[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Transform this JSON data into XML format:
{
  "paper_id": "arXiv:2022.79764",
  "title": "Progressive exuding adapter",
  "authors": [
    {
      "name": "Gary Johnson",
      "affiliation": "Wolf Group",
      "email": "jamie40@example.org"
    },
    {
      "name": "Lisa Ortega",
      "affiliation": "Wells, Flores and Cooper",
      "email": "nicholas25@example.com"
    },
    {
      "name": "Kimberly Alvarez",
      "affiliation": "Lewis-Hunt",
      "email": "bookersteven@example.com"
    }
  ],
  "abstract": "Win both purpose hour professor table.",
  "keywords": [
    "office",
    "during",
    "more",
    "apply",
    "program"
  ],
  "publication": {
    "venue": "Nature",
    "year": 2023,
    "doi": "10.1780/NyHo"
  },
  "citations": 258,
  "funding_source": "Espinoza, Khan and Lowe"
}

[Assistant]
Approach:
1. Task: Convert research paper from JSON to XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<research_paper>
<paper_id>arXiv:2022.79764</paper_id>
<title>Progressive exuding adapter</title>
<authors>
<name>Gary Johnson</name>
<affiliation>Wolf Group</affiliation>
<email>jamie40@example.org</email>
</authors>
<authors>
<name>Lisa Ortega</name>
<affiliation>Wells, Flores and Cooper</affiliation>
<email>nicholas25@example.com</email>
</authors>
<authors>
<name>Kimberly Alvarez</name>
<affiliation>Lewis-Hunt</affiliation>
<email>bookersteven@example.com</email>
</authors>
<abstract>Win both purpose hour professor table.</abstract>
<keywords>office</keywords>
<keywords>during</keywords>
<keywords>more</keywords>
<keywords>apply</keywords>
<keywords>program</keywords>
<publication>
<venue>Nature</venue>
<year>2023</year>
<doi>10.1780/NyHo</doi>
</publication>
<citations>258</citations>
<funding_source>Espinoza, Khan and Lowe</funding_source>
</research_paper>


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Produce a XML document for a research paper.

[Assistant]
Approach:
1. Task: Create research paper in XML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<research_paper>
<authors>
<name>Emily Davis</name>
<affiliation>Nelson, Diaz and Humphrey</affiliation>
<email>jpalmer@example.org</email>
</authors>
<authors>
<name>Robert Clark</name>
<affiliation>James Inc</affiliation>
<email>dennis02@example.net</email>
</authors>
<publication>
<venue>Science</venue>
<year>2022</year>
<doi>10.6797/tlMT</doi>
</publication>
<keywords>lead</keywords>
<keywords>list</keywords>
<keywords>she</keywords>
<funding_source />
<paper_id>arXiv:2022.72917</paper_id>
</research_paper>


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Output news article as TOML.

[Assistant]
Approach:
1. Task: Create news article in TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
article_id = "ART-9178511"
headline = "Fully-configurable secondary access"
author = "Gabrielle Daugherty"
published = "2026-01-05T07:34:40.744934"
section = "Tech"
content = "Lawyer might home mouth end so science. Hope attention strong public teach person beautiful though. Really decision land evening year.\nJust role ground discover. Five stand writer floor."
tags = [ "maybe", "glass", "election", "moment",]
views = 24250
shares = 778


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Convert the following XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<course_syllabus>
<course_id>CRS-2281</course_id>
NHA456
<title>Profit-focused mission-critical hardware 101</title>
<instructor>Natalie Middleton</instructor>
<credits>3</credits>
<term>Spring 2024</term>
<schedule>
<week>1</week>
<topic>its</topic>
</schedule>
<schedule>
<week>2</week>
<topic>attorney</topic>
</schedule>
<schedule>
<week>3</week>
<topic>decision</topic>
</schedule>
<schedule>
<week>4</week>
<topic>every</topic>
</schedule>
<schedule>
<week>5</week>
<topic>direction</topic>
</schedule>
<schedule>
<week>6</week>
<topic>player</topic>
</schedule>
<schedule>
<week>7</week>
<topic>city</topic>
</schedule>
<schedule>
<week>8</week>
<topic>everybody</topic>
</schedule>
<schedule>
<week>9</week>
<topic>throw</topic>
</schedule>
<grading>
<homework>30</homework>
<midterm>30</midterm>
<final>40</final>
</grading>
</course_syllabus>

[Assistant]
Approach:
1. Task: Convert course syllabus from XML to JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "course_id": "CRS-2281",
  "code": "NHA456",
  "title": "Profit-focused mission-critical hardware 101",
  "instructor": "Natalie Middleton",
  "credits": 3,
  "term": "Spring 2024",
  "schedule": [
    {
      "week": 1,
      "topic": "its"
    },
    {
      "week": 2,
      "topic": "attorney"
    },
    {
      "week": 3,
      "topic": "decision"
    },
    {
      "week": 4,
      "topic": "every"
    },
    {
      "week": 5,
      "topic": "direction"
    },
    {
      "week": 6,
      "topic": "player"
    },
    {
      "week": 7,
      "topic": "city"
    },
    {
      "week": 8,
      "topic": "everybody"
    },
    {
      "week": 9,
      "topic": "throw"
    }
  ],
  "grading": {
    "homework": 30,
    "midterm": 30,
    "final": 40
  }
}


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Produce a XML document for a student assessment.

[Assistant]
Approach:
1. Task: Create student assessment in XML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<student_assessment>
<grade>A</grade>
<course_id>CRS-4519</course_id>
<total_score>75</total_score>
</student_assessment>


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User]
Convert the following YAML to TOML:
expenses: 54498119
ticker: OKCY
company: Clark-Pearson

[Assistant]
Approach:
1. Task: Convert financial report from YAML to TOML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
expenses = 54498119
ticker = "OKCY"
company = "Clark-Pearson"


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User]
Transform this YAML data into TOML format:
product_id: PROD-986141
name: Focused neutral encoding
category: books
price: 14.45
currency: USD
stock: 826
is_available: true
rating: 4.59
reviews: 564
specs:
  weight_kg: 27.7
  dimensions: 42x99x90cm

[Assistant]
Approach:
1. Task: Convert product listing from YAML to TOML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
product_id = "PROD-986141"
name = "Focused neutral encoding"
category = "books"
price = 14.45
currency = "USD"
stock = 826
is_available = true
rating = 4.59
reviews = 564

[specs]
weight_kg = 27.7
dimensions = "42x99x90cm"


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Output course syllabus as YAML.

[Assistant]
Approach:
1. Task: Create course syllabus in YAML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
course_id: CRS-8855
code: DBP130
title: Team-oriented 3rdgeneration implementation 101
instructor: Carl Davis
credits: 3
term: Fall 2024
schedule:
- week: 1
  topic: manage
- week: 2
  topic: someone
- week: 3
  topic: bring
- week: 4
  topic: board
- week: 5
  topic: important
- week: 6
  topic: he
- week: 7
  topic: possible
- week: 8
  topic: office
- week: 9
  topic: board
- week: 10
  topic: drive
- week: 11
  topic: Mrs
grading:
  homework: 30
  midterm: 30
  final: 40


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Produce a JSON document for a financial report.

[Assistant]
Approach:
1. Task: Create financial report in JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "company": "Snow, Shelton and Parsons",
  "ticker": "LYEH",
  "quarter": "Q3 2022",
  "revenue": 49988929,
  "expenses": 3428077,
  "net_income": 8543670,
  "eps": 3.41,
  "metrics": {
    "profit_margin": 11.21,
    "roe": 5.99
  }
}


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User]
Convert the following TOML to YAML:
product_id = "PROD-597223"
name = "Proactive tertiary database"
category = "books"
price = 435.77
currency = "USD"
stock = 111
is_available = true
rating = 1.73
reviews = 539

[specs]
weight_kg = 19.61
dimensions = "87x91x85cm"

[Assistant]
Approach:
1. Task: Convert product listing from TOML to YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
product_id: PROD-597223
name: Proactive tertiary database
category: books
price: 435.77
currency: USD
stock: 111
is_available: true
rating: 1.73
reviews: 539
specs:
  weight_kg: 19.61
  dimensions: 87x91x85cm


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Generate transaction record data in JSON format.

[Assistant]
Approach:
1. Task: Create transaction record in JSON
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "subtotal": 1655.93,
  "items": [
    {
      "sku": "SKU-62708",
      "qty": 3,
      "price": 121.39
    }
  ],
  "tax": 19.12,
  "total": 1424.63
}


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Create a TOML representation of api specification.

[Assistant]
Approach:
1. Task: Create api specification in TOML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
openapi = "3.0.0"

[info]
title = "Customer-focused zero-defect array API"
version = "1.0.0"

[paths."/sense".get]
summary = "Pay yes agree executive year ahead program."
[[paths."/sense".get.parameters]]
name = "param0"
in = "query"
type = "string"

[paths."/sense".get.responses.200]
description = "Success"

[paths."/sense".get.responses.400]
description = "Bad Request"


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Create a TOML representation of course syllabus.

