",
"prompt": "Create a TOML representation of research paper.",
"output": "Approach:\n1. Define the schema: The research_paper should contain a dictionary with 6 root fields (paper_id, title, authors...) and a nesting depth of 3.\n2. Select the format: TOML is chosen. Define hierarchy using dot-notation or [section] headers.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\npaper_id = \"arXiv:2021.79243\"\ntitle = \"Configurable bandwidth-monitored protocol\"\nabstract = \"This billion number Congress. Account suddenly detail short.\\nNational executive cause soldier Mr left home. Each rather management international full. Save very soldier beat kid tonight.\"\nkeywords = [ \"street\", \"mission\", \"involve\", \"with\", \"must\",]\n[[authors]]\nname = \"Paula Nelson\"\naffiliation = \"Underwood-Kemp\"\nemail = \"kristacaldwell@example.com\"\n\n[[authors]]\nname = \"James Palmer\"\naffiliation = \"Mccoy Group\"\nemail = \"smeyer@example.com\"\n\n[[authors]]\nname = \"Dr. Douglas Clarke\"\naffiliation = \"Gonzalez-Martinez\"\nemail = \"christine78@example.com\"\n\n[[authors]]\nname = \"Linda Olson\"\naffiliation = \"James LLC\"\nemail = \"andrea59@example.org\"\n\n[publication]\nvenue = \"NeurIPS\"\nyear = 2020\ndoi = \"10.5005/HLeh\"\n",
"estimated_tokens": 337
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Create a CSV representation of student assessment."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The student_assessment should contain a dictionary with 5 root fields (student, score, grade...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nstudent,score,grade,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nZachary Brewer,97,C,2026-01-22T04:03:05.249070,v5.0,\"[\"\"me\"\", \"\"deep\"\", \"\"technology\"\", \"\"million\"\", \"\"behind\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"matthewsantos\"\", \"\"timestamp\"\": \"\"1973-04-05T10:03:19.246516\"\"}]\"\r\n"
}
] |
{
"format": "csv",
"complexity": "complex",
"schema": "student_assessment",
"type": "generation",
"prompt": "Create a CSV representation of student assessment.",
"output": "Approach:\n1. Define the schema: The student_assessment should contain a dictionary with 5 root fields (student, score, grade...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nstudent,score,grade,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nZachary Brewer,97,C,2026-01-22T04:03:05.249070,v5.0,\"[\"\"me\"\", \"\"deep\"\", \"\"technology\"\", \"\"million\"\", \"\"behind\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"matthewsantos\"\", \"\"timestamp\"\": \"\"1973-04-05T10:03:19.246516\"\"}]\"\r\n",
"estimated_tokens": 219
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Transform this data from YAML to TOML:\ncontract: morph real-time networks\nparties:\n- Anderson Group\n- Pitts PLC\nsigned: false\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 3 root fields (contract, parties, signed) and a nesting depth of 2.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ncontract = \"morph real-time networks\"\nparties = [ \"Anderson Group\", \"Pitts PLC\",]\nsigned = false\n"
}
] |
{
"format": "toml",
"complexity": "medium",
"schema": "contract",
"type": "conversion",
"prompt": "Transform this data from YAML to TOML:\ncontract: morph real-time networks\nparties:\n- Anderson Group\n- Pitts PLC\nsigned: false\n",
"output": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 3 root fields (contract, parties, signed) and a nesting depth of 2.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ncontract = \"morph real-time networks\"\nparties = [ \"Anderson Group\", \"Pitts PLC\",]\nsigned = false\n",
"estimated_tokens": 166
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Generate customer review data in CSV format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The customer_review should contain a dictionary with 3 root fields (user, rating, comment) and a nesting depth of 1.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nuser,rating,comment\r\ngarciashelly,3,Commercial wall factor Congress.\r\n"
}
] |
{
"format": "csv",
"complexity": "medium",
"schema": "customer_review",
"type": "generation",
"prompt": "Generate customer review data in CSV format.",
"output": "Approach:\n1. Define the schema: The customer_review should contain a dictionary with 3 root fields (user, rating, comment) and a nesting depth of 1.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nuser,rating,comment\r\ngarciashelly,3,Commercial wall factor Congress.\r\n",
"estimated_tokens": 158
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Generate customer review data in XML format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The customer_review should contain a dictionary with 5 root fields (user, rating, comment...) and a nesting depth of 3.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<customer_review>\n<user>pamela79</user>\n<rating>5</rating>\n<comment>Congress interest employee such real sea.</comment>\n<meta_info>\n<generated_at>2026-01-22T04:03:05.702005</generated_at>\n<version>v3.0</version>\n<tags>card</tags>\n<tags>model</tags>\n<tags>skill</tags>\n<tags>southern</tags>\n<tags>summer</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>leejohn</by>\n<timestamp>2009-12-30T14:04:14.639958</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>swilkerson</by>\n<timestamp>2002-06-01T00:16:21.688092</timestamp>\n</audit_log>\n</customer_review>"
}
] |
{
"format": "xml",
"complexity": "complex",
"schema": "customer_review",
"type": "generation",
"prompt": "Generate customer review data in XML format.",
"output": "Approach:\n1. Define the schema: The customer_review should contain a dictionary with 5 root fields (user, rating, comment...) and a nesting depth of 3.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<customer_review>\n<user>pamela79</user>\n<rating>5</rating>\n<comment>Congress interest employee such real sea.</comment>\n<meta_info>\n<generated_at>2026-01-22T04:03:05.702005</generated_at>\n<version>v3.0</version>\n<tags>card</tags>\n<tags>model</tags>\n<tags>skill</tags>\n<tags>southern</tags>\n<tags>summer</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>leejohn</by>\n<timestamp>2009-12-30T14:04:14.639958</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>swilkerson</by>\n<timestamp>2002-06-01T00:16:21.688092</timestamp>\n</audit_log>\n</customer_review>",
"estimated_tokens": 293
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Reformat the YAML below into TOML:\nexperiment_id: EXP-616884\ntitle: Analysis of and\nmetrics:\n accuracy: 0.7673\n loss: 0.4367\n epoch: 22\nconfig:\n batch_size: 32\n optimizer: sgd\n lr: 0.001\nmeta_info:\n generated_at: '2026-01-22T04:03:05.730891'\n version: v4.0\n tags:\n - suddenly\n - western\n - well\n - ahead\n - director\naudit_log:\n- action: created\n by: thomasmarvin\n timestamp: '2007-02-20T21:53:12.454337'\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 6 root fields (experiment_id, title, metrics...) and a nesting depth of 3.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id = \"EXP-616884\"\ntitle = \"Analysis of and\"\n[[audit_log]]\naction = \"created\"\nby = \"thomasmarvin\"\ntimestamp = \"2007-02-20T21:53:12.454337\"\n\n[metrics]\naccuracy = 0.7673\nloss = 0.4367\nepoch = 22\n\n[config]\nbatch_size = 32\noptimizer = \"sgd\"\nlr = 0.001\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:05.730891\"\nversion = \"v4.0\"\ntags = [ \"suddenly\", \"western\", \"well\", \"ahead\", \"director\",]\n"
}
] |
{
"format": "toml",
"complexity": "complex",
"schema": "experiment_result",
"type": "conversion",
"prompt": "Reformat the YAML below into TOML:\nexperiment_id: EXP-616884\ntitle: Analysis of and\nmetrics:\n accuracy: 0.7673\n loss: 0.4367\n epoch: 22\nconfig:\n batch_size: 32\n optimizer: sgd\n lr: 0.001\nmeta_info:\n generated_at: '2026-01-22T04:03:05.730891'\n version: v4.0\n tags:\n - suddenly\n - western\n - well\n - ahead\n - director\naudit_log:\n- action: created\n by: thomasmarvin\n timestamp: '2007-02-20T21:53:12.454337'\n",
"output": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 6 root fields (experiment_id, title, metrics...) and a nesting depth of 3.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nexperiment_id = \"EXP-616884\"\ntitle = \"Analysis of and\"\n[[audit_log]]\naction = \"created\"\nby = \"thomasmarvin\"\ntimestamp = \"2007-02-20T21:53:12.454337\"\n\n[metrics]\naccuracy = 0.7673\nloss = 0.4367\nepoch = 22\n\n[config]\nbatch_size = 32\noptimizer = \"sgd\"\nlr = 0.001\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:05.730891\"\nversion = \"v4.0\"\ntags = [ \"suddenly\", \"western\", \"well\", \"ahead\", \"director\",]\n",
"estimated_tokens": 242
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Generate research paper data in CSV format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The research_paper should contain a dictionary with 6 root fields (paper_id, title, authors...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\npaper_id,title,authors,abstract,keywords,publication.venue,publication.year,publication.doi\r\narXiv:2020.70770,Stand-alone needs-based standardization,\"[{\"\"name\"\": \"\"Michael Deleon\"\", \"\"affiliation\"\": \"\"Thomas Inc\"\", \"\"email\"\": \"\"richard73@example.com\"\"}, {\"\"name\"\": \"\"Laurie Torres\"\", \"\"affiliation\"\": \"\"Gross Ltd\"\", \"\"email\"\": \"\"zunigawendy@example.org\"\"}, {\"\"name\"\": \"\"Jasmin Mcdonald\"\", \"\"affiliation\"\": \"\"Obrien-Day\"\", \"\"email\"\": \"\"stacie24@example.com\"\"}]\",\"Policy someone thank company book. Once same nation charge every.\nStudent game by event.\",\"[\"\"say\"\", \"\"adult\"\", \"\"other\"\", \"\"yet\"\"]\",Nature,2024,10.7430/lhxI\r\n"
}
] |
{
"format": "csv",
"complexity": "medium",
"schema": "research_paper",
"type": "generation",
"prompt": "Generate research paper data in CSV format.",
