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

[User]
Transform this CSV data into YAML format:
abstract,authors,citations,funding_source,info_title,info_version,keywords,openapi,paper_id,paths_/similar_get_parameters,paths_/similar_get_responses_200_description,paths_/similar_get_responses_400_description,paths_/similar_get_summary,publication_doi,publication_venue,publication_year,title
,,,,Organic analyzing strategy API,1.0.0,,3.0.0,,"[{""name"": ""param0"", ""in"": ""query"", ""type"": ""string""}, {""name"": ""param1"", ""in"": ""query"", ""type"": ""str",Success,Bad Request,Natural see lot recently.,,,,
Light teach determine sense sister way specific. Wife toward too wide agree say the meeting.,"[{""name"": ""Vanessa Aguilar"", ""affiliation"": ""Hawkins, Johnson and Mejia"", ""email"": ""cheyennebird@exa",438,Jones PLC,,,"[""meet"", ""arm"", ""whom"", ""state"", ""not""]",,arXiv:2021.23888,,,,,10.1336/UFEn,Science,2020,Fundamental leadingedge capability
"Current clearly treat image.
Lawyer pass our among behind American short. Paper lawyer test clearly ","[{""name"": ""Dana Jacobs"", ""affiliation"": ""Vargas, Long and Cooper"", ""email"": ""grosskaren@example.org""",14,None,,,"[""race"", ""market"", ""by"", ""for"", ""just""]",,arXiv:2020.41531,,,,,10.9283/sbPW,Cell,2022,Compatible local throughput
"Political huge age yourself. Provide development buy.
Man war somebody four figure ground. Try yard ","[{""name"": ""Abigail Jones"", ""affiliation"": ""Jackson-Gonzalez"", ""email"": ""reynoldstrevor@example.org""}",293,"Osborne, Porter and Diaz",,,"[""maybe"", ""world"", ""as""]",,arXiv:2020.47015,,,,,10.7146/MnIR,Cell,2021,Realigned even-keeled Internet solution

[Assistant]
Approach:
1. Task: Convert api specification from CSV 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:
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]
Transform this JSON data into XML format:
{
  "note_id": "CN-3409162",
  "patient": {
    "mrn": "MRN-721241",
    "age": 35,
    "gender": "F"
  },
  "encounter_date": "2026-01-05",
  "chief_complaint": "fever",
  "vitals": {
    "bp": "173/107",
    "hr": 61,
    "temp_f": 99.6
  },
  "assessment": [
    {
      "problem": "diabetes",
      "icd10": "J46"
    }
  ]
}

[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-3409162</note_id>
<patient>
<mrn>MRN-721241</mrn>
<age>35</age>
<gender>F</gender>
</patient>
<encounter_date>2026-01-05</encounter_date>
<chief_complaint>fever</chief_complaint>
<vitals>
<bp>173/107</bp>

61

<temp_f>99.6</temp_f>
</vitals>
<assessment>
<problem>diabetes</problem>
<icd10>J46</icd10>
</assessment>
</clinical_note>


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

[User]
Convert the following JSON to XML:
{
  "note_id": "CN-4315709",
  "patient": {
    "mrn": "MRN-534874",
    "age": 89,
    "gender": "M"
  },
  "encounter_date": "2026-01-02",
  "chief_complaint": "chest pain",
  "vitals": {
    "bp": "165/61",
    "hr": 112,
    "temp_f": 99.4
  },
  "assessment": [
    {
      "problem": "diabetes",
      "icd10": "I12"
    },
    {
      "problem": "diabetes",
      "icd10": "K62"
    }
  ]
}

[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-4315709</note_id>
<patient>
<mrn>MRN-534874</mrn>
<age>89</age>
<gender>M</gender>
</patient>
<encounter_date>2026-01-02</encounter_date>
<chief_complaint>chest pain</chief_complaint>
<vitals>
<bp>165/61</bp>

112

<temp_f>99.4</temp_f>
</vitals>
<assessment>
<problem>diabetes</problem>
<icd10>I12</icd10>
</assessment>
<assessment>
<problem>diabetes</problem>
<icd10>K62</icd10>
</assessment>
</clinical_note>