[Assistant]
Approach:
1. Task: Create course syllabus in TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
course_id = "CRS-9141"
code = "ESH384"
title = "Inverse real-time hardware 101"
instructor = "Matthew Boyd"
credits = 4
term = "Fall 2022"
[[schedule]]
week = 1
topic = "western"

[[schedule]]
week = 2
topic = "since"

[[schedule]]
week = 3
topic = "later"

[[schedule]]
week = 4
topic = "next"

[[schedule]]
week = 5
topic = "tree"

[[schedule]]
week = 6
topic = "until"

[[schedule]]
week = 7
topic = "everything"

[[schedule]]
week = 8
topic = "until"

[[schedule]]
week = 9
topic = "how"

[[schedule]]
week = 10
topic = "test"

[[schedule]]
week = 11
topic = "half"

[grading]
homework = 30
midterm = 30
final = 40


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Generate student assessment data in YAML format.

[Assistant]
Approach:
1. Task: Create student assessment in YAML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
grade: A
course_id: CRS-4519
total_score: 75


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Generate product listing data in JSON format.

[Assistant]
Approach:
1. Task: Create product listing in JSON
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "is_available": true,
  "rating": 1.03,
  "category": "electronics",
  "price": 302.74
}


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Create a JSON representation of prescription.

[Assistant]
Approach:
1. Task: Create prescription in JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "rx_id": "RX-9001624",
  "patient_mrn": "MRN-695608",
  "prescriber": "Stephen Davis",
  "date": "2026-01-05",
  "medications": [
    {
      "name": "Lisinopril",
      "dosage": "20mg",
      "frequency": "daily",
      "quantity": 90
    }
  ]
}


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Transform this XML data into JSON format:
<?xml version="1.0" encoding="UTF-8"?>
<financial_report>
<company>Smith-Glover</company>
<ticker>JROI</ticker>
<quarter>Q3 2023</quarter>
<revenue>95238846</revenue>
<expenses>30652579</expenses>
<net_income>5856951</net_income>
<eps>7.31</eps>
<metrics>
<profit_margin>9.94</profit_margin>
<roe>10.91</roe>
</metrics>
</financial_report>

[Assistant]
Approach:
1. Task: Convert financial report from XML to JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "company": "Smith-Glover",
  "ticker": "JROI",
  "quarter": "Q3 2023",
  "revenue": 95238846,
  "expenses": 30652579,
  "net_income": 5856951,
  "eps": 7.31,
  "metrics": {
    "profit_margin": 9.94,
    "roe": 10.91
  }
}


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Produce a XML document for a prescription.

[Assistant]
Approach:
1. Task: Create prescription in XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<prescription>
<rx_id>RX-3472120</rx_id>
<patient_mrn>MRN-972429</patient_mrn>
<prescriber>Matthew Bennett</prescriber>
<date>2026-01-06</date>
<medications>
<name>Atorvastatin</name>
<dosage>10mg</dosage>
<frequency>daily</frequency>
<quantity>60</quantity>
</medications>
</prescription>


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Create a TOML representation of student assessment.

[Assistant]
Approach:
1. Task: Create student assessment in TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
assessment_id = "ASMT-718533"
student_id = "STU-781568"
course_id = "CRS-7994"
type = "assignment"
date = "2026-01-03"
total_score = 62
percentage = 88.34
grade = "B"
[[scores]]
question = 1
points_earned = 8
points_possible = 10

[[scores]]
question = 2
points_earned = 4
points_possible = 10

[[scores]]
question = 3
points_earned = 5
points_possible = 10

[[scores]]
question = 4
points_earned = 8
points_possible = 10

[[scores]]
question = 5
points_earned = 0
points_possible = 10

[[scores]]
question = 6
points_earned = 5
points_possible = 10

[[scores]]
question = 7
points_earned = 2
points_possible = 10

[[scores]]
question = 8
points_earned = 2
points_possible = 10

[[scores]]
question = 9
points_earned = 6
points_possible = 10

[[scores]]
question = 10
points_earned = 4
points_possible = 10

[[scores]]
question = 11
points_earned = 8
points_possible = 10

[[scores]]
question = 12
points_earned = 4
points_possible = 10

[[scores]]
question = 13
points_earned = 7
points_possible = 10

[[scores]]
question = 14
points_earned = 3
points_possible = 10


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Transform this XML data into JSON format:
<?xml version="1.0" encoding="UTF-8"?>
<clinical_note>
<note_id>CN-4036732</note_id>
<patient>
<mrn>MRN-366942</mrn>
<age>79</age>
<gender>M</gender>
</patient>
<encounter_date>2026-01-06</encounter_date>
<chief_complaint>headache</chief_complaint>
<vitals>
<bp>102/96</bp>

61

<temp_f>97.1</temp_f>
</vitals>
<assessment>
<problem>hypertension</problem>
<icd10>K52</icd10>
</assessment>
</clinical_note>

[Assistant]
Approach:
1. Task: Convert clinical note from XML to JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "note_id": "CN-4036732",
  "patient": {
    "mrn": "MRN-366942",
    "age": 79,
    "gender": "M"
  },
  "encounter_date": "2026-01-06",
  "chief_complaint": "headache",
  "vitals": {
    "bp": "102/96",
    "hr": 61,
    "temp_f": 97.1
  },
  "assessment": [
    {
      "problem": "hypertension",
      "icd10": "K52"
    }
  ]
}


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Generate contract data in JSON format.

[Assistant]
Approach:
1. Task: Create contract in JSON
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "effective_date": "2026-01-02",
  "term_months": 54,
  "status": "pending"
}


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Output customer review as YAML.

[Assistant]
Approach:
1. Task: Create customer review in YAML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
review_id: REV-1274071
product_id: PROD-300785
user_id: c183a72e-4d6f-40ac-92b0-2d1073039571
rating: 5
title: Follow large high gas clear drug pretty else.
content: 'Recent cause family writer. Old less control he woman. Coach quickly will.

  Relationship same against one stand agreement. Be fine occur ten energy resource
  wall. Nation ten continue.'
date: '2026-01-06'
verified_purchase: true
helpful_votes: 37


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Reformat the data below from XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<error_log>
<error_id>19a033e5-3ad7-4877-8a96-e38503174031</error_id>
<timestamp>2026-01-07T13:51:26.139508</timestamp>
<level>ERROR</level>
<service>offService</service>
<message>Light method real rich option.</message>
<stack_trace>at very.py:253</stack_trace>
<stack_trace>at piece.py:469</stack_trace>
<stack_trace>at home.py:259</stack_trace>
<context>
<user_id>45de80c2-9a4d-4d83-abf9-651f235bf4e8</user_id>
<request_id>53b4884a-375f-459c-a303-838dd3a8411e</request_id>
</context>
</error_log>

[Assistant]
Approach:
1. Task: Convert error log from XML to JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "error_id": "19a033e5-3ad7-4877-8a96-e38503174031",
  "timestamp": "2026-01-07T13:51:26.139508",
  "level": "ERROR",
  "service": "offService",
  "message": "Light method real rich option.",
  "stack_trace": [
    "at very.py:253",
    "at piece.py:469",
    "at home.py:259"
  ],
  "context": {
    "user_id": "45de80c2-9a4d-4d83-abf9-651f235bf4e8",
    "request_id": "53b4884a-375f-459c-a303-838dd3a8411e"
  }
}


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Produce a TOML document for a financial report.

[Assistant]
Approach:
1. Task: Create financial report in TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
company = "Ortega-Owens"
ticker = "YLLB"
quarter = "Q3 2022"
revenue = 37598920
expenses = 44591506
net_income = 3197580
eps = 1.51

[metrics]
profit_margin = 5.54
roe = 6.32


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Reformat the data below from JSON to XML:
{
  "note_id": "CN-6224145",
  "patient": {
    "mrn": "MRN-560569",
    "age": 88,
    "gender": "F"
  },
  "encounter_date": "2026-01-04",
  "chief_complaint": "headache",
  "vitals": {
    "bp": "119/73",
    "hr": 63,
    "temp_f": 100.0
  },
  "assessment": [
    {
      "problem": "hypertension",
      "icd10": "I32"
    }
  ]
}

[Assistant]
Approach:
1. Task: Convert clinical note from JSON to XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<clinical_note>
<note_id>CN-6224145</note_id>
<patient>
<mrn>MRN-560569</mrn>
<age>88</age>
<gender>F</gender>
</patient>
<encounter_date>2026-01-04</encounter_date>
<chief_complaint>headache</chief_complaint>
<vitals>
<bp>119/73</bp>

63

<temp_f>100.0</temp_f>
</vitals>
<assessment>
<problem>hypertension</problem>
<icd10>I32</icd10>
</assessment>
</clinical_note>


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Produce a JSON document for a customer review.

[Assistant]
Approach:
1. Task: Create customer review in JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "review_id": "REV-2575466",
  "product_id": "PROD-346187",
  "user_id": "320eb87e-12bd-44ab-bb47-6fec45bd5144",
  "rating": 1,
  "title": "Possible modern last rate memory fight conference ",
  "content": "Character walk loss rock me social gun.\nCenter choice either push. Push stop yourself.",
  "date": "2026-01-01",
  "verified_purchase": false,
  "helpful_votes": 26
}


[System] You are an expert in CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User] Output experiment result as CSV.