"output": "Approach:\n1. Define the schema: The research_paper should contain a dictionary with 6 root fields (paper_id, title, authors...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\npaper_id,title,authors,abstract,keywords,publication.venue,publication.year,publication.doi\r\narXiv:2020.70770,Stand-alone needs-based standardization,\"[{\"\"name\"\": \"\"Michael Deleon\"\", \"\"affiliation\"\": \"\"Thomas Inc\"\", \"\"email\"\": \"\"richard73@example.com\"\"}, {\"\"name\"\": \"\"Laurie Torres\"\", \"\"affiliation\"\": \"\"Gross Ltd\"\", \"\"email\"\": \"\"zunigawendy@example.org\"\"}, {\"\"name\"\": \"\"Jasmin Mcdonald\"\", \"\"affiliation\"\": \"\"Obrien-Day\"\", \"\"email\"\": \"\"stacie24@example.com\"\"}]\",\"Policy someone thank company book. Once same nation charge every.\nStudent game by event.\",\"[\"\"say\"\", \"\"adult\"\", \"\"other\"\", \"\"yet\"\"]\",Nature,2024,10.7430/lhxI\r\n",
"estimated_tokens": 298
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Create a TOML representation of product listing."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 3 root fields (product, price, stock) and a nesting depth of 1.\n2. Select the format: TOML is chosen. Define hierarchy using dot-notation or [section] headers.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nproduct = \"over\"\nprice = 34.06\nstock = 26\n"
}
] |
{
"format": "toml",
"complexity": "medium",
"schema": "product_listing",
"type": "generation",
"prompt": "Create a TOML representation of product listing.",
"output": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 3 root fields (product, price, stock) and a nesting depth of 1.\n2. Select the format: TOML is chosen. Define hierarchy using dot-notation or [section] headers.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nproduct = \"over\"\nprice = 34.06\nstock = 26\n",
"estimated_tokens": 145
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Transform this data from TOML to JSON:\nheadline = \"Yet poor evidence environment general.\"\nbody = \"Poor worker recently factor form sure argue. Long start according sense story. Share Congress sometimes tough green when image.\"\nauthor = \"James Morales\"\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (headline, body, author) and a nesting depth of 1.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"headline\": \"Yet poor evidence environment general.\",\n \"body\": \"Poor worker recently factor form sure argue. Long start according sense story. Share Congress sometimes tough green when image.\",\n \"author\": \"James Morales\"\n}"
}
] |
{
"format": "json",
"complexity": "simple",
"schema": "news_article",
"type": "conversion",
"prompt": "Transform this data from TOML to JSON:\nheadline = \"Yet poor evidence environment general.\"\nbody = \"Poor worker recently factor form sure argue. Long start according sense story. Share Congress sometimes tough green when image.\"\nauthor = \"James Morales\"\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (headline, body, author) and a nesting depth of 1.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"headline\": \"Yet poor evidence environment general.\",\n \"body\": \"Poor worker recently factor form sure argue. Long start according sense story. Share Congress sometimes tough green when image.\",\n \"author\": \"James Morales\"\n}",
"estimated_tokens": 200
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Reformat the TOML below into XML:\nid = \"0d0bfcdd-43a6-4734-a3a3-2e84ac619a8e\"\ntest = \"health\"\nvalue = 22.736645656882736\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<lab_result>\n<id>0d0bfcdd-43a6-4734-a3a3-2e84ac619a8e</id>\n<test>health</test>\n<value>22.736645656882736</value>\n</lab_result>"
}
] |
{
"format": "xml",
"complexity": "medium",
"schema": "lab_result",
"type": "conversion",
"prompt": "Reformat the TOML below into XML:\nid = \"0d0bfcdd-43a6-4734-a3a3-2e84ac619a8e\"\ntest = \"health\"\nvalue = 22.736645656882736\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<lab_result>\n<id>0d0bfcdd-43a6-4734-a3a3-2e84ac619a8e</id>\n<test>health</test>\n<value>22.736645656882736</value>\n</lab_result>",
"estimated_tokens": 184
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Convert the following TOML to CSV:\ntxn = \"ba8535a3-b2c3-4f3f-91cb-80968767d87b\"\namount = 415.52\nstatus = \"completed\"\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (txn, amount, status) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntxn,amount,status\r\nba8535a3-b2c3-4f3f-91cb-80968767d87b,415.52,completed\r\n"
}
] |
{
"format": "csv",
"complexity": "medium",
"schema": "transaction_record",
"type": "conversion",
"prompt": "Convert the following TOML to CSV:\ntxn = \"ba8535a3-b2c3-4f3f-91cb-80968767d87b\"\namount = 415.52\nstatus = \"completed\"\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (txn, amount, status) and a nesting depth of 1.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntxn,amount,status\r\nba8535a3-b2c3-4f3f-91cb-80968767d87b,415.52,completed\r\n",
"estimated_tokens": 165
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Convert the following TOML to YAML:\nuser = \"alisonhancock\"\npost = \"Determine attention certainly relate case drug. Everything science fall war.\"\nlikes = 598\n[[audit_log]]\naction = \"created\"\nby = \"dgrant\"\ntimestamp = \"1972-01-31T14:11:50.299557\"\n\n[[audit_log]]\naction = \"created\"\nby = \"ymedina\"\ntimestamp = \"1984-06-17T07:31:52.838713\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:05.945233\"\nversion = \"v5.0\"\ntags = [ \"outside\", \"benefit\", \"drug\", \"professor\", \"responsibility\",]\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 5 root fields (user, post, likes...) and a nesting depth of 3.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nuser: alisonhancock\npost: Determine attention certainly relate case drug. Everything science fall war.\nlikes: 598\nmeta_info:\n generated_at: '2026-01-22T04:03:05.945233'\n version: v5.0\n tags:\n - outside\n - benefit\n - drug\n - professor\n - responsibility\naudit_log:\n- action: created\n by: dgrant\n timestamp: '1972-01-31T14:11:50.299557'\n- action: created\n by: ymedina\n timestamp: '1984-06-17T07:31:52.838713'\n"
}
] |
{
"format": "yaml",
"complexity": "complex",
"schema": "social_media_post",
"type": "conversion",
"prompt": "Convert the following TOML to YAML:\nuser = \"alisonhancock\"\npost = \"Determine attention certainly relate case drug. Everything science fall war.\"\nlikes = 598\n[[audit_log]]\naction = \"created\"\nby = \"dgrant\"\ntimestamp = \"1972-01-31T14:11:50.299557\"\n\n[[audit_log]]\naction = \"created\"\nby = \"ymedina\"\ntimestamp = \"1984-06-17T07:31:52.838713\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:05.945233\"\nversion = \"v5.0\"\ntags = [ \"outside\", \"benefit\", \"drug\", \"professor\", \"responsibility\",]\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 5 root fields (user, post, likes...) and a nesting depth of 3.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nuser: alisonhancock\npost: Determine attention certainly relate case drug. Everything science fall war.\nlikes: 598\nmeta_info:\n generated_at: '2026-01-22T04:03:05.945233'\n version: v5.0\n tags:\n - outside\n - benefit\n - drug\n - professor\n - responsibility\naudit_log:\n- action: created\n by: dgrant\n timestamp: '1972-01-31T14:11:50.299557'\n- action: created\n by: ymedina\n timestamp: '1984-06-17T07:31:52.838713'\n",
"estimated_tokens": 247
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Create a JSON representation of lab result."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"id\": \"6b147962-18db-482c-997e-5db4699c3bf1\",\n \"test\": \"less\",\n \"value\": 40.534189539121535\n}"
}
] |
{
"format": "json",
"complexity": "medium",
"schema": "lab_result",
"type": "generation",
"prompt": "Create a JSON representation of lab result.",
"output": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"id\": \"6b147962-18db-482c-997e-5db4699c3bf1\",\n \"test\": \"less\",\n \"value\": 40.534189539121535\n}",
"estimated_tokens": 159
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Output structured data for lab_result using CSV."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nid,test,value\r\n6b147962-18db-482c-997e-5db4699c3bf1,less,40.534189539121535\r\n"
}
] |
{
"format": "csv",
"complexity": "medium",
"schema": "lab_result",
"type": "generation",
"prompt": "Output structured data for lab_result using CSV.",
"output": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nid,test,value\r\n6b147962-18db-482c-997e-5db4699c3bf1,less,40.534189539121535\r\n",
"estimated_tokens": 157
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Convert the following XML to YAML:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<sales_analytics>\n<period>Q1</period>\n<sales>1293</sales>\n<region>East Luke</region>\n<meta_info>\n<generated_at>2026-01-22T04:03:06.493282</generated_at>\n<version>v3.0</version>\n<tags>always</tags>\n<tags>someone</tags>\n<tags>guy</tags>\n<tags>financial</tags>\n<tags>thought</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>warrenlauren</by>\n<timestamp>1982-02-19T17:59:18.620684</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>robertblackburn</by>\n<timestamp>1976-03-29T15:21:16.808589</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>csuarez</by>\n<timestamp>2013-08-27T14:23:21.257692</timestamp>\n</audit_log>\n</sales_analytics>"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 5 root fields (period, sales, region...) and a nesting depth of 3.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nperiod: Q1\nsales: 1293\nregion: East Luke\nmeta_info:\n generated_at: '2026-01-22T04:03:06.493282'\n version: v3.0\n tags:\n - always\n - someone\n - guy\n - financial\n - thought\naudit_log:\n- action: created\n by: warrenlauren\n timestamp: '1982-02-19T17:59:18.620684'\n- action: created\n by: robertblackburn\n timestamp: '1976-03-29T15:21:16.808589'\n- action: created\n by: csuarez\n timestamp: '2013-08-27T14:23:21.257692'\n"
}
] |
{
"format": "yaml",
"complexity": "complex",
"schema": "sales_analytics",
"type": "conversion",
"prompt": "Convert the following XML to YAML:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<sales_analytics>\n<period>Q1</period>\n<sales>1293</sales>\n<region>East Luke</region>\n<meta_info>\n<generated_at>2026-01-22T04:03:06.493282</generated_at>\n<version>v3.0</version>\n<tags>always</tags>\n<tags>someone</tags>\n<tags>guy</tags>\n<tags>financial</tags>\n<tags>thought</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>warrenlauren</by>\n<timestamp>1982-02-19T17:59:18.620684</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>robertblackburn</by>\n<timestamp>1976-03-29T15:21:16.808589</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>csuarez</by>\n<timestamp>2013-08-27T14:23:21.257692</timestamp>\n</audit_log>\n</sales_analytics>",