[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-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"?>
<clinical_note>
<note_id>CN-6952117</note_id>
<patient>
<mrn>MRN-227055</mrn>
<age>50</age>
<gender>F</gender>
</patient>
<encounter_date>2026-01-03</encounter_date>
<chief_complaint>headache</chief_complaint>
<vitals>
<bp>146/78</bp>

63

<temp_f>97.9</temp_f>
</vitals>
<assessment>
<problem>hypertension</problem>
<icd10>J35</icd10>
</assessment>
<assessment>
<problem>hypertension</problem>
<icd10>K44</icd10>
</assessment>
<assessment>
<problem>hypertension</problem>
<icd10>I58</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-6952117",
  "patient": {
    "mrn": "MRN-227055",
    "age": 50,
    "gender": "F"
  },
  "encounter_date": "2026-01-03",
  "chief_complaint": "headache",
  "vitals": {
    "bp": "146/78",
    "hr": 63,
    "temp_f": 97.9
  },
  "assessment": [
    {
      "problem": "hypertension",
      "icd10": "J35"
    },
    {
      "problem": "hypertension",
      "icd10": "K44"
    },
    {
      "problem": "hypertension",
      "icd10": "I58"
    }
  ]
}


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

[User] Create a XML representation of lab result.

[Assistant]
Approach:
1. Task: Create lab result 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"?>
<lab_result>
<result_id>LAB-8547948</result_id>
<patient_mrn>MRN-375584</patient_mrn>
<order_date>2026-01-05</order_date>
<panel>Lipid</panel>
<results>
<test>WBC</test>
<value>136.4</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Glucose</test>
<value>110.1</value>
<unit>mg/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Glucose</test>
<value>77.6</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Glucose</test>
<value>164.2</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>53.3</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
</lab_result>


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

[User] Output clinical note as CSV.

[Assistant]
Approach:
1. Task: Create clinical note 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:
assessment,chief_complaint,encounter_date,note_id,patient_age,patient_gender,patient_mrn,vitals_bp,vitals_hr,vitals_temp_f
"[{""problem"": ""diabetes"", ""icd10"": ""K80""}]",fever,2026-01-05,CN-5754690,32,M,MRN-521303,101/104,85,99.3
"[{""problem"": ""diabetes"", ""icd10"": ""K98""}, {""problem"": ""diabetes"", ""icd10"": ""K45""}]",fever,2026-01-02,CN-1002106,60,F,MRN-250362,161/86,69,97.7
"[{""problem"": ""hypertension"", ""icd10"": ""K83""}, {""problem"": ""diabetes"", ""icd10"": ""J46""}, {""problem"": """,headache,2026-01-05,CN-9605917,39,M,MRN-467643,173/74,61,97.4
"[{""problem"": ""hypertension"", ""icd10"": ""K28""}]",chest pain,2026-01-03,CN-3832671,51,F,MRN-397585,116/85,101,103.0
"[{""problem"": ""diabetes"", ""icd10"": ""J53""}, {""problem"": ""hypertension"", ""icd10"": ""I19""}, {""problem"": """,headache,2026-01-04,CN-6116955,76,M,MRN-288445,112/95,61,97.6


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

[User]
Convert the following JSON to CSV:
{
  "rx_id": "RX-7309915",
  "patient_mrn": "MRN-389849",
  "prescriber": "Briana Dickerson",
  "date": "2026-01-03",
  "medications": [
    {
      "name": "Metformin",
      "dosage": "20mg",
      "frequency": "daily",
      "quantity": 30
    }
  ]
}

[Assistant]
Approach:
1. Task: Convert prescription from JSON to 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. Transform values to match the target format specifications.

Output:
date,medications,patient_mrn,prescriber,rx_id
2026-01-03,"[{""name"": ""Metformin"", ""dosage"": ""20mg"", ""frequency"": ""daily"", ""quantity"": 30}]",MRN-389849,Briana Dickerson,RX-7309915
2026-01-04,"[{""name"": ""Atorvastatin"", ""dosage"": ""5mg"", ""frequency"": ""daily"", ""quantity"": 90}, {""name"": ""Atorvast",MRN-282226,Brenda Roberson,RX-8074644
2026-01-03,"[{""name"": ""Lisinopril"", ""dosage"": ""5mg"", ""frequency"": ""TID"", ""quantity"": 90}]",MRN-937202,Jacob Taylor,RX-8384566
2026-01-03,"[{""name"": ""Metformin"", ""dosage"": ""10mg"", ""frequency"": ""BID"", ""quantity"": 30}, {""name"": ""Atorvastatin",MRN-446981,Jessica Smith,RX-2483272


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

[User] Generate customer review data in YAML format.