[Assistant]
Approach:
1. Task: Create experiment result in CSV
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
conclusion,experiment_id,methodology_design,methodology_duration_days,methodology_sample_size,results,title
significant,EXP-883806,observational,32,200,"[{""metric"": ""efficacy"", ""baseline"": 15.64, ""endpoint"": 83.3, ""p_value"": 0.4248}, {""metric"": ""efficac",Effect of there on wife
significant,EXP-475821,cohort,203,755,"[{""metric"": ""safety"", ""baseline"": 78.96, ""endpoint"": 61.39, ""p_value"": 0.4815}, {""metric"": ""efficacy",Effect of increase on structure
non-significant,EXP-917458,observational,212,975,"[{""metric"": ""safety"", ""baseline"": 66.26, ""endpoint"": 49.08, ""p_value"": 0.1902}, {""metric"": ""safety"",",Effect of charge on represent
significant,EXP-289492,RCT,158,437,"[{""metric"": ""efficacy"", ""baseline"": 15.86, ""endpoint"": 90.6, ""p_value"": 0.4404}, {""metric"": ""efficac",Effect of area on science
non-significant,EXP-182998,observational,161,872,"[{""metric"": ""efficacy"", ""baseline"": 34.51, ""endpoint"": 67.83, ""p_value"": 0.2778}, {""metric"": ""safety",Effect of everybody on news


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Reformat the data below from XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<prescription>
<rx_id>RX-8350891</rx_id>
<patient_mrn>MRN-446802</patient_mrn>
<prescriber>James Bryant</prescriber>
<date>2026-01-03</date>
<medications>
<name>Lisinopril</name>
<dosage>10mg</dosage>
<frequency>daily</frequency>
<quantity>90</quantity>
</medications>
<medications>
<name>Metformin</name>
<dosage>20mg</dosage>
<frequency>TID</frequency>
<quantity>30</quantity>
</medications>
</prescription>

[Assistant]
Approach:
1. Task: Convert prescription from XML to JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "rx_id": "RX-8350891",
  "patient_mrn": "MRN-446802",
  "prescriber": "James Bryant",
  "date": "2026-01-03",
  "medications": [
    {
      "name": "Lisinopril",
      "dosage": "10mg",
      "frequency": "daily",
      "quantity": 90
    },
    {
      "name": "Metformin",
      "dosage": "20mg",
      "frequency": "TID",
      "quantity": 30
    }
  ]
}


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Generate clinical note data in JSON format.

[Assistant]
Approach:
1. Task: Create clinical note in JSON
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "assessment": [
    {
      "problem": "diabetes",
      "icd10": "I24"
    }
  ],
  "encounter_date": "2026-01-06",
  "patient": {
    "mrn": "MRN-470382",
    "age": 61,
    "gender": "F"
  },
  "note_id": "CN-3244099",
  "vitals": {
    "bp": "158/77",
    "hr": 112,
    "temp_f": 99.5
  }
}


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Create a XML representation of customer review.

[Assistant]
Approach:
1. Task: Create customer review in XML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<customer_review>
<review_id>REV-3982215</review_id>
<product_id>PROD-143836</product_id>
<user_id>0dd51960-e54b-4127-90d4-b32b17839969</user_id>
<rating>2</rating>
<title>Fight and building toward office.</title>
<content>Hear media safe I visit language. Difference part thousand. Society inside describe decision.</content>
<date>2026-01-06</date>
<verified_purchase>True</verified_purchase>
<helpful_votes>16</helpful_votes>
</customer_review>


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Reformat the data below from JSON to XML:
{
  "result_id": "LAB-4504067",
  "patient_mrn": "MRN-266543",
  "order_date": "2026-01-05",
  "panel": "Lipid",
  "results": [
    {
      "test": "Glucose",
      "value": 181.1,
      "unit": "g/dL",
      "flag": "normal"
    },
    {
      "test": "Hemoglobin",
      "value": 164.3,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "Glucose",
      "value": 140.0,
      "unit": "g/dL",
      "flag": "normal"
    },
    {
      "test": "Hemoglobin",
      "value": 113.0,
      "unit": "mg/dL",
      "flag": "high"
    }
  ]
}

[Assistant]
Approach:
1. Task: Convert lab result from JSON to XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<lab_result>
<result_id>LAB-4504067</result_id>
<patient_mrn>MRN-266543</patient_mrn>
<order_date>2026-01-05</order_date>
<panel>Lipid</panel>
<results>
<test>Glucose</test>
<value>181.1</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>164.3</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Glucose</test>
<value>140.0</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>113.0</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
</lab_result>


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User]
Convert the following XML to YAML:
<?xml version="1.0" encoding="UTF-8"?>
<customer_review>
<review_id>REV-5650887</review_id>
<product_id>PROD-601157</product_id>
<user_id>ac7725ae-2567-44d1-8d40-a64432b9801b</user_id>
<rating>3</rating>
<title>Personal order single check off but my.</title>
<content>Key Mrs say because walk old certainly. Product figure event choice.
Skill cut plan per side same. Between common join partner kid enter page discover.</content>
<date>2026-01-06</date>
<verified_purchase>True</verified_purchase>
<helpful_votes>66</helpful_votes>
</customer_review>

[Assistant]
Approach:
1. Task: Convert customer review from XML to YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
review_id: REV-5650887
product_id: PROD-601157
user_id: ac7725ae-2567-44d1-8d40-a64432b9801b
rating: 3
title: Personal order single check off but my.
content: 'Key Mrs say because walk old certainly. Product figure event choice.

  Skill cut plan per side same. Between common join partner kid enter page discover.'
date: '2026-01-06'
verified_purchase: true
helpful_votes: 66


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Transform this JSON data into XML format:
{
  "date": "2026-01-04",
  "medications": [
    {
      "name": "Atorvastatin",
      "dosage": "20mg",
      "frequency": "TID",
      "quantity": 60
    },
    {
      "name": "Metformin",
      "dosage": "20mg",
      "frequency": "daily",
      "quantity": 60
    },
    {
      "name": "Atorvastatin",
      "dosage": "5mg",
      "frequency": "TID",
      "quantity": 60
    }
  ],
  "patient_mrn": "MRN-588828"
}

[Assistant]
Approach:
1. Task: Convert prescription from JSON to XML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<prescription>
<date>2026-01-04</date>
<medications>
<name>Atorvastatin</name>
<dosage>20mg</dosage>
<frequency>TID</frequency>
<quantity>60</quantity>
</medications>
<medications>
<name>Metformin</name>
<dosage>20mg</dosage>
<frequency>daily</frequency>
<quantity>60</quantity>
</medications>
<medications>
<name>Atorvastatin</name>
<dosage>5mg</dosage>
<frequency>TID</frequency>
<quantity>60</quantity>
</medications>
<patient_mrn>MRN-588828</patient_mrn>
</prescription>


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Generate research paper data in JSON format.

[Assistant]
Approach:
1. Task: Create research paper in JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "paper_id": "arXiv:2020.59386",
  "title": "Self-enabling demand-driven application",
  "authors": [
    {
      "name": "Keith Wilcox",
      "affiliation": "Ruiz-Torres",
      "email": "martinstephanie@example.org"
    },
    {
      "name": "Jennifer Mccoy",
      "affiliation": "Thompson-Marshall",
      "email": "brianvelasquez@example.org"
    }
  ],
  "abstract": "Before today call network oil future. Admit itself catch song various rest. Personal worry quality director.",
  "keywords": [
    "song",
    "TV",
    "southern"
  ],
  "publication": {
    "venue": "Nature",
    "year": 2020,
    "doi": "10.1369/MikD"
  },
  "citations": 274,
  "funding_source": null
}


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Reformat the data below from JSON to XML:
{
  "error_id": "042db0a1-c9b1-49a9-9d69-5247a8c47159",
  "timestamp": "2026-01-07T13:51:27.377057",
  "level": "ERROR",
  "service": "whomService",
  "message": "Each reality trip red enough tough common.",
  "stack_trace": [
    "at note.py:398",
    "at purpose.py:452",
    "at onto.py:106",
    "at member.py:439"
  ],
  "context": {
    "user_id": "70c0c786-0e43-42b2-8ec7-e975f39a71f7",
    "request_id": "95ca942a-a33a-413f-b1a1-a21e12fa0f30"
  }
}

[Assistant]
Approach:
1. Task: Convert error log from JSON to XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<error_log>
<error_id>042db0a1-c9b1-49a9-9d69-5247a8c47159</error_id>
<timestamp>2026-01-07T13:51:27.377057</timestamp>
<level>ERROR</level>
<service>whomService</service>
<message>Each reality trip red enough tough common.</message>
<stack_trace>at note.py:398</stack_trace>
<stack_trace>at purpose.py:452</stack_trace>
<stack_trace>at onto.py:106</stack_trace>
<stack_trace>at member.py:439</stack_trace>
<context>
<user_id>70c0c786-0e43-42b2-8ec7-e975f39a71f7</user_id>
<request_id>95ca942a-a33a-413f-b1a1-a21e12fa0f30</request_id>
</context>
</error_log>


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Output error log as XML.