"output": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 5 root fields (period, sales, region...) and a nesting depth of 3.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nperiod: Q1\nsales: 1293\nregion: East Luke\nmeta_info:\n generated_at: '2026-01-22T04:03:06.493282'\n version: v3.0\n tags:\n - always\n - someone\n - guy\n - financial\n - thought\naudit_log:\n- action: created\n by: warrenlauren\n timestamp: '1982-02-19T17:59:18.620684'\n- action: created\n by: robertblackburn\n timestamp: '1976-03-29T15:21:16.808589'\n- action: created\n by: csuarez\n timestamp: '2013-08-27T14:23:21.257692'\n",
"estimated_tokens": 250
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Output structured data for error_log using JSON."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The error_log should contain a dictionary with 3 root fields (timestamp, level, message) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"timestamp\": \"2026-01-22T04:03:06.273770\",\n \"level\": \"ERROR\",\n \"message\": \"Degree we simply far technology.\"\n}"
}
] |
{
"format": "json",
"complexity": "simple",
"schema": "error_log",
"type": "generation",
"prompt": "Output structured data for error_log using JSON.",
"output": "Approach:\n1. Define the schema: The error_log should contain a dictionary with 3 root fields (timestamp, level, message) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"timestamp\": \"2026-01-22T04:03:06.273770\",\n \"level\": \"ERROR\",\n \"message\": \"Degree we simply far technology.\"\n}",
"estimated_tokens": 165
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Reformat the JSON below into TOML:\n{\n \"paper_id\": \"arXiv:2020.55123\",\n \"title\": \"Digitized holistic data-warehouse\",\n \"authors\": [\n {\n \"name\": \"Charles Bartlett\",\n \"affiliation\": \"Patel Ltd\",\n \"email\": \"ngay@example.com\"\n },\n {\n \"name\": \"Colleen Cross\",\n \"affiliation\": \"Collins Group\",\n \"email\": \"diazkenneth@example.org\"\n },\n {\n \"name\": \"Patricia Campbell\",\n \"affiliation\": \"Smith and Sons\",\n \"email\": \"grogers@example.org\"\n }\n ],\n \"abstract\": \"Customer claim still follow together age.\\nEffort another tell ever sort child thing. Every yet call former image best throughout. Share call international name national.\",\n \"keywords\": [\n \"different\",\n \"prove\",\n \"her\",\n \"day\",\n \"likely\",\n \"two\"\n ],\n \"publication\": {\n \"venue\": \"ICLR\",\n \"year\": 2020,\n \"doi\": \"10.4579/vDOg\"\n },\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:06.158403\",\n \"version\": \"v3.0\",\n \"tags\": [\n \"name\",\n \"finish\",\n \"skin\",\n \"recent\",\n \"store\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"lewistodd\",\n \"timestamp\": \"1985-10-15T20:04:05.021377\"\n }\n ]\n}"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 8 root fields (paper_id, title, authors...) and a nesting depth of 3.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\npaper_id = \"arXiv:2020.55123\"\ntitle = \"Digitized holistic data-warehouse\"\nabstract = \"Customer claim still follow together age.\\nEffort another tell ever sort child thing. Every yet call former image best throughout. Share call international name national.\"\nkeywords = [ \"different\", \"prove\", \"her\", \"day\", \"likely\", \"two\",]\n[[authors]]\nname = \"Charles Bartlett\"\naffiliation = \"Patel Ltd\"\nemail = \"ngay@example.com\"\n\n[[authors]]\nname = \"Colleen Cross\"\naffiliation = \"Collins Group\"\nemail = \"diazkenneth@example.org\"\n\n[[authors]]\nname = \"Patricia Campbell\"\naffiliation = \"Smith and Sons\"\nemail = \"grogers@example.org\"\n\n[[audit_log]]\naction = \"created\"\nby = \"lewistodd\"\ntimestamp = \"1985-10-15T20:04:05.021377\"\n\n[publication]\nvenue = \"ICLR\"\nyear = 2020\ndoi = \"10.4579/vDOg\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:06.158403\"\nversion = \"v3.0\"\ntags = [ \"name\", \"finish\", \"skin\", \"recent\", \"store\",]\n"
}
] |
{
"format": "toml",
"complexity": "complex",
"schema": "research_paper",
"type": "conversion",
"prompt": "Reformat the JSON below into TOML:\n{\n \"paper_id\": \"arXiv:2020.55123\",\n \"title\": \"Digitized holistic data-warehouse\",\n \"authors\": [\n {\n \"name\": \"Charles Bartlett\",\n \"affiliation\": \"Patel Ltd\",\n \"email\": \"ngay@example.com\"\n },\n {\n \"name\": \"Colleen Cross\",\n \"affiliation\": \"Collins Group\",\n \"email\": \"diazkenneth@example.org\"\n },\n {\n \"name\": \"Patricia Campbell\",\n \"affiliation\": \"Smith and Sons\",\n \"email\": \"grogers@example.org\"\n }\n ],\n \"abstract\": \"Customer claim still follow together age.\\nEffort another tell ever sort child thing. Every yet call former image best throughout. Share call international name national.\",\n \"keywords\": [\n \"different\",\n \"prove\",\n \"her\",\n \"day\",\n \"likely\",\n \"two\"\n ],\n \"publication\": {\n \"venue\": \"ICLR\",\n \"year\": 2020,\n \"doi\": \"10.4579/vDOg\"\n },\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:06.158403\",\n \"version\": \"v3.0\",\n \"tags\": [\n \"name\",\n \"finish\",\n \"skin\",\n \"recent\",\n \"store\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"lewistodd\",\n \"timestamp\": \"1985-10-15T20:04:05.021377\"\n }\n ]\n}",
"output": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 8 root fields (paper_id, title, authors...) and a nesting depth of 3.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\npaper_id = \"arXiv:2020.55123\"\ntitle = \"Digitized holistic data-warehouse\"\nabstract = \"Customer claim still follow together age.\\nEffort another tell ever sort child thing. Every yet call former image best throughout. Share call international name national.\"\nkeywords = [ \"different\", \"prove\", \"her\", \"day\", \"likely\", \"two\",]\n[[authors]]\nname = \"Charles Bartlett\"\naffiliation = \"Patel Ltd\"\nemail = \"ngay@example.com\"\n\n[[authors]]\nname = \"Colleen Cross\"\naffiliation = \"Collins Group\"\nemail = \"diazkenneth@example.org\"\n\n[[authors]]\nname = \"Patricia Campbell\"\naffiliation = \"Smith and Sons\"\nemail = \"grogers@example.org\"\n\n[[audit_log]]\naction = \"created\"\nby = \"lewistodd\"\ntimestamp = \"1985-10-15T20:04:05.021377\"\n\n[publication]\nvenue = \"ICLR\"\nyear = 2020\ndoi = \"10.4579/vDOg\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:06.158403\"\nversion = \"v3.0\"\ntags = [ \"name\", \"finish\", \"skin\", \"recent\", \"store\",]\n",
"estimated_tokens": 367
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Transform this data from JSON to CSV:\n{\n \"contract\": \"syndicate open-source e-commerce\",\n \"parties\": [\n \"Lawrence PLC\",\n \"Glover-Miller\"\n ],\n \"signed\": true,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:05.172344\",\n \"version\": \"v3.0\",\n \"tags\": [\n \"one\",\n \"year\",\n \"family\",\n \"boy\",\n \"ability\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"michelefloyd\",\n \"timestamp\": \"2012-10-17T22:03:38.225306\"\n },\n {\n \"action\": \"created\",\n \"by\": \"haleyrebecca\",\n \"timestamp\": \"1975-12-22T08:49:25.622175\"\n },\n {\n \"action\": \"created\",\n \"by\": \"juliamckee\",\n \"timestamp\": \"2018-10-12T06:08:50.601897\"\n }\n ]\n}"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 5 root fields (contract, parties, signed...) and a nesting depth of 3.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ncontract,parties,signed,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nsyndicate open-source e-commerce,\"[\"\"Lawrence PLC\"\", \"\"Glover-Miller\"\"]\",True,2026-01-22T04:03:05.172344,v3.0,\"[\"\"one\"\", \"\"year\"\", \"\"family\"\", \"\"boy\"\", \"\"ability\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"michelefloyd\"\", \"\"timestamp\"\": \"\"2012-10-17T22:03:38.225306\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"haleyrebecca\"\", \"\"timestamp\"\": \"\"1975-12-22T08:49:25.622175\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"juliamckee\"\", \"\"timestamp\"\": \"\"2018-10-12T06:08:50.601897\"\"}]\"\r\n"
}
] |
{
"format": "csv",
"complexity": "complex",
"schema": "contract",
"type": "conversion",
"prompt": "Transform this data from JSON to CSV:\n{\n \"contract\": \"syndicate open-source e-commerce\",\n \"parties\": [\n \"Lawrence PLC\",\n \"Glover-Miller\"\n ],\n \"signed\": true,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:05.172344\",\n \"version\": \"v3.0\",\n \"tags\": [\n \"one\",\n \"year\",\n \"family\",\n \"boy\",\n \"ability\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"michelefloyd\",\n \"timestamp\": \"2012-10-17T22:03:38.225306\"\n },\n {\n \"action\": \"created\",\n \"by\": \"haleyrebecca\",\n \"timestamp\": \"1975-12-22T08:49:25.622175\"\n },\n {\n \"action\": \"created\",\n \"by\": \"juliamckee\",\n \"timestamp\": \"2018-10-12T06:08:50.601897\"\n }\n ]\n}",
"output": "Approach:\n1. Analyze the input JSON structure: It represents a dictionary with 5 root fields (contract, parties, signed...) and a nesting depth of 3.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ncontract,parties,signed,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nsyndicate open-source e-commerce,\"[\"\"Lawrence PLC\"\", \"\"Glover-Miller\"\"]\",True,2026-01-22T04:03:05.172344,v3.0,\"[\"\"one\"\", \"\"year\"\", \"\"family\"\", \"\"boy\"\", \"\"ability\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"michelefloyd\"\", \"\"timestamp\"\": \"\"2012-10-17T22:03:38.225306\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"haleyrebecca\"\", \"\"timestamp\"\": \"\"1975-12-22T08:49:25.622175\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"juliamckee\"\", \"\"timestamp\"\": \"\"2018-10-12T06:08:50.601897\"\"}]\"\r\n",
"estimated_tokens": 288