[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-5832770
product_id: PROD-688379
user_id: d5e035fb-55b8-4fae-85aa-504fb2b837bd
rating: 3
title: Investment great beat fear step Democrat treat.
content: 'Near season take foot decade hold some meet.

  Pretty season exist old turn consumer particularly star. Step receive unit different.'
date: '2026-01-02'
verified_purchase: false
helpful_votes: 62


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

[User] Produce a YAML document for a contract.

[Assistant]
Approach:
1. Task: Create contract 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:
contract_id: CTR-784600
parties:
- name: Snyder-Thornton
  role: party_a
- name: Oliver Group
  role: party_b
effective_date: '2026-01-06'
term_months: 16
value: 53888
status: active


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

[User] Create a XML representation of financial report.

[Assistant]
Approach:
1. Task: Create financial report 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"?>
<financial_report>
<company>Nichols, Ferguson and Butler</company>
<ticker>NDVN</ticker>
<quarter>Q1 2022</quarter>
<revenue>39470251</revenue>
<expenses>26562838</expenses>
<net_income>16784469</net_income>
<eps>3.2</eps>
<metrics>
<profit_margin>27.93</profit_margin>
<roe>5.96</roe>
</metrics>
</financial_report>


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

[User] Create a JSON representation of product listing.

[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:
{
  "rating": 4.54,
  "stock": 843,
  "specs": {
    "weight_kg": 34.7,
    "dimensions": "61x86x65cm"
  },
  "currency": "USD"
}


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

[User] Produce a JSON document for a product listing.

[Assistant]
Approach:
1. Task: Create product listing 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:
{
  "product_id": "PROD-125540",
  "name": "Universal eco-centric approach",
  "category": "books",
  "price": 208.68,
  "currency": "USD",
  "stock": 879,
  "is_available": true,
  "rating": 4.53,
  "reviews": 925,
  "specs": {
    "weight_kg": 20.35,
    "dimensions": "45x91x83cm"
  }
}


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

[User]
Convert the following YAML to CSV:
product_id: PROD-685161
name: Balanced demand-driven capacity
category: clothing
price: 554.83
currency: USD
stock: 872
is_available: true
rating: 3.32
reviews: 212
specs:
  weight_kg: 39.83
  dimensions: 86x88x63cm

[Assistant]
Approach:
1. Task: Convert product listing from YAML to 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. Transform values to match the target format specifications.

Output:
category,currency,is_available,name,price,product_id,rating,reviews,specs_dimensions,specs_weight_kg,stock
clothing,USD,True,Balanced demand-driven capacity,554.83,PROD-685161,3.32,212,86x88x63cm,39.83,872
books,USD,True,Persevering clear-thinking system engine,60.23,PROD-666035,2.19,226,86x17x28cm,4.22,840
electronics,USD,True,Multi-tiered directional policy,879.15,PROD-832344,2.12,992,67x53x29cm,5.65,417


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

[User] Generate financial report data in TOML format.

[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 = "Martinez, Patterson and Hunter"
ticker = "PPOB"
quarter = "Q1 2023"
revenue = 66694743
expenses = 21448775
net_income = 421192
eps = 2.32

[metrics]
profit_margin = 7.89
roe = 9.32


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

[User] Output student assessment as XML.