[Assistant]
Approach:
1. Task: Create error log in XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<error_log>
<error_id>e78ff1cc-d984-499d-9fcf-126ccada9355</error_id>
<timestamp>2026-01-07T13:51:26.607679</timestamp>
<level>WARNING</level>
<service>generationService</service>
<message>Bag difference growth have leader yeah him.</message>
<stack_trace>at teach.py:30</stack_trace>
<stack_trace>at poor.py:409</stack_trace>
<context>
<user_id>e8daac99-0bad-4fb0-ae26-78525528db85</user_id>
<request_id>a6fe9e0f-a1a6-42b2-ab77-98b8bb662a52</request_id>
</context>
</error_log>


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User]
Convert the following XML to YAML:
<?xml version="1.0" encoding="UTF-8"?>
<transaction_record>
<txn_id>TXN-79156891</txn_id>
<customer_id>5450ecc9-30b8-498f-a527-04ec3de16dd8</customer_id>
<timestamp>2026-01-07T13:51:26.258900</timestamp>
<items>
<sku>SKU-81880</sku>
<qty>4</qty>
<price>401.7</price>
</items>
<items>
<sku>SKU-99718</sku>
<qty>5</qty>
<price>334.37</price>
</items>
<items>
<sku>SKU-65882</sku>
<qty>3</qty>
<price>292.8</price>
</items>
<items>
<sku>SKU-88201</sku>
<qty>2</qty>
<price>114.91</price>
</items>
<subtotal>258.62</subtotal>
<tax>22.79</tax>
<total>1713.37</total>
<payment_method>paypal</payment_method>
</transaction_record>

[Assistant]
Approach:
1. Task: Convert transaction record from XML to YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
txn_id: TXN-79156891
customer_id: 5450ecc9-30b8-498f-a527-04ec3de16dd8
timestamp: '2026-01-07T13:51:26.258900'
items:
- sku: SKU-81880
  qty: 4
  price: 401.7
- sku: SKU-99718
  qty: 5
  price: 334.37
- sku: SKU-65882
  qty: 3
  price: 292.8
- sku: SKU-88201
  qty: 2
  price: 114.91
subtotal: 258.62
tax: 22.79
total: 1713.37
payment_method: paypal


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Generate news article data in XML format.

[Assistant]
Approach:
1. Task: Create news article in XML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<news_article>
<article_id>ART-1823437</article_id>
<headline>Integrated content-based throughput</headline>
<author>Anna Ortiz</author>
<published>2026-01-05T06:32:59.977242</published>

Politics

<content>Success off activity him process hot whose pay. Most probably including at arm avoid media finally.
Film traditional together still member. Culture let serve home.
Choose free minute in rich after image. Family meet health offer total leave media. Education enter against foreign.</content>
<tags>base</tags>
<tags>fight</tags>
<tags>test</tags>
<views>75522</views>
<shares>3070</shares>
</news_article>


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Output product listing as YAML.

[Assistant]
Approach:
1. Task: Create product listing in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
product_id: PROD-842353
name: Synergistic bottom-line conglomeration
category: books
price: 609.49
currency: USD
stock: 399
is_available: true
rating: 1.05
reviews: 570
specs:
  weight_kg: 25.04
  dimensions: 30x19x82cm


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Convert the following XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<financial_report>
<company>Rodriguez, Sanchez and French</company>
<ticker>TOPI</ticker>
<quarter>Q3 2022</quarter>
<revenue>30596216</revenue>
<expenses>79425858</expenses>
<net_income>10739155</net_income>
<eps>9.27</eps>
<metrics>
<profit_margin>21.79</profit_margin>
<roe>22.32</roe>
</metrics>
</financial_report>

[Assistant]
Approach:
1. Task: Convert financial report from XML to JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "company": "Rodriguez, Sanchez and French",
  "ticker": "TOPI",
  "quarter": "Q3 2022",
  "revenue": 30596216,
  "expenses": 79425858,
  "net_income": 10739155,
  "eps": 9.27,
  "metrics": {
    "profit_margin": 21.79,
    "roe": 22.32
  }
}


[System] You are an expert in CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User] Create a CSV representation of lab result.

[Assistant]
Approach:
1. Task: Create lab result in CSV
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
order_date,panel,patient_mrn,result_id,results
2026-01-01,CBC,MRN-739958,LAB-3941350,"[{""test"": ""Glucose"", ""value"": 134.1, ""unit"": ""mg/dL"", ""flag"": ""high""}, {""test"": ""WBC"", ""value"": 188."
2026-01-03,CMP,MRN-842970,LAB-1750909,"[{""test"": ""WBC"", ""value"": 162.4, ""unit"": ""mg/dL"", ""flag"": ""low""}, {""test"": ""WBC"", ""value"": 66.6, ""un"
2026-01-05,Lipid,MRN-377003,LAB-4868937,"[{""test"": ""Glucose"", ""value"": 93.8, ""unit"": ""mg/dL"", ""flag"": ""low""}, {""test"": ""WBC"", ""value"": 50.6, "
2026-01-03,CMP,MRN-103937,LAB-1964785,"[{""test"": ""Hemoglobin"", ""value"": 92.9, ""unit"": ""mg/dL"", ""flag"": ""low""}, {""test"": ""Hemoglobin"", ""valu"


[System] You are an expert in CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User] Generate customer review data in CSV format.

[Assistant]
Approach:
1. Task: Create customer review in CSV
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
abstract,authors,citations,date,funding_source,keywords,paper_id,publication_doi,publication_venue,publication_year,rating,title,verified_purchase
,,,2026-01-05,,,,,,,1,,True
Blood west why smile. Direction similar too. Finally place politics learn resource edge page.,"[{""name"": ""Jeffrey Bailey"", ""affiliation"": ""Johnson-Morris"", ""email"": ""leejohn@example.org""}, {""name",105,,Barnett LLC,"[""treatment"", ""structure"", ""hour"", ""guy""]",arXiv:2021.42878,10.6425/tcqO,Science,2024,,Adaptive high-level encryption,
Reason exactly best thing would even. Threat back front radio land. After I behind Republican wrong.,"[{""name"": ""Sherry Cooper"", ""affiliation"": ""Rose Inc"", ""email"": ""joeferrell@example.com""}, {""name"": """,37,,None,"[""that"", ""and"", ""effort""]",arXiv:2023.35516,10.8771/mFfB,Nature,2022,,Innovative asymmetric neural-net,
"May media drug play different. Trade program within hear.
Former every price push south kind. Focus ","[{""name"": ""Robert Campbell"", ""affiliation"": ""Howard, Roman and Neal"", ""email"": ""proberts@example.com",122,,None,"[""city"", ""look"", ""away"", ""between""]",arXiv:2021.24937,10.8988/RkLa,Cell,2020,,Customer-focused needs-based collaboration,
Growth authority often range than behavior. Child even condition. Put memory threat play.,"[{""name"": ""Dr. Tina Thomas DVM"", ""affiliation"": ""Daniels-Bird"", ""email"": ""aallen@example.org""}, {""na",106,,Smith and Sons,"[""might"", ""maintain"", ""wrong""]",arXiv:2020.55753,10.6312/QznF,Cell,2020,,Quality-focused modular migration,


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Create a JSON representation of api specification.

[Assistant]
Approach:
1. Task: Create api specification in JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "openapi": "3.0.0",
  "info": {
    "title": "Organic analyzing strategy API",
    "version": "1.0.0"
  },
  "paths": {
    "/similar": {
      "get": {
        "summary": "Natural see lot recently.",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param1",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "400": {
            "description": "Bad Request"
          }
        }
      }
    }
  }
}


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Generate contract data in XML format.

[Assistant]
Approach:
1. Task: Create contract in XML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<contract>
<effective_date>2026-01-02</effective_date>
<term_months>54</term_months>
<status>pending</status>
</contract>


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Convert the following TOML to JSON:
note_id = "CN-4441974"
encounter_date = "2026-01-02"
chief_complaint = "headache"
[[assessment]]
problem = "diabetes"
icd10 = "J60"

[[assessment]]
problem = "hypertension"
icd10 = "I21"

[[assessment]]
problem = "diabetes"
icd10 = "K16"

[patient]
mrn = "MRN-925906"
age = 60
gender = "F"

[vitals]
bp = "154/95"
hr = 108
temp_f = 97.8

[Assistant]
Approach:
1. Task: Convert clinical note from TOML to JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "note_id": "CN-4441974",
  "patient": {
    "mrn": "MRN-925906",
    "age": 60,
    "gender": "F"
  },
  "encounter_date": "2026-01-02",
  "chief_complaint": "headache",
  "vitals": {
    "bp": "154/95",
    "hr": 108,
    "temp_f": 97.8
  },
  "assessment": [
    {
      "problem": "diabetes",
      "icd10": "J60"
    },
    {
      "problem": "hypertension",
      "icd10": "I21"
    },
    {
      "problem": "diabetes",
      "icd10": "K16"
    }
  ]
}


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Create a TOML representation of social media post.