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Convert the following XML to YAML:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<social_media_post>\n<user>moyerjohn</user>\n<post>Team writer should. Democratic tree in doctor leg enter feeling stay.</post>\n<likes>135</likes>\n</social_media_post>"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 3 root fields (user, post, likes) and a nesting depth of 1.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nuser: moyerjohn\npost: Team writer should. Democratic tree in doctor leg enter feeling stay.\nlikes: 135\n"
}
] |
{
"format": "yaml",
"complexity": "simple",
"schema": "social_media_post",
"type": "conversion",
"prompt": "Convert the following XML to YAML:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<social_media_post>\n<user>moyerjohn</user>\n<post>Team writer should. Democratic tree in doctor leg enter feeling stay.</post>\n<likes>135</likes>\n</social_media_post>",
"output": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 3 root fields (user, post, likes) and a nesting depth of 1.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nuser: moyerjohn\npost: Team writer should. Democratic tree in doctor leg enter feeling stay.\nlikes: 135\n",
"estimated_tokens": 168
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Output structured data for transaction_record using YAML."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The transaction_record should contain a dictionary with 5 root fields (txn, amount, status...) and a nesting depth of 3.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ntxn: 65129b6d-3a7e-4539-b313-cfd54baa3601\namount: 146.7\nstatus: completed\nmeta_info:\n generated_at: '2026-01-22T04:03:06.215611'\n version: v1.0\n tags:\n - because\n - everyone\n - particularly\n - reveal\n - education\naudit_log:\n- action: created\n by: ocline\n timestamp: '2006-09-27T15:52:18.113473'\n"
}
] |
{
"format": "yaml",
"complexity": "complex",
"schema": "transaction_record",
"type": "generation",
"prompt": "Output structured data for transaction_record using YAML.",
"output": "Approach:\n1. Define the schema: The transaction_record should contain a dictionary with 5 root fields (txn, amount, status...) and a nesting depth of 3.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ntxn: 65129b6d-3a7e-4539-b313-cfd54baa3601\namount: 146.7\nstatus: completed\nmeta_info:\n generated_at: '2026-01-22T04:03:06.215611'\n version: v1.0\n tags:\n - because\n - everyone\n - particularly\n - reveal\n - education\naudit_log:\n- action: created\n by: ocline\n timestamp: '2006-09-27T15:52:18.113473'\n",
"estimated_tokens": 214
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Convert the following TOML to XML:\nid = \"1d9061f2-fb53-4be8-acad-6da5016ea71d\"\npatient = \"Jon Hawkins\"\ndiagnosis = \"fear\"\nnotes = \"Once sell station between office wall popular. When there shoulder.\\nLive these personal activity. Road teach concern. Song theory story total note suddenly already international.\"\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 4 root fields (id, patient, diagnosis...) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<clinical_note>\n<id>1d9061f2-fb53-4be8-acad-6da5016ea71d</id>\n<patient>Jon Hawkins</patient>\n<diagnosis>fear</diagnosis>\n<notes>Once sell station between office wall popular. When there shoulder.\nLive these personal activity. Road teach concern. Song theory story total note suddenly already international.</notes>\n</clinical_note>"
}
] |
{
"format": "xml",
"complexity": "simple",
"schema": "clinical_note",
"type": "conversion",
"prompt": "Convert the following TOML to XML:\nid = \"1d9061f2-fb53-4be8-acad-6da5016ea71d\"\npatient = \"Jon Hawkins\"\ndiagnosis = \"fear\"\nnotes = \"Once sell station between office wall popular. When there shoulder.\\nLive these personal activity. Road teach concern. Song theory story total note suddenly already international.\"\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 4 root fields (id, patient, diagnosis...) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<clinical_note>\n<id>1d9061f2-fb53-4be8-acad-6da5016ea71d</id>\n<patient>Jon Hawkins</patient>\n<diagnosis>fear</diagnosis>\n<notes>Once sell station between office wall popular. When there shoulder.\nLive these personal activity. Road teach concern. Song theory story total note suddenly already international.</notes>\n</clinical_note>",
"estimated_tokens": 237
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Output structured data for course_syllabus using JSON."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The course_syllabus should contain a dictionary with 3 root fields (course, weeks, credits) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"course\": \"Adaptive value-added knowledge user\",\n \"weeks\": 10,\n \"credits\": 3\n}"
}
] |
{
"format": "json",
"complexity": "medium",
"schema": "course_syllabus",
"type": "generation",
"prompt": "Output structured data for course_syllabus using JSON.",
"output": "Approach:\n1. Define the schema: The course_syllabus should contain a dictionary with 3 root fields (course, weeks, credits) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"course\": \"Adaptive value-added knowledge user\",\n \"weeks\": 10,\n \"credits\": 3\n}",
"estimated_tokens": 158
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Reformat the TOML below into XML:\nstudent = \"Kelly Tucker\"\nscore = 82\ngrade = \"B\"\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (student, score, grade) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<student_assessment>\n<student>Kelly Tucker</student>\n<score>82</score>\n<grade>B</grade>\n</student_assessment>"
}
] |
{
"format": "xml",
"complexity": "simple",
"schema": "student_assessment",
"type": "conversion",
"prompt": "Reformat the TOML below into XML:\nstudent = \"Kelly Tucker\"\nscore = 82\ngrade = \"B\"\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (student, score, grade) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<student_assessment>\n<student>Kelly Tucker</student>\n<score>82</score>\n<grade>B</grade>\n</student_assessment>",
"estimated_tokens": 181
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Generate error log data in XML format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The error_log should contain a dictionary with 3 root fields (timestamp, level, message) and a nesting depth of 1.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<error_log>\n<timestamp>2026-01-22T04:03:06.821685</timestamp>\n<level>ERROR</level>\n<message>Follow perform image fight end drop.</message>\n</error_log>"
}
] |
{
"format": "xml",
"complexity": "medium",
"schema": "error_log",
"type": "generation",
"prompt": "Generate error log data in XML format.",
"output": "Approach:\n1. Define the schema: The error_log should contain a dictionary with 3 root fields (timestamp, level, message) and a nesting depth of 1.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<error_log>\n<timestamp>2026-01-22T04:03:06.821685</timestamp>\n<level>ERROR</level>\n<message>Follow perform image fight end drop.</message>\n</error_log>",
"estimated_tokens": 185
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Convert the following CSV to XML:\nuser,rating,comment,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\njeffersonjames,4,Medical myself however weight admit for likely say.,2026-01-22T04:03:05.096294,v1.0,\"[\"\"note\"\", \"\"police\"\", \"\"offer\"\", \"\"sing\"\", \"\"friend\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"jenniferbartlett\"\", \"\"timestamp\"\": \"\"2023-09-11T15:31:12.547088\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"ahoward\"\", \"\"timestamp\"\": \"\"1992-07-11T20:06:55.844429\"\"}]\"\r\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 5 root fields (user, rating, comment...) and a nesting depth of 3.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<customer_review>\n<user>jeffersonjames</user>\n<rating>4</rating>\n<comment>Medical myself however weight admit for likely say.</comment>\n<meta_info>\n<generated_at>2026-01-22T04:03:05.096294</generated_at>\n<version>v1.0</version>\n<tags>note</tags>\n<tags>police</tags>\n<tags>offer</tags>\n<tags>sing</tags>\n<tags>friend</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>jenniferbartlett</by>\n<timestamp>2023-09-11T15:31:12.547088</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>ahoward</by>\n<timestamp>1992-07-11T20:06:55.844429</timestamp>\n</audit_log>\n</customer_review>"
}
] |
{
"format": "xml",
"complexity": "complex",
"schema": "customer_review",
"type": "conversion",
"prompt": "Convert the following CSV to XML:\nuser,rating,comment,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\njeffersonjames,4,Medical myself however weight admit for likely say.,2026-01-22T04:03:05.096294,v1.0,\"[\"\"note\"\", \"\"police\"\", \"\"offer\"\", \"\"sing\"\", \"\"friend\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"jenniferbartlett\"\", \"\"timestamp\"\": \"\"2023-09-11T15:31:12.547088\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"ahoward\"\", \"\"timestamp\"\": \"\"1992-07-11T20:06:55.844429\"\"}]\"\r\n",
"output": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 5 root fields (user, rating, comment...) and a nesting depth of 3.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<customer_review>\n<user>jeffersonjames</user>\n<rating>4</rating>\n<comment>Medical myself however weight admit for likely say.</comment>\n<meta_info>\n<generated_at>2026-01-22T04:03:05.096294</generated_at>\n<version>v1.0</version>\n<tags>note</tags>\n<tags>police</tags>\n<tags>offer</tags>\n<tags>sing</tags>\n<tags>friend</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>jenniferbartlett</by>\n<timestamp>2023-09-11T15:31:12.547088</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>ahoward</by>\n<timestamp>1992-07-11T20:06:55.844429</timestamp>\n</audit_log>\n</customer_review>",
"estimated_tokens": 303
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Generate product listing data in TOML format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 3 root fields (product, price, stock) and a nesting depth of 1.\n2. Select the format: TOML is chosen. Define hierarchy using dot-notation or [section] headers.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nproduct = \"poor\"\nprice = 73.89\nstock = 4\n"
}
] |
{
"format": "toml",
"complexity": "medium",
"schema": "product_listing",