[Assistant]
Approach:
1. Task: Create student assessment 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"?>
<student_assessment>
<assessment_id>ASMT-871992</assessment_id>
<student_id>STU-916232</student_id>
<course_id>CRS-5865</course_id>
<type>quiz</type>
<date>2026-01-05</date>
<scores>
<question>1</question>
<points_earned>1</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>2</question>
<points_earned>6</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>3</question>
<points_earned>10</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>4</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>5</question>
<points_earned>5</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>6</question>
<points_earned>5</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>7</question>
<points_earned>0</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>8</question>
<points_earned>7</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>9</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>10</question>
<points_earned>4</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>11</question>
<points_earned>5</points_earned>
<points_possible>10</points_possible>
</scores>
<total_score>83</total_score>
<percentage>70.57</percentage>
<grade>B</grade>
</student_assessment>


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

[User]
Convert the following JSON to TOML:
{
  "post_id": "POST-58883169",
  "user_id": "9fc1586a-703b-4459-a18b-2f5e0582ab64",
  "username": "alexandra57"
}

[Assistant]
Approach:
1. Task: Convert social media post from JSON 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:
post_id = "POST-58883169"
user_id = "9fc1586a-703b-4459-a18b-2f5e0582ab64"
username = "alexandra57"


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

[User]
Reformat the data below from TOML to YAML:
instructor = "Tamara Stark"
term = "Spring 2022"
code = "VMA483"

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

[Assistant]
Approach:
1. Task: Convert course syllabus from TOML to 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. Transform values to match the target format specifications.

Output:
instructor: Tamara Stark
term: Spring 2022
code: VMA483
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:
{
  "title": "Stand-alone empowering functionalities 101",
  "schedule": [
    {
      "week": 1,
      "topic": "class"
    },
    {
      "week": 2,
      "topic": "leave"
    },
    {
      "week": 3,
      "topic": "develop"
    },
    {
      "week": 4,
      "topic": "style"
    },
    {
      "week": 5,
      "topic": "again"
    },
    {
      "week": 6,
      "topic": "man"
    },
    {
      "week": 7,
      "topic": "list"
    }
  ],
  "grading": {
    "homework": 30,
    "midterm": 30,
    "final": 40
  }
}

[Assistant]
Approach:
1. Task: Convert course syllabus 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"?>
<course_syllabus>
<title>Stand-alone empowering functionalities 101</title>
<schedule>
<week>1</week>
<topic>class</topic>
</schedule>
<schedule>
<week>2</week>
<topic>leave</topic>
</schedule>
<schedule>
<week>3</week>
<topic>develop</topic>
</schedule>
<schedule>
<week>4</week>
<topic>style</topic>
</schedule>
<schedule>
<week>5</week>
<topic>again</topic>
</schedule>
<schedule>
<week>6</week>
<topic>man</topic>
</schedule>
<schedule>
<week>7</week>
<topic>list</topic>
</schedule>
<grading>
<homework>30</homework>
<midterm>30</midterm>
<final>40</final>
</grading>
</course_syllabus>


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

[User] Generate contract data in TOML format.

[Assistant]
Approach:
1. Task: Create contract 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:
contract_id = "CTR-324198"
effective_date = "2026-01-06"
term_months = 34
value = 725205
status = "pending"
[[parties]]
name = "Alvarado-Barnes"
role = "party_a"

[[parties]]
name = "Lopez, Price and Melendez"
role = "party_b"


[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: 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:
{
  "txn_id": "TXN-98000345",
  "customer_id": "681a59bc-1151-4d49-8e75-1af0878315cb",
  "timestamp": "2026-01-07T13:51:26.087659",
  "items": [
    {
      "sku": "SKU-50616",
      "qty": 3,
      "price": 180.94
    },
    {
      "sku": "SKU-82812",
      "qty": 5,
      "price": 490.31
    }
  ],
  "subtotal": 948.16,
  "tax": 93.65,
  "total": 1389.7,
  "payment_method": "credit_card"
}


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

[User] Output transaction record as CSV.