[Assistant]
Approach:
1. Task: Create social media post in TOML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
post_id = "POST-78635818"
platform = "facebook"
user_id = "a739269a-9642-463c-9c88-cb97c795c8d8"
username = "kevin10"
content = "Specific other kind choice. Condition main resource finish north reach with break. Area strategy huge edge."
timestamp = "2026-01-07T13:51:25.965362"
hashtags = [ "#model", "#firm",]

[engagement]
likes = 3831
shares = 379
comments = 204


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User]
Reformat the data below from CSV to YAML:
abstract,authors,citations,funding_source,keywords,paper_id,publication_doi,publication_venue,publication_year,title
Body exactly last operation trouble. Financial whole check management chance eat us. Likely wrong ex,"[{""name"": ""David Cohen"", ""affiliation"": ""Lopez and Sons"", ""email"": ""mark98@example.com""}, {""name"": """,174,None,"[""rather"", ""hair"", ""attorney"", ""chance"", ""bring""]",arXiv:2024.66116,10.7468/EeJM,Cell,2020,Quality-focused grid-enabled service-desk
"Especially room explain kid would listen subject.
Write man him six enjoy season rest. Few less minu","[{""name"": ""Ashley Howard"", ""affiliation"": ""Jackson-Moore"", ""email"": ""turnercharles@example.com""}, {""",44,"Mercer, Ruiz and Wright","[""measure"", ""reflect"", ""discover"", ""future""]",arXiv:2024.31155,10.1812/jZeP,Science,2021,Sharable real-time system engine
Reflect end note movement civil successful surface once. Experience life teacher between long traini,"[{""name"": ""Alejandro Young"", ""affiliation"": ""Castillo-Cox"", ""email"": ""howardjessica@example.com""}, {",248,"Rivera, James and Jones","[""spend"", ""them"", ""late"", ""risk""]",arXiv:2022.69649,10.2689/ADsC,Nature,2020,Realigned 6thgeneration archive
Next avoid lot while development feeling result. Friend boy full sea which can performance.,"[{""name"": ""Laura Aguilar"", ""affiliation"": ""Wade PLC"", ""email"": ""urobinson@example.org""}, {""name"": ""J",171,Bell LLC,"[""there"", ""ability"", ""theory"", ""list"", ""maintain""]",arXiv:2020.56309,10.9367/bePU,Science,2022,Cross-platform zero administration encoding

[Assistant]
Approach:
1. Task: Convert research paper from CSV to YAML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
paper_id: arXiv:2024.66116
title: Quality-focused grid-enabled service-desk
authors:
- name: David Cohen
  affiliation: Lopez and Sons
  email: mark98@example.com
- name: Ashley Spencer
  affiliation: Hood Ltd
  email: pbailey@example.org
- name: Robin Lynch
  affiliation: Camacho, Nelson and Perez
  email: samantha44@example.net
abstract: Body exactly last operation trouble. Financial whole check management chance
  eat us. Likely wrong explain by would born only.
keywords:
- rather
- hair
- attorney
- chance
- bring
publication:
  venue: Cell
  year: 2020
  doi: 10.7468/EeJM
citations: 174
funding_source: null


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Generate prescription data in XML format.

[Assistant]
Approach:
1. Task: Create prescription in XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<prescription>
<rx_id>RX-1565761</rx_id>
<patient_mrn>MRN-226686</patient_mrn>
<prescriber>Regina Martin</prescriber>
<date>2026-01-03</date>
<medications>
<name>Metformin</name>
<dosage>20mg</dosage>
<frequency>TID</frequency>
<quantity>60</quantity>
</medications>
<medications>
<name>Atorvastatin</name>
<dosage>20mg</dosage>
<frequency>BID</frequency>
<quantity>90</quantity>
</medications>
</prescription>


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Output contract as TOML.

[Assistant]
Approach:
1. Task: Create contract in TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
contract_id = "CTR-760727"
effective_date = "2026-01-04"
term_months = 15
value = 170545
status = "active"
[[parties]]
name = "King, Moore and Vazquez"
role = "party_a"

[[parties]]
name = "Brown, Henry and Craig"
role = "party_b"


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Create a JSON representation of prescription.

[Assistant]
Approach:
1. Task: Create prescription in JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "rx_id": "RX-3022361",
  "patient_mrn": "MRN-726192",
  "prescriber": "Jessica Warner",
  "date": "2026-01-02",
  "medications": [
    {
      "name": "Atorvastatin",
      "dosage": "20mg",
      "frequency": "BID",
      "quantity": 90
    },
    {
      "name": "Atorvastatin",
      "dosage": "20mg",
      "frequency": "daily",
      "quantity": 30
    }
  ]
}


[System] You are an expert in CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User] Generate prescription data in CSV format.

[Assistant]
Approach:
1. Task: Create prescription in CSV
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
date,medications,patient_mrn,prescriber,rx_id
2026-01-04,"[{""name"": ""Atorvastatin"", ""dosage"": ""10mg"", ""frequency"": ""BID"", ""quantity"": 60}, {""name"": ""Metformin",MRN-644382,Chelsea Henson,RX-3684509
2026-01-05,"[{""name"": ""Atorvastatin"", ""dosage"": ""5mg"", ""frequency"": ""BID"", ""quantity"": 30}, {""name"": ""Metformin""",MRN-621492,Adrian Gardner,RX-3538435
2026-01-02,"[{""name"": ""Metformin"", ""dosage"": ""20mg"", ""frequency"": ""daily"", ""quantity"": 60}, {""name"": ""Atorvastat",MRN-799462,James Hurst,RX-5141910


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Create a JSON representation of experiment result.

[Assistant]
Approach:
1. Task: Create experiment result in JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "experiment_id": "EXP-168652",
  "title": "Effect of green on from",
  "methodology": {
    "design": "cohort",
    "sample_size": 797,
    "duration_days": 186
  },
  "results": [
    {
      "metric": "safety",
      "baseline": 24.16,
      "endpoint": 44.82,
      "p_value": 0.0559
    },
    {
      "metric": "efficacy",
      "baseline": 16.54,
      "endpoint": 28.48,
      "p_value": 0.2997
    }
  ],
  "conclusion": "non-significant"
}


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Output course syllabus as YAML.

[Assistant]
Approach:
1. Task: Create course syllabus in YAML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
course_id: CRS-5704
code: BXM405
title: Profit-focused responsive throughput 101
instructor: Emily Bryan
credits: 3
term: Spring 2021
schedule:
- week: 1
  topic: southern
- week: 2
  topic: raise
- week: 3
  topic: across
- week: 4
  topic: teacher
- week: 5
  topic: service
- week: 6
  topic: body
- week: 7
  topic: practice
- week: 8
  topic: authority
- week: 9
  topic: throw
- week: 10
  topic: second
- week: 11
  topic: authority
grading:
  homework: 30
  midterm: 30
  final: 40


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Create a YAML representation of social media post.

[Assistant]
Approach:
1. Task: Create social media post in YAML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
post_id: POST-39452168
platform: twitter
user_id: ca270bc8-d443-4389-880a-6fc8dd3e9fa5
username: robertowens
content: 'With writer dinner any kitchen add well. Floor image speech charge rate
  manager vote. Worry involve benefit there nothing rock.

  Factor even apply how three box light.'
timestamp: '2026-01-07T13:51:25.425211'
hashtags:
- '#training'
- '#Democrat'
- '#pattern'
- '#discover'
engagement:
  likes: 5319
  shares: 573
  comments: 283


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Output student assessment as YAML.

[Assistant]
Approach:
1. Task: Create student assessment in YAML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
date: '2026-01-01'
total_score: 85
student_id: STU-920172


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Transform this JSON data into XML format:
{
  "note_id": "CN-8348908",
  "patient": {
    "mrn": "MRN-109351",
    "age": 84,
    "gender": "F"
  },
  "encounter_date": "2026-01-06",
  "chief_complaint": "fever",
  "vitals": {
    "bp": "157/81",
    "hr": 101,
    "temp_f": 97.1
  },
  "assessment": [
    {
      "problem": "diabetes",
      "icd10": "J60"
    },
    {
      "problem": "diabetes",
      "icd10": "K69"
    },
    {
      "problem": "hypertension",
      "icd10": "J80"
    }
  ]
}

[Assistant]
Approach:
1. Task: Convert clinical note from JSON to XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<clinical_note>
<note_id>CN-8348908</note_id>
<patient>
<mrn>MRN-109351</mrn>
<age>84</age>
<gender>F</gender>
</patient>
<encounter_date>2026-01-06</encounter_date>
<chief_complaint>fever</chief_complaint>
<vitals>
<bp>157/81</bp>

101

<temp_f>97.1</temp_f>
</vitals>
<assessment>
<problem>diabetes</problem>
<icd10>J60</icd10>
</assessment>
<assessment>
<problem>diabetes</problem>
<icd10>K69</icd10>
</assessment>
<assessment>
<problem>hypertension</problem>
<icd10>J80</icd10>
</assessment>
</clinical_note>


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Produce a JSON document for a error log.

[Assistant]
Approach:
1. Task: Create error log in JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "error_id": "385c5830-2f90-42e3-ae2f-53faace4448a",
  "timestamp": "2026-01-07T13:51:26.292009",
  "level": "CRITICAL",
  "service": "almostService",
  "message": "People party agreement mother that investment approach.",
  "stack_trace": [
    "at fight.py:491",
    "at city.py:369",
    "at statement.py:378"
  ],
  "context": {
    "user_id": "264dfcb4-39aa-45b1-999f-d115795470f4",
    "request_id": "4c579c9e-314d-44f6-9e5a-9c3c95ea5e82"
  }
}


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Create a XML representation of transaction record.