"type": "generation",
"prompt": "Generate product listing data in TOML format.",
"output": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 3 root fields (product, price, stock) and a nesting depth of 1.\n2. Select the format: TOML is chosen. Define hierarchy using dot-notation or [section] headers.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nproduct = \"poor\"\nprice = 73.89\nstock = 4\n",
"estimated_tokens": 145
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Output structured data for lab_result using CSV."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nid,test,value\r\n897a27ea-8fb5-420c-b42b-e1a5c40c2759,later,99.72910826248814\r\n"
}
] |
{
"format": "csv",
"complexity": "simple",
"schema": "lab_result",
"type": "generation",
"prompt": "Output structured data for lab_result using CSV.",
"output": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nid,test,value\r\n897a27ea-8fb5-420c-b42b-e1a5c40c2759,later,99.72910826248814\r\n",
"estimated_tokens": 157
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Output structured data for clinical_note using JSON."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The clinical_note should contain a dictionary with 6 root fields (id, patient, diagnosis...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"id\": \"faf6572c-1f63-4642-90a4-4fc20e61e457\",\n \"patient\": \"Amanda Bell\",\n \"diagnosis\": \"smile\",\n \"notes\": \"Action alone science season ago. Law better eight them everyone theory affect. Capital law situation practice trouble.\",\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:06.298547\",\n \"version\": \"v4.0\",\n \"tags\": [\n \"understand\",\n \"feel\",\n \"floor\",\n \"me\",\n \"especially\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"tiffanyberg\",\n \"timestamp\": \"2002-12-14T15:01:11.646925\"\n },\n {\n \"action\": \"created\",\n \"by\": \"michaelsmith\",\n \"timestamp\": \"2013-08-03T12:02:23.573499\"\n },\n {\n \"action\": \"created\",\n \"by\": \"melissa10\",\n \"timestamp\": \"1980-07-10T18:06:08.132136\"\n }\n ]\n}"
}
] |
{
"format": "json",
"complexity": "complex",
"schema": "clinical_note",
"type": "generation",
"prompt": "Output structured data for clinical_note using JSON.",
"output": "Approach:\n1. Define the schema: The clinical_note should contain a dictionary with 6 root fields (id, patient, diagnosis...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"id\": \"faf6572c-1f63-4642-90a4-4fc20e61e457\",\n \"patient\": \"Amanda Bell\",\n \"diagnosis\": \"smile\",\n \"notes\": \"Action alone science season ago. Law better eight them everyone theory affect. Capital law situation practice trouble.\",\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:06.298547\",\n \"version\": \"v4.0\",\n \"tags\": [\n \"understand\",\n \"feel\",\n \"floor\",\n \"me\",\n \"especially\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"tiffanyberg\",\n \"timestamp\": \"2002-12-14T15:01:11.646925\"\n },\n {\n \"action\": \"created\",\n \"by\": \"michaelsmith\",\n \"timestamp\": \"2013-08-03T12:02:23.573499\"\n },\n {\n \"action\": \"created\",\n \"by\": \"melissa10\",\n \"timestamp\": \"1980-07-10T18:06:08.132136\"\n }\n ]\n}",
"estimated_tokens": 336
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Reformat the CSV below into JSON:\nstudent,score,grade\r\nRobert Lopez,6,B\r\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 3 root fields (student, score, grade) and a nesting depth of 1.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"student\": \"Robert Lopez\",\n \"score\": 6,\n \"grade\": \"B\"\n}"
}
] |
{
"format": "json",
"complexity": "medium",
"schema": "student_assessment",
"type": "conversion",
"prompt": "Reformat the CSV below into JSON:\nstudent,score,grade\r\nRobert Lopez,6,B\r\n",
"output": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 3 root fields (student, score, grade) and a nesting depth of 1.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"student\": \"Robert Lopez\",\n \"score\": 6,\n \"grade\": \"B\"\n}",
"estimated_tokens": 158
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Create a XML representation of student assessment."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The student_assessment should contain a dictionary with 3 root fields (student, score, grade) and a nesting depth of 1.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<student_assessment>\n<student>John Phillips</student>\n<score>18</score>\n<grade>A</grade>\n</student_assessment>"
}
] |
{
"format": "xml",
"complexity": "medium",
"schema": "student_assessment",
"type": "generation",
"prompt": "Create a XML representation of student assessment.",
"output": "Approach:\n1. Define the schema: The student_assessment should contain a dictionary with 3 root fields (student, score, grade) and a nesting depth of 1.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<student_assessment>\n<student>John Phillips</student>\n<score>18</score>\n<grade>A</grade>\n</student_assessment>",
"estimated_tokens": 176
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Generate api specification data in CSV format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The api_specification should contain a dictionary with 5 root fields (endpoint, method, responses...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nendpoint,method,responses.200,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\n/api/v1/users,GET,OK,2026-01-22T04:03:05.057559,v3.0,\"[\"\"respond\"\", \"\"week\"\", \"\"across\"\", \"\"market\"\", \"\"they\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"lmclaughlin\"\", \"\"timestamp\"\": \"\"2005-02-11T13:11:29.129834\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"verickson\"\", \"\"timestamp\"\": \"\"2002-03-12T22:49:54.265695\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"hwilliams\"\", \"\"timestamp\"\": \"\"1980-12-27T10:13:52.544021\"\"}]\"\r\n"
}
] |
{
"format": "csv",
"complexity": "complex",
"schema": "api_specification",
"type": "generation",
"prompt": "Generate api specification data in CSV format.",
"output": "Approach:\n1. Define the schema: The api_specification should contain a dictionary with 5 root fields (endpoint, method, responses...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nendpoint,method,responses.200,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\n/api/v1/users,GET,OK,2026-01-22T04:03:05.057559,v3.0,\"[\"\"respond\"\", \"\"week\"\", \"\"across\"\", \"\"market\"\", \"\"they\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"lmclaughlin\"\", \"\"timestamp\"\": \"\"2005-02-11T13:11:29.129834\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"verickson\"\", \"\"timestamp\"\": \"\"2002-03-12T22:49:54.265695\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"hwilliams\"\", \"\"timestamp\"\": \"\"1980-12-27T10:13:52.544021\"\"}]\"\r\n",
"estimated_tokens": 270
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Reformat the XML below into JSON:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<social_media_post>\n<user>staceysalazar</user>\n<post>Remember say produce hit decade include thought. Sit green force table prove. Political season administration.</post>\n<likes>154</likes>\n<meta_info>\n<generated_at>2026-01-22T04:03:06.716958</generated_at>\n<version>v3.0</version>\n<tags>child</tags>\n<tags>red</tags>\n<tags>small</tags>\n<tags>late</tags>\n<tags>sometimes</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>michael81</by>\n<timestamp>2005-02-24T19:33:58.673431</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>lisa65</by>\n<timestamp>1992-08-02T16:56:20.836960</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>sethshields</by>\n<timestamp>2024-07-21T13:32:39.224149</timestamp>\n</audit_log>\n</social_media_post>"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 5 root fields (user, post, likes...) and a nesting depth of 3.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"user\": \"staceysalazar\",\n \"post\": \"Remember say produce hit decade include thought. Sit green force table prove. Political season administration.\",\n \"likes\": 154,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:06.716958\",\n \"version\": \"v3.0\",\n \"tags\": [\n \"child\",\n \"red\",\n \"small\",\n \"late\",\n \"sometimes\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"michael81\",\n \"timestamp\": \"2005-02-24T19:33:58.673431\"\n },\n {\n \"action\": \"created\",\n \"by\": \"lisa65\",\n \"timestamp\": \"1992-08-02T16:56:20.836960\"\n },\n {\n \"action\": \"created\",\n \"by\": \"sethshields\",\n \"timestamp\": \"2024-07-21T13:32:39.224149\"\n }\n ]\n}"
}
] |
{
"format": "json",
"complexity": "complex",
"schema": "social_media_post",
"type": "conversion",
"prompt": "Reformat the XML below into JSON:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<social_media_post>\n<user>staceysalazar</user>\n<post>Remember say produce hit decade include thought. Sit green force table prove. Political season administration.</post>\n<likes>154</likes>\n<meta_info>\n<generated_at>2026-01-22T04:03:06.716958</generated_at>\n<version>v3.0</version>\n<tags>child</tags>\n<tags>red</tags>\n<tags>small</tags>\n<tags>late</tags>\n<tags>sometimes</tags>\n</meta_info>\n<audit_log>\n<action>created</action>\n<by>michael81</by>\n<timestamp>2005-02-24T19:33:58.673431</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>lisa65</by>\n<timestamp>1992-08-02T16:56:20.836960</timestamp>\n</audit_log>\n<audit_log>\n<action>created</action>\n<by>sethshields</by>\n<timestamp>2024-07-21T13:32:39.224149</timestamp>\n</audit_log>\n</social_media_post>",
"output": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 5 root fields (user, post, likes...) and a nesting depth of 3.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"user\": \"staceysalazar\",\n \"post\": \"Remember say produce hit decade include thought. Sit green force table prove. Political season administration.\",\n \"likes\": 154,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:06.716958\",\n \"version\": \"v3.0\",\n \"tags\": [\n \"child\",\n \"red\",\n \"small\",\n \"late\",\n \"sometimes\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"michael81\",\n \"timestamp\": \"2005-02-24T19:33:58.673431\"\n },\n {\n \"action\": \"created\",\n \"by\": \"lisa65\",\n \"timestamp\": \"1992-08-02T16:56:20.836960\"\n },\n {\n \"action\": \"created\",\n \"by\": \"sethshields\",\n \"timestamp\": \"2024-07-21T13:32:39.224149\"\n }\n ]\n}",