[Assistant]
Approach:
1. Task: Create transaction record 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:
customer_id,items,payment_method,subtotal,tax,timestamp,total,txn_id
9090a0a6-3421-4a93-9d6e-78b6a1f8fed7,"[{""sku"": ""SKU-33525"", ""qty"": 3, ""price"": 130.6}]",apple_pay,793.41,85.16,2026-01-07T13:51:27.692524,1645.24,TXN-95106885
86a44613-04a4-42fe-9f17-d36f56e72a16,"[{""sku"": ""SKU-36909"", ""qty"": 1, ""price"": 60.14}, {""sku"": ""SKU-49823"", ""qty"": 5, ""price"": 406.68}, {""",apple_pay,624.04,16.36,2026-01-07T13:51:27.693375,1135.24,TXN-20589055
342e6dec-1677-43a9-a2d8-11dcc43eb2b3,"[{""sku"": ""SKU-94897"", ""qty"": 1, ""price"": 71.61}, {""sku"": ""SKU-65129"", ""qty"": 5, ""price"": 202.05}]",credit_card,60.8,177.08,2026-01-07T13:51:27.693445,1399.3,TXN-88260739


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

[User] Create a TOML representation of research paper.

[Assistant]
Approach:
1. Task: Create research paper 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:
paper_id = "arXiv:2023.57642"
title = "Organized clear-thinking projection"
abstract = "Leg third those over. Almost state money hear condition little.\nManager minute writer manager. Walk hand situation similar media back fill huge."
keywords = [ "decade", "kitchen", "discover", "wide",]
citations = 306
[[authors]]
name = "Thomas Harrison"
affiliation = "Benjamin, Peterson and James"
email = "martinjoseph@example.com"

[[authors]]
name = "Samantha Phillips"
affiliation = "Carpenter Ltd"
email = "hawkinssarah@example.com"

[publication]
venue = "Cell"
year = 2023
doi = "10.5371/HWME"


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

[User] Produce a TOML document for a transaction record.

[Assistant]
Approach:
1. Task: Create transaction record 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:
txn_id = "TXN-72432123"
customer_id = "d14101dd-b47b-4916-b7e5-0ad54eefbb55"
timestamp = "2026-01-07T13:51:26.808008"
subtotal = 1301.16
tax = 124.54
total = 239.49
payment_method = "apple_pay"
[[items]]
sku = "SKU-29323"
qty = 2
price = 70.49

[[items]]
sku = "SKU-65460"
qty = 3
price = 466.32

[[items]]
sku = "SKU-59283"
qty = 1
price = 149.96

[[items]]
sku = "SKU-68579"
qty = 2
price = 13.04


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

[User] Output social media post as CSV.

[Assistant]
Approach:
1. Task: Create social media post 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:
content,engagement_comments,engagement_likes,engagement_shares,hashtags,platform,post_id,timestamp,user_id,username
Eight television free focus window leave. Education Democrat past music mother able. Fund season rep,292,85,112,"[""#left"", ""#long"", ""#instead"", ""#budget""]",instagram,POST-15490463,2026-01-07T13:51:27.338072,dcf0c28f-6ad6-4153-873b-0cd39fa73a46,allenjames
"Information choose writer economy. Report other dog her crime reason push.
Behind feeling seven fact",247,1153,820,"[""#computer"", ""#maintain""]",facebook,POST-11960767,2026-01-07T13:51:27.339440,2573bbe9-e8b7-4c21-a471-166b2c3c8e5a,jeffcain
"Police product way. Notice child them make both system.
Compare break growth end among family organi",29,1132,819,"[""#American"", ""#she"", ""#myself"", ""#religious""]",facebook,POST-28656326,2026-01-07T13:51:27.339819,405784ac-08a4-4b8d-9adf-4364834692b1,lopezpamela
"Play science buy note listen condition. Score agreement material.
Entire pay create pressure oil tea",464,2314,306,"[""#since"", ""#factor""]",instagram,POST-78157261,2026-01-07T13:51:27.340202,6d070911-d46f-4504-b9ea-6de223788ebe,gomezjulia
Firm any until couple open possible team. Impact sister action important difference south believe. W,14,1169,393,"[""#summer"", ""#government"", ""#down"", ""#choose""]",facebook,POST-37501606,2026-01-07T13:51:27.340462,adfcad1d-6147-4e41-8873-93f8018f4c24,shawn54


[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"?>
<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>

[Assistant]
Approach:
1. Task: Convert error log 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:
{
  "service": "approachService",
  "context": {
    "user_id": "fe05329e-c851-4258-9b5b-a5822613303b",
    "request_id": "7878bb2d-6319-4d97-8924-bd0357b0788d"
  },
  "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"
}


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

[User] Produce a YAML document for a sales analytics.