[Assistant]
Approach:
1. Task: Create transaction record in XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<transaction_record>
<txn_id>TXN-15638187</txn_id>
<customer_id>242fd3fb-d1fb-4e06-b998-4ca6fd25c6b7</customer_id>
<timestamp>2026-01-07T13:51:27.047573</timestamp>
<items>
<sku>SKU-16950</sku>
<qty>3</qty>
<price>120.22</price>
</items>
<items>
<sku>SKU-76122</sku>
<qty>1</qty>
<price>377.76</price>
</items>
<items>
<sku>SKU-41194</sku>
<qty>3</qty>
<price>254.19</price>
</items>
<items>
<sku>SKU-75246</sku>
<qty>2</qty>
<price>418.85</price>
</items>
<subtotal>1338.06</subtotal>
<tax>172.92</tax>
<total>2048.81</total>
<payment_method>apple_pay</payment_method>
</transaction_record>


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Reformat the data below from JSON to XML:
{
  "experiment_id": "EXP-942720",
  "title": "Effect of edge on when",
  "methodology": {
    "design": "cohort",
    "sample_size": 645,
    "duration_days": 54
  },
  "results": [
    {
      "metric": "safety",
      "baseline": 79.64,
      "endpoint": 26.86,
      "p_value": 0.2863
    },
    {
      "metric": "safety",
      "baseline": 55.57,
      "endpoint": 59.78,
      "p_value": 0.1705
    },
    {
      "metric": "efficacy",
      "baseline": 30.69,
      "endpoint": 27.4,
      "p_value": 0.1116
    },
    {
      "metric": "efficacy",
      "baseline": 32.07,
      "endpoint": 35.79,
      "p_value": 0.1946
    }
  ],
  "conclusion": "non-significant"
}

[Assistant]
Approach:
1. Task: Convert experiment result from JSON to XML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<experiment_result>
<experiment_id>EXP-942720</experiment_id>
<title>Effect of edge on when</title>
<methodology>
<design>cohort</design>
<sample_size>645</sample_size>
<duration_days>54</duration_days>
</methodology>
<results>
<metric>safety</metric>
<baseline>79.64</baseline>
<endpoint>26.86</endpoint>
<p_value>0.2863</p_value>
</results>
<results>
<metric>safety</metric>
<baseline>55.57</baseline>
<endpoint>59.78</endpoint>
<p_value>0.1705</p_value>
</results>
<results>
<metric>efficacy</metric>
<baseline>30.69</baseline>
<endpoint>27.4</endpoint>
<p_value>0.1116</p_value>
</results>
<results>
<metric>efficacy</metric>
<baseline>32.07</baseline>
<endpoint>35.79</endpoint>
<p_value>0.1946</p_value>
</results>
<conclusion>non-significant</conclusion>
</experiment_result>


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User]
Reformat the data below from XML to TOML:
<?xml version="1.0" encoding="UTF-8"?>
<news_article>
<article_id>ART-2388619</article_id>
<headline>Switchable tertiary collaboration</headline>
<author>Paula Martinez</author>
<published>2026-01-07T02:15:35.006278</published>

Politics

<content>Choice list strong let reason. Education class ok front impact successful. Huge professor happen data image stop. Notice movie reduce everyone among ask already company.
Door president he always capital.
Black democratic heart read name. Professional billion prepare executive meeting sport.</content>
<tags>hair</tags>
<tags>democratic</tags>
<tags>financial</tags>
<tags>huge</tags>
<tags>available</tags>
<views>74003</views>
<shares>1794</shares>
</news_article>

[Assistant]
Approach:
1. Task: Convert news article from XML to TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
article_id = "ART-2388619"
headline = "Switchable tertiary collaboration"
author = "Paula Martinez"
published = "2026-01-07T02:15:35.006278"
section = "Politics"
content = "Choice list strong let reason. Education class ok front impact successful. Huge professor happen data image stop. Notice movie reduce everyone among ask already company.\nDoor president he always capital.\nBlack democratic heart read name. Professional billion prepare executive meeting sport."
tags = [ "hair", "democratic", "financial", "huge", "available",]
views = 74003
shares = 1794


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Reformat the data below from XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<product_listing>
<product_id>PROD-842353</product_id>
<name>Synergistic bottom-line conglomeration</name>
<category>books</category>
<price>609.49</price>
<currency>USD</currency>
<stock>399</stock>
<is_available>True</is_available>
<rating>1.05</rating>
<reviews>570</reviews>
<specs>
<weight_kg>25.04</weight_kg>
<dimensions>30x19x82cm</dimensions>
</specs>
</product_listing>

[Assistant]
Approach:
1. Task: Convert product listing from XML to JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "product_id": "PROD-842353",
  "name": "Synergistic bottom-line conglomeration",
  "category": "books",
  "price": 609.49,
  "currency": "USD",
  "stock": 399,
  "is_available": true,
  "rating": 1.05,
  "reviews": 570,
  "specs": {
    "weight_kg": 25.04,
    "dimensions": "30x19x82cm"
  }
}


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Create a TOML representation of lab result.

[Assistant]
Approach:
1. Task: Create lab result in TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
result_id = "LAB-1056021"
patient_mrn = "MRN-977601"
order_date = "2026-01-02"
panel = "CMP"
[[results]]
test = "WBC"
value = 124.0
unit = "g/dL"
flag = "high"

[[results]]
test = "Hemoglobin"
value = 114.5
unit = "g/dL"
flag = "high"

[[results]]
test = "WBC"
value = 70.6
unit = "g/dL"
flag = "high"

[[results]]
test = "Glucose"
value = 119.6
unit = "mg/dL"
flag = "high"

[[results]]
test = "Hemoglobin"
value = 171.0
unit = "mg/dL"
flag = "normal"


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Generate lab result data in YAML format.

[Assistant]
Approach:
1. Task: Create lab result in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
result_id: LAB-1056021
patient_mrn: MRN-977601
order_date: '2026-01-02'
panel: CMP
results:
- test: WBC
  value: 124.0
  unit: g/dL
  flag: high
- test: Hemoglobin
  value: 114.5
  unit: g/dL
  flag: high
- test: WBC
  value: 70.6
  unit: g/dL
  flag: high
- test: Glucose
  value: 119.6
  unit: mg/dL
  flag: high
- test: Hemoglobin
  value: 171.0
  unit: mg/dL
  flag: normal


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Produce a JSON document for a error log.

[Assistant]
Approach:
1. Task: Create error log in JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "error_id": "0ea30289-ce13-425f-910d-da970e03d0fa",
  "timestamp": "2026-01-07T13:51:25.772316",
  "level": "WARNING",
  "service": "theService",
  "message": "Country specific information follow daughter dark.",
  "stack_trace": [
    "at enjoy.py:340",
    "at current.py:45",
    "at hand.py:478"
  ],
  "context": {
    "user_id": "91a6b883-2593-4790-8a1e-5acd27aad31b",
    "request_id": "1b22b6e6-0879-45de-9655-0fac03142f9b"
  }
}


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Create a YAML representation of news article.

[Assistant]
Approach:
1. Task: Create news article in YAML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
article_id: ART-1748125
headline: Customizable leadingedge structure
author: Sandra Bennett
published: '2026-01-03T22:52:42.741679'
section: Sports
content: 'Majority meeting sound have week public pass. Major food relate spend seven.

  Indeed run fund.

  Federal right stuff difficult alone miss. Stuff rest model job deep knowledge pull
  force. Too follow face husband every. Hit important agreement rather card west want.'
tags:
- subject
- attention
- class
- campaign
- second
views: 63827
shares: 2505


[System] You are an expert in CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User] Create a CSV representation of transaction record.

[Assistant]
Approach:
1. Task: Create transaction record in CSV
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
abstract,authors,citations,funding_source,items,keywords,paper_id,payment_method,publication_doi,publication_venue,publication_year,title,total
,,,,"[{""sku"": ""SKU-89816"", ""qty"": 1, ""price"": 437.44}, {""sku"": ""SKU-65650"", ""qty"": 1, ""price"": 455.72}, {",,,apple_pay,,,,,1900.4
Believe deep rather role term as wall radio. Daughter teacher throw tree. Clear phone one media form,"[{""name"": ""Mary Johnson"", ""affiliation"": ""Mclean Inc"", ""email"": ""johnrichmond@example.net""}, {""name""",136,None,,"[""travel"", ""place"", ""personal"", ""plant"", ""common""]",arXiv:2020.71653,,10.4721/SvCN,Cell,2022,Reduced motivating migration,
"Need another board perform east after. Which ever bill exist budget suggest.
Forget attention yeah a","[{""name"": ""Alex Espinoza"", ""affiliation"": ""Estes Group"", ""email"": ""rodney19@example.net""}, {""name"": ",127,Bradley Group,,"[""administration"", ""seek"", ""admit"", ""pretty""]",arXiv:2024.77919,,10.4459/LaOE,Nature,2021,Decentralized motivating data-warehouse,


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Produce a JSON document for a social media post.

[Assistant]
Approach:
1. Task: Create social media post in JSON
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "engagement": {
    "likes": 6676,
    "shares": 196,
    "comments": 84
  },
  "username": "nicholasalvarez",
  "post_id": "POST-61351209",
  "hashtags": [
    "#pretty",
    "#admit",
    "#available"
  ],
  "user_id": "5e546096-8461-4a3f-84e6-813e9e89a39c"
}


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Reformat the data below from XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<lab_result>
<result_id>LAB-4303735</result_id>
<patient_mrn>MRN-230243</patient_mrn>
<order_date>2026-01-02</order_date>
<panel>Lipid</panel>
<results>
<test>WBC</test>
<value>142.8</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>100.6</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<results>
<test>WBC</test>
<value>148.7</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>95.1</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
</lab_result>

[Assistant]
Approach:
1. Task: Convert lab result from XML to JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "result_id": "LAB-4303735",
  "patient_mrn": "MRN-230243",
  "order_date": "2026-01-02",
  "panel": "Lipid",
  "results": [
    {
      "test": "WBC",
      "value": 142.8,
      "unit": "g/dL",
      "flag": "high"
    },
    {
      "test": "Hemoglobin",
      "value": 100.6,
      "unit": "mg/dL",
      "flag": "high"
    },
    {
      "test": "WBC",
      "value": 148.7,
      "unit": "mg/dL",
      "flag": "high"
    },
    {
      "test": "Hemoglobin",
      "value": 95.1,
      "unit": "g/dL",
      "flag": "normal"
    }
  ]
}


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Produce a YAML document for a error log.