"estimated_tokens": 322
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Generate transaction record data in TOML format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The transaction_record should contain a dictionary with 3 root fields (txn, amount, status) and a nesting depth of 1.\n2. Select the format: TOML is chosen. Define hierarchy using dot-notation or [section] headers.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ntxn = \"b5419c9f-44d8-4ece-83e3-af9c3d23a56f\"\namount = 7.37\nstatus = \"completed\"\n"
}
] |
{
"format": "toml",
"complexity": "medium",
"schema": "transaction_record",
"type": "generation",
"prompt": "Generate transaction record data in TOML format.",
"output": "Approach:\n1. Define the schema: The transaction_record should contain a dictionary with 3 root fields (txn, amount, status) and a nesting depth of 1.\n2. Select the format: TOML is chosen. Define hierarchy using dot-notation or [section] headers.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ntxn = \"b5419c9f-44d8-4ece-83e3-af9c3d23a56f\"\namount = 7.37\nstatus = \"completed\"\n",
"estimated_tokens": 155
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Output structured data for experiment_result using XML."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The experiment_result should contain a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<experiment_result>\n<experiment_id>EXP-712069</experiment_id>\n<title>Analysis of hard</title>\n<metrics>\n<accuracy>0.902</accuracy>\n<loss>0.1319</loss>\n<epoch>85</epoch>\n</metrics>\n<config>\n<batch_size>64</batch_size>\n<optimizer>adam</optimizer>\n<lr>0.001</lr>\n</config>\n</experiment_result>"
}
] |
{
"format": "xml",
"complexity": "medium",
"schema": "experiment_result",
"type": "generation",
"prompt": "Output structured data for experiment_result using XML.",
"output": "Approach:\n1. Define the schema: The experiment_result should contain a dictionary with 4 root fields (experiment_id, title, metrics...) and a nesting depth of 2.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<experiment_result>\n<experiment_id>EXP-712069</experiment_id>\n<title>Analysis of hard</title>\n<metrics>\n<accuracy>0.902</accuracy>\n<loss>0.1319</loss>\n<epoch>85</epoch>\n</metrics>\n<config>\n<batch_size>64</batch_size>\n<optimizer>adam</optimizer>\n<lr>0.001</lr>\n</config>\n</experiment_result>",
"estimated_tokens": 223
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Output structured data for sales_analytics using XML."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The sales_analytics should contain a dictionary with 3 root fields (period, sales, region) and a nesting depth of 1.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<sales_analytics>\n<period>Q1</period>\n<sales>3806</sales>\n<region>West Anitaville</region>\n</sales_analytics>"
}
] |
{
"format": "xml",
"complexity": "medium",
"schema": "sales_analytics",
"type": "generation",
"prompt": "Output structured data for sales_analytics using XML.",
"output": "Approach:\n1. Define the schema: The sales_analytics should contain a dictionary with 3 root fields (period, sales, region) and a nesting depth of 1.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<sales_analytics>\n<period>Q1</period>\n<sales>3806</sales>\n<region>West Anitaville</region>\n</sales_analytics>",
"estimated_tokens": 175
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Convert the following TOML to XML:\ncourse = \"Secured national structure\"\nweeks = 9\ncredits = 3\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (course, weeks, credits) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<course_syllabus>\n<course>Secured national structure</course>\n<weeks>9</weeks>\n<credits>3</credits>\n</course_syllabus>"
}
] |
{
"format": "xml",
"complexity": "medium",
"schema": "course_syllabus",
"type": "conversion",
"prompt": "Convert the following TOML to XML:\ncourse = \"Secured national structure\"\nweeks = 9\ncredits = 3\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (course, weeks, credits) and a nesting depth of 1.\n2. Identify the target format requirements: XML requires strict syntax. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<course_syllabus>\n<course>Secured national structure</course>\n<weeks>9</weeks>\n<credits>3</credits>\n</course_syllabus>",
"estimated_tokens": 183
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Generate lab result data in JSON format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"id\": \"6e591c2c-cc68-4d05-ac15-c20ef70f7229\",\n \"test\": \"board\",\n \"value\": 55.329482418139975\n}"
}
] |
{
"format": "json",
"complexity": "medium",
"schema": "lab_result",
"type": "generation",
"prompt": "Generate lab result data in JSON format.",
"output": "Approach:\n1. Define the schema: The lab_result should contain a dictionary with 3 root fields (id, test, value) and a nesting depth of 1.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"id\": \"6e591c2c-cc68-4d05-ac15-c20ef70f7229\",\n \"test\": \"board\",\n \"value\": 55.329482418139975\n}",
"estimated_tokens": 159
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Generate product listing data in JSON format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 5 root fields (product, price, stock...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"product\": \"there\",\n \"price\": 46.25,\n \"stock\": 34,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:04.998742\",\n \"version\": \"v2.0\",\n \"tags\": [\n \"camera\",\n \"news\",\n \"kid\",\n \"for\",\n \"own\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"bakerstacy\",\n \"timestamp\": \"2000-11-04T10:52:59.673937\"\n },\n {\n \"action\": \"created\",\n \"by\": \"gsmith\",\n \"timestamp\": \"2021-07-21T12:03:17.086905\"\n }\n ]\n}"
}
] |
{
"format": "json",
"complexity": "complex",
"schema": "product_listing",
"type": "generation",
"prompt": "Generate product listing data in JSON format.",
"output": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 5 root fields (product, price, stock...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"product\": \"there\",\n \"price\": 46.25,\n \"stock\": 34,\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:04.998742\",\n \"version\": \"v2.0\",\n \"tags\": [\n \"camera\",\n \"news\",\n \"kid\",\n \"for\",\n \"own\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"bakerstacy\",\n \"timestamp\": \"2000-11-04T10:52:59.673937\"\n },\n {\n \"action\": \"created\",\n \"by\": \"gsmith\",\n \"timestamp\": \"2021-07-21T12:03:17.086905\"\n }\n ]\n}",
"estimated_tokens": 259
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Generate product listing data in CSV format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 5 root fields (product, price, stock...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nproduct,price,stock,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nguess,38.69,37,2026-01-22T04:03:07.009581,v3.0,\"[\"\"though\"\", \"\"fall\"\", \"\"she\"\", \"\"pass\"\", \"\"though\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"millercameron\"\", \"\"timestamp\"\": \"\"1987-09-07T04:14:05.471635\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"bryantsabrina\"\", \"\"timestamp\"\": \"\"1976-08-01T23:58:31.998373\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"gpatterson\"\", \"\"timestamp\"\": \"\"1997-07-15T19:10:47.077986\"\"}]\"\r\n"
}
] |
{
"format": "csv",
"complexity": "complex",
"schema": "product_listing",
"type": "generation",
"prompt": "Generate product listing data in CSV format.",
"output": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 5 root fields (product, price, stock...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nproduct,price,stock,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nguess,38.69,37,2026-01-22T04:03:07.009581,v3.0,\"[\"\"though\"\", \"\"fall\"\", \"\"she\"\", \"\"pass\"\", \"\"though\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"millercameron\"\", \"\"timestamp\"\": \"\"1987-09-07T04:14:05.471635\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"bryantsabrina\"\", \"\"timestamp\"\": \"\"1976-08-01T23:58:31.998373\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"gpatterson\"\", \"\"timestamp\"\": \"\"1997-07-15T19:10:47.077986\"\"}]\"\r\n",
"estimated_tokens": 265
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Create a CSV representation of financial report."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The financial_report should contain a dictionary with 5 root fields (company, revenue, profit...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ncompany,revenue,profit,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nSkinner Ltd,830953,93279,2026-01-22T04:03:05.100685,v2.0,\"[\"\"artist\"\", \"\"know\"\", \"\"appear\"\", \"\"well\"\", \"\"role\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"valerie40\"\", \"\"timestamp\"\": \"\"2014-09-29T07:24:14.273386\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"brandy04\"\", \"\"timestamp\"\": \"\"1988-04-13T17:50:16.124494\"\"}]\"\r\n"
}
] |
{
"format": "csv",
"complexity": "complex",
"schema": "financial_report",
"type": "generation",
"prompt": "Create a CSV representation of financial report.",
"output": "Approach:\n1. Define the schema: The financial_report should contain a dictionary with 5 root fields (company, revenue, profit...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ncompany,revenue,profit,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nSkinner Ltd,830953,93279,2026-01-22T04:03:05.100685,v2.0,\"[\"\"artist\"\", \"\"know\"\", \"\"appear\"\", \"\"well\"\", \"\"role\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"valerie40\"\", \"\"timestamp\"\": \"\"2014-09-29T07:24:14.273386\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"brandy04\"\", \"\"timestamp\"\": \"\"1988-04-13T17:50:16.124494\"\"}]\"\r\n",
"estimated_tokens": 243
}
|
[
{
"role": "system",
"content": "You are an expert in TOML format. Analyze the data structure and generate syntactically perfect TOML."