[Assistant]
Approach:
1. Task: Create sales analytics 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:
period: February 2024
total_revenue: 3108970
units_sold: 70292
channels:
- name: online
  revenue: 1867975
  growth: -2.95
- name: retail
  revenue: 1947932
  growth: 22.54
top_products:
- sku: SKU-5409
  sales: 492017
- sku: SKU-4816
  sales: 486050
- sku: SKU-9957
  sales: 44038


[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"?>
<financial_report>
<net_income>16280737</net_income>
<revenue>93235840</revenue>
<metrics>
<profit_margin>9.67</profit_margin>
<roe>5.79</roe>
</metrics>
<expenses>6835352</expenses>
</financial_report>

[Assistant]
Approach:
1. Task: Convert financial report from XML to 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. Transform values to match the target format specifications.

Output:
net_income: 16280737
revenue: 93235840
metrics:
  profit_margin: 9.67
  roe: 5.79
expenses: 6835352


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

[User] Create a CSV representation of clinical note.

[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"": ""J39""}]",fever,2026-01-05,CN-8293808,24,F,MRN-980604,150/98,93,102.4
"[{""problem"": ""hypertension"", ""icd10"": ""J48""}, {""problem"": ""hypertension"", ""icd10"": ""K24""}, {""problem",headache,2026-01-02,CN-6513905,90,F,MRN-241562,140/81,119,103.0
"[{""problem"": ""hypertension"", ""icd10"": ""J44""}, {""problem"": ""diabetes"", ""icd10"": ""K52""}]",fever,2026-01-03,CN-4759765,84,M,MRN-475659,118/72,100,97.3
"[{""problem"": ""diabetes"", ""icd10"": ""K60""}, {""problem"": ""hypertension"", ""icd10"": ""J24""}]",chest pain,2026-01-02,CN-3596414,74,F,MRN-647661,136/93,95,97.6


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

[User] Produce a TOML document for a news article.

[Assistant]
Approach:
1. Task: Create news article 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:
content = "Range condition class leader wrong subject wait. Stop we prevent particular front method. Force item executive employee green large house nation. General doctor material rule head event.\nOne will dog chance. Order away music seem director put upon. Find boy again direction four."
article_id = "ART-9628131"
headline = "Re-contextualized logistical paradigm"


[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>
<timestamp>2026-01-07T13:51:26.818902</timestamp>
<context>
<user_id>3a96bf92-d9b7-4f9e-91da-6a15b590d5cf</user_id>
<request_id>d8c0cfbc-2ae8-46b8-8db8-ee3a71153dc5</request_id>
</context>
<error_id>de32926d-7560-4176-8c16-f346784a78af</error_id>
</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"?>
<product_listing>
<specs>
<weight_kg>8.02</weight_kg>
<dimensions>62x22x22cm</dimensions>
</specs>
<name>Universal fault-tolerant service-desk</name>
<price>907.33</price>
<reviews>493</reviews>
</product_listing>

[Assistant]
Approach:
1. Task: Convert product listing from XML to 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. Transform values to match the target format specifications.

Output:
specs:
  weight_kg: 8.02
  dimensions: 62x22x22cm
name: Universal fault-tolerant service-desk
price: 907.33
reviews: 493


[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: 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"?>
<customer_review>
<review_id>REV-1274071</review_id>
<product_id>PROD-300785</product_id>
<user_id>c183a72e-4d6f-40ac-92b0-2d1073039571</user_id>
<rating>5</rating>
<title>Follow large high gas clear drug pretty else.</title>
<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.</content>
<date>2026-01-06</date>
<verified_purchase>True</verified_purchase>
<helpful_votes>37</helpful_votes>
</customer_review>


[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: 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:
product_id: PROD-738287
name: Function-based dedicated installation
category: electronics
price: 364.53
currency: USD
stock: 956
is_available: true
rating: 1.64
reviews: 854
specs:
  weight_kg: 19.72
  dimensions: 48x33x88cm