[Assistant]
Approach:
1. Task: Create error log in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
error_id: bcec7cd1-70fc-42ba-97dd-2c6e2159fc7d
timestamp: '2026-01-07T13:51:27.288021'
level: CRITICAL
service: reasonService
message: Recent security meet three Congress find seat.
stack_trace:
- at current.py:236
- at your.py:146
- at without.py:194
context:
  user_id: fc408ea4-9df4-4d81-87da-9d63d4bc8744
  request_id: d8c95012-b6bb-42d7-8cb4-e57e77353a5b


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Produce a TOML document for a financial report.

[Assistant]
Approach:
1. Task: Create financial report in TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
company = "Rodriguez, Curry and Yates"
ticker = "KWAZ"
quarter = "Q1 2020"
revenue = 4863925
expenses = 55129980
net_income = 12179320
eps = 7.94

[metrics]
profit_margin = 29.26
roe = 9.63


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Produce a YAML document for a research paper.

[Assistant]
Approach:
1. Task: Create research paper in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
paper_id: arXiv:2020.86983
title: Reverse-engineered national Graphical User Interface
authors:
- name: Wesley Murphy
  affiliation: Nunez, Meyer and Snow
  email: nina74@example.net
- name: Richard Morgan
  affiliation: Hall-Jones
  email: justin85@example.org
- name: Anthony Johnson
  affiliation: Kelley-Murphy
  email: srichmond@example.com
abstract: Sure finally site who truth. Close stop when low. Check news mother democratic.
keywords:
- old
- indeed
- general
- claim
- every
publication:
  venue: Nature
  year: 2024
  doi: 10.2539/QMDE
citations: 221
funding_source: null


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Transform this XML data into JSON format:
<?xml version="1.0" encoding="UTF-8"?>
<transaction_record>
<txn_id>TXN-15638187</txn_id>
<customer_id>242fd3fb-d1fb-4e06-b998-4ca6fd25c6b7</customer_id>
<timestamp>2026-01-07T13:51:27.047573</timestamp>
<items>
<sku>SKU-16950</sku>
<qty>3</qty>
<price>120.22</price>
</items>
<items>
<sku>SKU-76122</sku>
<qty>1</qty>
<price>377.76</price>
</items>
<items>
<sku>SKU-41194</sku>
<qty>3</qty>
<price>254.19</price>
</items>
<items>
<sku>SKU-75246</sku>
<qty>2</qty>
<price>418.85</price>
</items>
<subtotal>1338.06</subtotal>
<tax>172.92</tax>
<total>2048.81</total>
<payment_method>apple_pay</payment_method>
</transaction_record>

[Assistant]
Approach:
1. Task: Convert transaction record from XML to JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "txn_id": "TXN-15638187",
  "customer_id": "242fd3fb-d1fb-4e06-b998-4ca6fd25c6b7",
  "timestamp": "2026-01-07T13:51:27.047573",
  "items": [
    {
      "sku": "SKU-16950",
      "qty": 3,
      "price": 120.22
    },
    {
      "sku": "SKU-76122",
      "qty": 1,
      "price": 377.76
    },
    {
      "sku": "SKU-41194",
      "qty": 3,
      "price": 254.19
    },
    {
      "sku": "SKU-75246",
      "qty": 2,
      "price": 418.85
    }
  ],
  "subtotal": 1338.06,
  "tax": 172.92,
  "total": 2048.81,
  "payment_method": "apple_pay"
}


[System] You are an expert in CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User] Output error log as CSV.

[Assistant]
Approach:
1. Task: Create error log in CSV
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
abstract,authors,citations,context_request_id,context_user_id,error_id,funding_source,keywords,level,message,paper_id,publication_doi,publication_venue,publication_year,service,stack_trace,timestamp,title
,,,a6fe9e0f-a1a6-42b2-ab77-98b8bb662a52,e8daac99-0bad-4fb0-ae26-78525528db85,e78ff1cc-d984-499d-9fcf-126ccada9355,,,WARNING,Bag difference growth have leader yeah him.,,,,,generationService,"[""at teach.py:30"", ""at poor.py:409""]",2026-01-07T13:51:26.607679,
Newspaper might position sport box Mrs. Fund end theory member place image. Many analysis from use p,"[{""name"": ""Chase Le"", ""affiliation"": ""Gregory LLC"", ""email"": ""emily06@example.com""}, {""name"": ""Prisc",324,,,,Johnson-Wilson,"[""today"", ""miss"", ""court""]",,,arXiv:2021.55593,10.4708/SHLa,Science,2020,,,,Diverse systemic benchmark
Network again need weight account sound measure machine. Final finally few set it everybody another.,"[{""name"": ""Dennis Anderson"", ""affiliation"": ""Robinson, Hines and Turner"", ""email"": ""jason15@example.",319,,,,None,"[""night"", ""but"", ""face""]",,,arXiv:2023.14192,10.9079/ySGH,Nature,2024,,,,De-engineered foreground standardization
"Center language bring once purpose itself. Your compare maybe sister party decide.
Experience remain","[{""name"": ""Janet Gardner"", ""affiliation"": ""Mooney, Stuart and Dunn"", ""email"": ""christopherbutler@exa",6,,,,Anderson-Nelson,"[""forward"", ""read"", ""institution""]",,,arXiv:2022.74141,10.5780/NBeF,Nature,2024,,,,Digitized dedicated middleware


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Create a YAML representation of customer review.

[Assistant]
Approach:
1. Task: Create customer review in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
review_id: REV-9701957
product_id: PROD-683988
user_id: 4d7f9754-e296-4a29-967f-5c8de166a17c
rating: 5
title: Teach look history according want manager perform.
content: Admit large politics decide might Mr their. Hundred play I deal but player
  leader old. Then similar drug coach.
date: '2026-01-03'
verified_purchase: false
helpful_votes: 36


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Generate product listing data in YAML format.

[Assistant]
Approach:
1. Task: Create product listing in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
product_id: PROD-361703
name: Grass-roots methodical neural-net
category: books
price: 156.58
currency: USD
stock: 3
is_available: true
rating: 3.73
reviews: 915
specs:
  weight_kg: 25.41
  dimensions: 90x40x81cm


[System] You are an expert in CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User] Generate clinical note data in CSV format.

[Assistant]
Approach:
1. Task: Create clinical note in CSV
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
assessment,chief_complaint,encounter_date,note_id,patient_age,patient_gender,patient_mrn,vitals_bp,vitals_hr,vitals_temp_f
"[{""problem"": ""diabetes"", ""icd10"": ""J94""}]",headache,2026-01-04,CN-3766387,48,F,MRN-378219,145/101,106,101.7
"[{""problem"": ""diabetes"", ""icd10"": ""J58""}]",headache,2026-01-01,CN-4336964,19,F,MRN-926074,105/105,69,101.1
"[{""problem"": ""hypertension"", ""icd10"": ""J13""}]",headache,2026-01-03,CN-6888054,25,M,MRN-148741,157/77,60,102.3


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Create a YAML representation of lab result.

[Assistant]
Approach:
1. Task: Create lab result in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
result_id: LAB-1812594
patient_mrn: MRN-755137
order_date: '2026-01-06'
panel: CMP
results:
- test: Glucose
  value: 131.9
  unit: mg/dL
  flag: normal
- test: Glucose
  value: 183.7
  unit: g/dL
  flag: normal
- test: WBC
  value: 130.1
  unit: mg/dL
  flag: high
- test: Hemoglobin
  value: 178.3
  unit: g/dL
  flag: low


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Transform this XML data into JSON format:
<?xml version="1.0" encoding="UTF-8"?>
<prescription>
<rx_id>RX-9719971</rx_id>
<patient_mrn>MRN-909141</patient_mrn>
<prescriber>Heather Stephens</prescriber>
<date>2026-01-05</date>
<medications>
<name>Atorvastatin</name>
<dosage>20mg</dosage>
<frequency>daily</frequency>
<quantity>30</quantity>
</medications>
</prescription>

[Assistant]
Approach:
1. Task: Convert prescription from XML to JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "rx_id": "RX-9719971",
  "patient_mrn": "MRN-909141",
  "prescriber": "Heather Stephens",
  "date": "2026-01-05",
  "medications": [
    {
      "name": "Atorvastatin",
      "dosage": "20mg",
      "frequency": "daily",
      "quantity": 30
    }
  ]
}


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Generate prescription data in YAML format.

[Assistant]
Approach:
1. Task: Create prescription in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
rx_id: RX-5855932
patient_mrn: MRN-921122
prescriber: Spencer Smith
date: '2026-01-05'
medications:
- name: Metformin
  dosage: 20mg
  frequency: daily
  quantity: 30


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Output error log as XML.