},
{
"role": "user",
"content": "Transform this data from CSV to TOML:\ncontract,parties,signed\r\ndeliver web-enabled vortals,\"[\"\"Murphy Inc\"\", \"\"Neal-Wolf\"\"]\",True\r\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 3 root fields (contract, parties, signed) and a nesting depth of 2.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ncontract = \"deliver web-enabled vortals\"\nparties = [ \"Murphy Inc\", \"Neal-Wolf\",]\nsigned = true\n"
}
] |
{
"format": "toml",
"complexity": "medium",
"schema": "contract",
"type": "conversion",
"prompt": "Transform this data from CSV to TOML:\ncontract,parties,signed\r\ndeliver web-enabled vortals,\"[\"\"Murphy Inc\"\", \"\"Neal-Wolf\"\"]\",True\r\n",
"output": "Approach:\n1. Analyze the input CSV structure: It represents a dictionary with 3 root fields (contract, parties, signed) and a nesting depth of 2.\n2. Identify the target format requirements: TOML requires strict syntax. Define hierarchy using dot-notation or [section] headers.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ncontract = \"deliver web-enabled vortals\"\nparties = [ \"Murphy Inc\", \"Neal-Wolf\",]\nsigned = true\n",
"estimated_tokens": 165
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Generate product listing data in CSV format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 5 root fields (product, price, stock...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nproduct,price,stock,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nreason,97.18,46,2026-01-22T04:03:06.613948,v5.0,\"[\"\"grow\"\", \"\"include\"\", \"\"close\"\", \"\"decision\"\", \"\"loss\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"paulbowman\"\", \"\"timestamp\"\": \"\"1987-02-25T23:48:21.906535\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"michaela33\"\", \"\"timestamp\"\": \"\"1972-03-21T00:44:22.319687\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"pavery\"\", \"\"timestamp\"\": \"\"1972-02-24T04:42:29.868387\"\"}]\"\r\n"
}
] |
{
"format": "csv",
"complexity": "complex",
"schema": "product_listing",
"type": "generation",
"prompt": "Generate product listing data in CSV format.",
"output": "Approach:\n1. Define the schema: The product_listing should contain a dictionary with 5 root fields (product, price, stock...) and a nesting depth of 3.\n2. Select the format: CSV is chosen. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nproduct,price,stock,meta_info.generated_at,meta_info.version,meta_info.tags,audit_log\r\nreason,97.18,46,2026-01-22T04:03:06.613948,v5.0,\"[\"\"grow\"\", \"\"include\"\", \"\"close\"\", \"\"decision\"\", \"\"loss\"\"]\",\"[{\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"paulbowman\"\", \"\"timestamp\"\": \"\"1987-02-25T23:48:21.906535\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"michaela33\"\", \"\"timestamp\"\": \"\"1972-03-21T00:44:22.319687\"\"}, {\"\"action\"\": \"\"created\"\", \"\"by\"\": \"\"pavery\"\", \"\"timestamp\"\": \"\"1972-02-24T04:42:29.868387\"\"}]\"\r\n",
"estimated_tokens": 264
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Convert the following XML to JSON:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<social_media_post>\n<user>kpowers</user>\n<post>Article stage how final development receive front. Perhaps forward scientist need. Agree such civil loss agent those child very.</post>\n<likes>909</likes>\n</social_media_post>"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 3 root fields (user, post, likes) and a nesting depth of 1.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"user\": \"kpowers\",\n \"post\": \"Article stage how final development receive front. Perhaps forward scientist need. Agree such civil loss agent those child very.\",\n \"likes\": 909\n}"
}
] |
{
"format": "json",
"complexity": "medium",
"schema": "social_media_post",
"type": "conversion",
"prompt": "Convert the following XML to JSON:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<social_media_post>\n<user>kpowers</user>\n<post>Article stage how final development receive front. Perhaps forward scientist need. Agree such civil loss agent those child very.</post>\n<likes>909</likes>\n</social_media_post>",
"output": "Approach:\n1. Analyze the input XML structure: It represents a dictionary with 3 root fields (user, post, likes) and a nesting depth of 1.\n2. Identify the target format requirements: JSON requires strict syntax. Ensure all keys are quoted and nested structures use curly braces.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\n{\n \"user\": \"kpowers\",\n \"post\": \"Article stage how final development receive front. Perhaps forward scientist need. Agree such civil loss agent those child very.\",\n \"likes\": 909\n}",
"estimated_tokens": 187
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Create a YAML representation of api specification."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The api_specification should contain a dictionary with 3 root fields (endpoint, method, responses) and a nesting depth of 2.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nendpoint: /api/v1/users\nmethod: GET\nresponses:\n '200': OK\n"
}
] |
{
"format": "yaml",
"complexity": "simple",
"schema": "api_specification",
"type": "generation",
"prompt": "Create a YAML representation of api specification.",
"output": "Approach:\n1. Define the schema: The api_specification should contain a dictionary with 3 root fields (endpoint, method, responses) and a nesting depth of 2.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\nendpoint: /api/v1/users\nmethod: GET\nresponses:\n '200': OK\n",
"estimated_tokens": 154
}
|
[
{
"role": "system",
"content": "You are an expert in JSON format. Analyze the data structure and generate syntactically perfect JSON."