[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>Scott-Campbell</company>
<ticker>SZQB</ticker>
<quarter>Q2 2021</quarter>
<revenue>15063505</revenue>
<expenses>40738667</expenses>
<net_income>16626814</net_income>
<eps>6.47</eps>
<metrics>
<profit_margin>15.92</profit_margin>
<roe>8.35</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": "Scott-Campbell",
  "ticker": "SZQB",
  "quarter": "Q2 2021",
  "revenue": 15063505,
  "expenses": 40738667,
  "net_income": 16626814,
  "eps": 6.47,
  "metrics": {
    "profit_margin": 15.92,
    "roe": 8.35
  }
}


[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"?>
<lab_result>
<result_id>LAB-9548003</result_id>
<patient_mrn>MRN-322322</patient_mrn>
<order_date>2026-01-03</order_date>
<panel>CMP</panel>
<results>
<test>Glucose</test>
<value>67.0</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>66.7</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Glucose</test>
<value>118.1</value>
<unit>mg/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>198.6</value>
<unit>mg/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Glucose</test>
<value>167.9</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>143.5</value>
<unit>mg/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Glucose</test>
<value>150.3</value>
<unit>g/dL</unit>
<flag>low</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-9548003",
  "patient_mrn": "MRN-322322",
  "order_date": "2026-01-03",
  "panel": "CMP",
  "results": [
    {
      "test": "Glucose",
      "value": 67.0,
      "unit": "g/dL",
      "flag": "high"
    },
    {
      "test": "Hemoglobin",
      "value": 66.7,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "Glucose",
      "value": 118.1,
      "unit": "mg/dL",
      "flag": "normal"
    },
    {
      "test": "Hemoglobin",
      "value": 198.6,
      "unit": "mg/dL",
      "flag": "normal"
    },
    {
      "test": "Glucose",
      "value": 167.9,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "Hemoglobin",
      "value": 143.5,
      "unit": "mg/dL",
      "flag": "normal"
    },
    {
      "test": "Glucose",
      "value": 150.3,
      "unit": "g/dL",
      "flag": "low"
    }
  ]
}


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

[User]
Convert the following JSON to XML:
{
  "paths": {
    "/population": {
      "get": {
        "summary": "Lead room develop man according street century.",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param1",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param2",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "400": {
            "description": "Bad Request"
          }
        }
      }
    }
  },
  "info": {
    "title": "Ameliorated radical initiative API",
    "version": "1.0.0"
  },
  "openapi": "3.0.0"
}

[Assistant]
Approach:
1. Task: Convert api specification 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"?>
<api_specification>
<paths>
</population>
<get>
<summary>Lead room develop man according street century.</summary>
<parameters>
<name>param0</name>
<in>query</in>
<type>string</type>
</parameters>
<parameters>
<name>param1</name>
<in>query</in>
<type>string</type>
</parameters>
<parameters>
<name>param2</name>
<in>query</in>
<type>string</type>
</parameters>
<responses>
<200>
<description>Success</description>
</200>
<400>
<description>Bad Request</description>
</400>
</responses>
</get>
<//population>
</paths>
<info>
<title>Ameliorated radical initiative API</title>
<version>1.0.0</version>
</info>
<openapi>3.0.0</openapi>
</api_specification>


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

[User]
Convert the following JSON to XML:
{
  "article_id": "ART-2650245",
  "headline": "Enhanced multimedia hub",
  "author": "Jennifer Davis",
  "published": "2026-01-02T17:44:08.333575",
  "section": "Tech",
  "content": "Late situation both data share market. These foot health box.\nPay me together western ever story cup. Game production million happy reveal. Attorney bill shake crime eat finally drive.\nChoice four concern cold test. Road executive policy official season increase.",
  "tags": [
    "future",
    "here",
    "new"
  ],
  "views": 46115,
  "shares": 2819
}

[Assistant]
Approach:
1. Task: Convert news article 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"?>
<news_article>
<article_id>ART-2650245</article_id>
<headline>Enhanced multimedia hub</headline>
<author>Jennifer Davis</author>
<published>2026-01-02T17:44:08.333575</published>

Tech

<content>Late situation both data share market. These foot health box.
Pay me together western ever story cup. Game production million happy reveal. Attorney bill shake crime eat finally drive.
Choice four concern cold test. Road executive policy official season increase.</content>
<tags>future</tags>
<tags>here</tags>
<tags>new</tags>
<views>46115</views>
<shares>2819</shares>
</news_article>


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

[User] Generate customer review data in YAML format.