[Assistant]
Approach:
1. Task: Create error log in XML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<error_log>
<service>approachService</service>
<context>
<user_id>fe05329e-c851-4258-9b5b-a5822613303b</user_id>
<request_id>7878bb2d-6319-4d97-8924-bd0357b0788d</request_id>
</context>
<stack_trace>at level.py:451</stack_trace>
<stack_trace>at science.py:176</stack_trace>
<stack_trace>at art.py:498</stack_trace>
<stack_trace>at success.py:287</stack_trace>
<level>WARNING</level>
<error_id>d3f1548e-3d70-475b-a25a-df504add1be1</error_id>
</error_log>


[System] You are an expert in CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User] Output financial report as CSV.

[Assistant]
Approach:
1. Task: Create financial report in CSV
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
abstract,authors,citations,expenses,funding_source,keywords,metrics_profit_margin,metrics_roe,net_income,paper_id,publication_doi,publication_venue,publication_year,revenue,title
,,,6835352,,,9.67,5.79,16280737,,,,,93235840,
"Mind sometimes gun short. Describe southern set describe fine wall stop.
Rate their population. Clai","[{""name"": ""Timothy Landry"", ""affiliation"": ""Moss and Sons"", ""email"": ""stacy67@example.org""}, {""name""",273,,Williams LLC,"[""soon"", ""arrive"", ""note"", ""table"", ""could""]",,,,arXiv:2024.70256,10.8226/qyAm,Cell,2020,,Compatible disintermediate knowledge user
Everything industry account image argue. Low low Mrs watch skill. Service could organization feel sk,"[{""name"": ""Alexander Hansen"", ""affiliation"": ""Morgan, Tyler and Vargas"", ""email"": ""laura70@example.o",198,,Wilson Group,"[""response"", ""grow"", ""center"", ""every""]",,,,arXiv:2020.67002,10.2553/fYWh,Nature,2022,,Cross-platform 4thgeneration encoding
Floor wish cut reason major. Hair responsibility new not. Much enter whose daughter others already. ,"[{""name"": ""Richard Glass"", ""affiliation"": ""Stevens-Johnson"", ""email"": ""ryan14@example.org""}, {""name""",50,,"Morgan, Marshall and Landry","[""beautiful"", ""individual"", ""tell"", ""practice""]",,,,arXiv:2020.54857,10.2762/ACvj,Science,2023,,Self-enabling logistical core


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Create a TOML representation of error log.

[Assistant]
Approach:
1. Task: Create error log in TOML
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
service = "approachService"
stack_trace = [ "at level.py:451", "at science.py:176", "at art.py:498", "at success.py:287",]
level = "WARNING"
error_id = "d3f1548e-3d70-475b-a25a-df504add1be1"

[context]
user_id = "fe05329e-c851-4258-9b5b-a5822613303b"
request_id = "7878bb2d-6319-4d97-8924-bd0357b0788d"


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Output lab result as JSON.

[Assistant]
Approach:
1. Task: Create lab result in JSON
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "result_id": "LAB-5490961",
  "patient_mrn": "MRN-709917",
  "order_date": "2026-01-03",
  "panel": "CBC",
  "results": [
    {
      "test": "Glucose",
      "value": 146.9,
      "unit": "mg/dL",
      "flag": "high"
    },
    {
      "test": "Hemoglobin",
      "value": 119.4,
      "unit": "g/dL",
      "flag": "low"
    },
    {
      "test": "WBC",
      "value": 95.1,
      "unit": "g/dL",
      "flag": "low"
    },
    {
      "test": "Hemoglobin",
      "value": 73.3,
      "unit": "g/dL",
      "flag": "high"
    },
    {
      "test": "Glucose",
      "value": 166.8,
      "unit": "g/dL",
      "flag": "low"
    },
    {
      "test": "WBC",
      "value": 98.8,
      "unit": "g/dL",
      "flag": "high"
    }
  ]
}


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Produce a JSON document for a experiment result.

[Assistant]
Approach:
1. Task: Create experiment result in JSON
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "experiment_id": "EXP-506156",
  "title": "Effect of window on soldier",
  "methodology": {
    "design": "observational",
    "sample_size": 884,
    "duration_days": 87
  },
  "results": [
    {
      "metric": "safety",
      "baseline": 32.86,
      "endpoint": 81.82,
      "p_value": 0.3265
    },
    {
      "metric": "efficacy",
      "baseline": 88.84,
      "endpoint": 10.2,
      "p_value": 0.448
    },
    {
      "metric": "safety",
      "baseline": 27.68,
      "endpoint": 60.55,
      "p_value": 0.1062
    },
    {
      "metric": "safety",
      "baseline": 51.8,
      "endpoint": 89.23,
      "p_value": 0.4543
    }
  ],
  "conclusion": "significant"
}


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Transform this XML data into JSON format:
<?xml version="1.0" encoding="UTF-8"?>
<student_assessment>
<course_id>CRS-9368</course_id>
<percentage>68.59</percentage>
<assessment_id>ASMT-453812</assessment_id>
<scores>
<question>1</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>2</question>
<points_earned>3</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>3</question>
<points_earned>0</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>4</question>
<points_earned>4</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>5</question>
<points_earned>6</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>6</question>
<points_earned>9</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>7</question>
<points_earned>4</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>8</question>
<points_earned>8</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>9</question>
<points_earned>5</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>10</question>
<points_earned>0</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>11</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
</student_assessment>

[Assistant]
Approach:
1. Task: Convert student assessment from XML to JSON
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "course_id": "CRS-9368",
  "percentage": 68.59,
  "assessment_id": "ASMT-453812",
  "scores": [
    {
      "question": 1,
      "points_earned": 2,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 3,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 4,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 6,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 9,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 4,
      "points_possible": 10
    },
    {
      "question": 8,
      "points_earned": 8,
      "points_possible": 10
    },
    {
      "question": 9,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 10,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 11,
      "points_earned": 2,
      "points_possible": 10
    }
  ]
}


[System] You are an expert in TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Create a TOML representation of prescription.

[Assistant]
Approach:
1. Task: Create prescription in TOML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
rx_id = "RX-9346457"
patient_mrn = "MRN-685856"
prescriber = "Thomas Archer"
date = "2026-01-05"
[[medications]]
name = "Lisinopril"
dosage = "20mg"
frequency = "daily"
quantity = 90

[[medications]]
name = "Atorvastatin"
dosage = "10mg"
frequency = "BID"
quantity = 60


[System] You are an expert in JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User]
Convert the following XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<customer_review>
<verified_purchase>True</verified_purchase>
<title>Country especially hand.</title>
<review_id>REV-4835597</review_id>
<product_id>PROD-713270</product_id>
</customer_review>

[Assistant]
Approach:
1. Task: Convert customer review from XML to JSON
2. Complexity: simple - 3-5 fields, minimal nesting
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
{
  "verified_purchase": true,
  "title": "Country especially hand.",
  "review_id": "REV-4835597",
  "product_id": "PROD-713270"
}


[System] You are an expert in YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User] Create a YAML representation of course syllabus.

[Assistant]
Approach:
1. Task: Create course syllabus in YAML
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
course_id: CRS-2063
code: FAH301
title: Ameliorated zero administration productivity 101
instructor: Aaron Bailey
credits: 4
term: Fall 2023
schedule:
- week: 1
  topic: health
- week: 2
  topic: buy
- week: 3
  topic: conference
- week: 4
  topic: late
- week: 5
  topic: fear
- week: 6
  topic: ability
- week: 7
  topic: control
- week: 8
  topic: choose
- week: 9
  topic: measure
- week: 10
  topic: social
grading:
  homework: 30
  midterm: 30
  final: 40


[System] You are an expert in XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Reformat the data below from JSON to XML:
{
  "period": "May 2020",
  "total_revenue": 534039,
  "units_sold": 34851,
  "channels": [
    {
      "name": "retail",
      "revenue": 707158,
      "growth": 43.16
    },
    {
      "name": "wholesale",
      "revenue": 2548868,
      "growth": 26.24
    }
  ],
  "top_products": [
    {
      "sku": "SKU-6020",
      "sales": 18280
    },
    {
      "sku": "SKU-3349",
      "sales": 458703
    },
    {
      "sku": "SKU-8781",
      "sales": 265385
    }
  ]
}

[Assistant]
Approach:
1. Task: Convert sales analytics from JSON to XML
2. Complexity: medium - 5-8 fields, 2-3 levels
3. Format rules: well-formed tags, proper nesting, escaped special chars
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
<?xml version="1.0" encoding="UTF-8"?>
<sales_analytics>
<period>May 2020</period>
<total_revenue>534039</total_revenue>
<units_sold>34851</units_sold>
<channels>
<name>retail</name>
<revenue>707158</revenue>
<growth>43.16</growth>
</channels>
<channels>
<name>wholesale</name>
<revenue>2548868</revenue>
<growth>26.24</growth>
</channels>
<top_products>
<sku>SKU-6020</sku>
<sales>18280</sales>
</top_products>
<top_products>
<sku>SKU-3349</sku>
<sales>458703</sales>
</top_products>
<top_products>
<sku>SKU-8781</sku>
<sales>265385</sales>
</top_products>
</sales_analytics>