},
{
"role": "user",
"content": "Generate transaction record data in JSON format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The transaction_record should contain a dictionary with 5 root fields (txn, amount, status...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"txn\": \"a2a21367-3995-4fc1-9134-9127b3dce120\",\n \"amount\": 370.83,\n \"status\": \"completed\",\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:07.201416\",\n \"version\": \"v4.0\",\n \"tags\": [\n \"major\",\n \"about\",\n \"yourself\",\n \"would\",\n \"maintain\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"ernestjacobs\",\n \"timestamp\": \"1974-08-10T15:07:17.873548\"\n },\n {\n \"action\": \"created\",\n \"by\": \"bharmon\",\n \"timestamp\": \"1983-06-17T10:13:31.692769\"\n },\n {\n \"action\": \"created\",\n \"by\": \"madisonwashington\",\n \"timestamp\": \"1985-08-19T18:37:31.167179\"\n }\n ]\n}"
}
] |
{
"format": "json",
"complexity": "complex",
"schema": "transaction_record",
"type": "generation",
"prompt": "Generate transaction record data in JSON format.",
"output": "Approach:\n1. Define the schema: The transaction_record should contain a dictionary with 5 root fields (txn, amount, status...) and a nesting depth of 3.\n2. Select the format: JSON is chosen. Ensure all keys are quoted and nested structures use curly braces.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n{\n \"txn\": \"a2a21367-3995-4fc1-9134-9127b3dce120\",\n \"amount\": 370.83,\n \"status\": \"completed\",\n \"meta_info\": {\n \"generated_at\": \"2026-01-22T04:03:07.201416\",\n \"version\": \"v4.0\",\n \"tags\": [\n \"major\",\n \"about\",\n \"yourself\",\n \"would\",\n \"maintain\"\n ]\n },\n \"audit_log\": [\n {\n \"action\": \"created\",\n \"by\": \"ernestjacobs\",\n \"timestamp\": \"1974-08-10T15:07:17.873548\"\n },\n {\n \"action\": \"created\",\n \"by\": \"bharmon\",\n \"timestamp\": \"1983-06-17T10:13:31.692769\"\n },\n {\n \"action\": \"created\",\n \"by\": \"madisonwashington\",\n \"timestamp\": \"1985-08-19T18:37:31.167179\"\n }\n ]\n}",
"estimated_tokens": 303
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Transform this data from TOML to YAML:\ntimestamp = \"2026-01-22T04:03:06.848557\"\nlevel = \"ERROR\"\nmessage = \"Condition talk down.\"\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (timestamp, level, message) and a nesting depth of 1.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntimestamp: '2026-01-22T04:03:06.848557'\nlevel: ERROR\nmessage: Condition talk down.\n"
}
] |
{
"format": "yaml",
"complexity": "medium",
"schema": "error_log",
"type": "conversion",
"prompt": "Transform this data from TOML to YAML:\ntimestamp = \"2026-01-22T04:03:06.848557\"\nlevel = \"ERROR\"\nmessage = \"Condition talk down.\"\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 3 root fields (timestamp, level, message) and a nesting depth of 1.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntimestamp: '2026-01-22T04:03:06.848557'\nlevel: ERROR\nmessage: Condition talk down.\n",
"estimated_tokens": 165
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Transform this data from TOML to YAML:\ntxn = \"a2a21367-3995-4fc1-9134-9127b3dce120\"\namount = 370.83\nstatus = \"completed\"\n[[audit_log]]\naction = \"created\"\nby = \"ernestjacobs\"\ntimestamp = \"1974-08-10T15:07:17.873548\"\n\n[[audit_log]]\naction = \"created\"\nby = \"bharmon\"\ntimestamp = \"1983-06-17T10:13:31.692769\"\n\n[[audit_log]]\naction = \"created\"\nby = \"madisonwashington\"\ntimestamp = \"1985-08-19T18:37:31.167179\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:07.201416\"\nversion = \"v4.0\"\ntags = [ \"major\", \"about\", \"yourself\", \"would\", \"maintain\",]\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 5 root fields (txn, amount, status...) and a nesting depth of 3.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntxn: a2a21367-3995-4fc1-9134-9127b3dce120\namount: 370.83\nstatus: completed\nmeta_info:\n generated_at: '2026-01-22T04:03:07.201416'\n version: v4.0\n tags:\n - major\n - about\n - yourself\n - would\n - maintain\naudit_log:\n- action: created\n by: ernestjacobs\n timestamp: '1974-08-10T15:07:17.873548'\n- action: created\n by: bharmon\n timestamp: '1983-06-17T10:13:31.692769'\n- action: created\n by: madisonwashington\n timestamp: '1985-08-19T18:37:31.167179'\n"
}
] |
{
"format": "yaml",
"complexity": "complex",
"schema": "transaction_record",
"type": "conversion",
"prompt": "Transform this data from TOML to YAML:\ntxn = \"a2a21367-3995-4fc1-9134-9127b3dce120\"\namount = 370.83\nstatus = \"completed\"\n[[audit_log]]\naction = \"created\"\nby = \"ernestjacobs\"\ntimestamp = \"1974-08-10T15:07:17.873548\"\n\n[[audit_log]]\naction = \"created\"\nby = \"bharmon\"\ntimestamp = \"1983-06-17T10:13:31.692769\"\n\n[[audit_log]]\naction = \"created\"\nby = \"madisonwashington\"\ntimestamp = \"1985-08-19T18:37:31.167179\"\n\n[meta_info]\ngenerated_at = \"2026-01-22T04:03:07.201416\"\nversion = \"v4.0\"\ntags = [ \"major\", \"about\", \"yourself\", \"would\", \"maintain\",]\n",
"output": "Approach:\n1. Analyze the input TOML structure: It represents a dictionary with 5 root fields (txn, amount, status...) and a nesting depth of 3.\n2. Identify the target format requirements: YAML requires strict syntax. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\ntxn: a2a21367-3995-4fc1-9134-9127b3dce120\namount: 370.83\nstatus: completed\nmeta_info:\n generated_at: '2026-01-22T04:03:07.201416'\n version: v4.0\n tags:\n - major\n - about\n - yourself\n - would\n - maintain\naudit_log:\n- action: created\n by: ernestjacobs\n timestamp: '1974-08-10T15:07:17.873548'\n- action: created\n by: bharmon\n timestamp: '1983-06-17T10:13:31.692769'\n- action: created\n by: madisonwashington\n timestamp: '1985-08-19T18:37:31.167179'\n",
"estimated_tokens": 258
}
|
[
{
"role": "system",
"content": "You are an expert in CSV format. Analyze the data structure and generate syntactically perfect CSV."
},
{
"role": "user",
"content": "Convert the following YAML to CSV:\nendpoint: /api/v1/users\nmethod: GET\nresponses:\n '200': OK\n"
},
{
"role": "assistant",
"content": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 3 root fields (endpoint, method, responses) and a nesting depth of 2.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nendpoint,method,responses.200\r\n/api/v1/users,GET,OK\r\n"
}
] |
{
"format": "csv",
"complexity": "simple",
"schema": "api_specification",
"type": "conversion",
"prompt": "Convert the following YAML to CSV:\nendpoint: /api/v1/users\nmethod: GET\nresponses:\n '200': OK\n",
"output": "Approach:\n1. Analyze the input YAML structure: It represents a dictionary with 3 root fields (endpoint, method, responses) and a nesting depth of 2.\n2. Identify the target format requirements: CSV requires strict syntax. Flatten the hierarchical structure. Use dot notation for nested keys in the header.\n3. Map the fields: The root keys will map directly, while nested objects need careful formatting.\n4. Handle data types: Ensure strings, numbers, and booleans are formatted correctly for the target.\n5. Construct the output: serialized the data strictly following the target schema.\n\nOutput:\nendpoint,method,responses.200\r\n/api/v1/users,GET,OK\r\n",
"estimated_tokens": 162
}
|
[
{
"role": "system",
"content": "You are an expert in XML format. Analyze the data structure and generate syntactically perfect XML."
},
{
"role": "user",
"content": "Generate news article data in XML format."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The news_article should contain a dictionary with 3 root fields (headline, body, author) and a nesting depth of 1.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<news_article>\n<headline>Consumer say specific.</headline>\n<body>Enjoy adult professional garden oil easy tax technology. Nearly across sure physical least everyone buy. Continue more realize star.</body>\n<author>Damon Cisneros</author>\n</news_article>"
}
] |
{
"format": "xml",
"complexity": "medium",
"schema": "news_article",
"type": "generation",
"prompt": "Generate news article data in XML format.",
"output": "Approach:\n1. Define the schema: The news_article should contain a dictionary with 3 root fields (headline, body, author) and a nesting depth of 1.\n2. Select the format: XML is chosen. Map dictionary keys to tags. Handle list items by repeating child tags.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<news_article>\n<headline>Consumer say specific.</headline>\n<body>Enjoy adult professional garden oil easy tax technology. Nearly across sure physical least everyone buy. Continue more realize star.</body>\n<author>Damon Cisneros</author>\n</news_article>",
"estimated_tokens": 210
}
|
[
{
"role": "system",
"content": "You are an expert in YAML format. Analyze the data structure and generate syntactically perfect YAML."
},
{
"role": "user",
"content": "Output structured data for contract using YAML."
},
{
"role": "assistant",
"content": "Approach:\n1. Define the schema: The contract should contain a dictionary with 3 root fields (contract, parties, signed) and a nesting depth of 2.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ncontract: synthesize world-class info-mediaries\nparties:\n- Greene, Hayes and Jones\n- Henson Group\nsigned: true\n"
}
] |
{
"format": "yaml",
"complexity": "medium",
"schema": "contract",
"type": "generation",
"prompt": "Output structured data for contract using YAML.",
"output": "Approach:\n1. Define the schema: The contract should contain a dictionary with 3 root fields (contract, parties, signed) and a nesting depth of 2.\n2. Select the format: YAML is chosen. Use indentation to represent nesting. lists are denoted by hyphens.\n3. Generate realistic content: Use plausible names, dates, and values appropriate for the domain.\n4. Structure the data: Organize the fields logically with appropriate nesting and hierarchies.\n5. Serialize: Output the final string making sure to escape special characters if necessary.\n\nOutput:\ncontract: synthesize world-class info-mediaries\nparties:\n- Greene, Hayes and Jones\n- Henson Group\nsigned: true\n",
"estimated_tokens": 164
}
|
End of preview. Expand
in Data Studio
structured_data_with_cot_dataset
このデータセットは、様々な形式（JSON、XML、YAML、TOML、CSV）の構造化データと、それぞれに対応する簡潔な思考連鎖（Chain-of-Thought, CoT）推論を含む多様な例を提供します。
データセットの概要
- messages: OpenAIチャット形式 (system, user, assistant)
- metadata: format, complexity, schema, estimated_tokens
サポートされるデータ形式
- JSON, XML, YAML, TOML, CSV
生成方法
Fakerライブラリを使用し、Pythonスクリプトで生成。検証用・テスト用に分割済み。
- Downloads last month
- 33
Size of downloaded dataset files:
14.9 MB
Size of the auto-converted Parquet files:
3.49 MB
Number of rows:
5,758