[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-2971879
product_id: PROD-601177
user_id: 49ec79ac-cc63-409f-8812-a70c492b6835
rating: 3
title: Safe everything country understand subject.
content: 'Explain among above role speech. Hear law enjoy. Lose building fall everything
  million.

  East choose however class dark test good contain. Indicate worker mean hot.'
date: '2026-01-06'
verified_purchase: true
helpful_votes: 67


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

[User] Generate news article data in CSV format.

[Assistant]
Approach:
1. Task: Create news article 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:
article_id,author,content,headline,published,section,shares,tags,views
ART-5646798,Joshua Bell,List road lay not year. Congress gas full particular according. Prevent hear century game friend civ,Switchable hybrid groupware,2026-01-05T05:31:42.088007,Business,596,"[""author"", ""son"", ""food""]",74484
ART-2254015,Rachel Dunn,"Policy message ready book outside. Prove leader full everything tell agent.
Defense right shoulder l",Customizable zero-defect solution,2026-01-01T17:12:22.480515,Sports,2743,"[""hundred"", ""wait"", ""within"", ""minute""]",726
ART-7615310,Samantha Pope,"Newspaper nothing make policy key policy. Hit hard individual military arrive.
Approach see road bag",Ergonomic composite migration,2026-01-06T07:23:19.216455,Business,2428,"[""situation"", ""despite"", ""throw""]",58030
ART-6728296,Gregory Pratt,Wrong buy eight forward exist usually. Toward my fall let forget door. Respond capital trip exist in,Proactive non-volatile ability,2026-01-02T11:24:20.035798,Business,445,"[""wonder"", ""quite"", ""enjoy""]",77809


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

[User] Output lab result as XML.

[Assistant]
Approach:
1. Task: Create lab result 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"?>
<lab_result>
<result_id>LAB-5790382</result_id>
<patient_mrn>MRN-357903</patient_mrn>
<order_date>2026-01-02</order_date>
<panel>CBC</panel>
<results>
<test>Glucose</test>
<value>56.1</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Glucose</test>
<value>114.1</value>
<unit>mg/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>WBC</test>
<value>154.7</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Glucose</test>
<value>196.1</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
</lab_result>


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

[User] Output course syllabus as TOML.

[Assistant]
Approach:
1. Task: Create course syllabus 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:
instructor = "Tamara Stark"
term = "Spring 2022"
code = "VMA483"

[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]
Convert the following YAML to JSON:
openapi: 3.0.0
info:
  title: Streamlined logistical help-desk API
  version: 1.0.0
paths:
  /grow:
    get:
      summary: Will entire deep source history.
      parameters:
      - name: param0
        in: query
        type: string
      - name: param1
        in: query
        type: string
      - name: param2
        in: query
        type: string
      responses:
        '200':
          description: Success
        '400':
          description: Bad Request

[Assistant]
Approach:
1. Task: Convert api specification from YAML 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:
{
  "openapi": "3.0.0",
  "info": {
    "title": "Streamlined logistical help-desk API",
    "version": "1.0.0"
  },
  "paths": {
    "/grow": {
      "get": {
        "summary": "Will entire deep source history.",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param1",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param2",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "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 customer review.

[Assistant]
Approach:
1. Task: Create customer review 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:
review_id = "REV-5008217"
product_id = "PROD-173271"
user_id = "11121b86-b60c-4ea2-b83a-cbb727cd7a24"
rating = 2
title = "Lay lead it politics brother."
content = "Camera left hope prevent inside real. Whether trouble their always manager cultural majority popular. Song condition entire security concern science. Run number firm listen serious."
date = "2026-01-01"
verified_purchase = true
helpful_votes = 23


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

[User] Output student assessment as JSON.

[Assistant]
Approach:
1. Task: Create student assessment 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:
{
  "assessment_id": "ASMT-485879",
  "student_id": "STU-284737",
  "course_id": "CRS-1053",
  "type": "exam",
  "date": "2026-01-03",
  "scores": [
    {
      "question": 1,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 6,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 7,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 9,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 9,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 2,
      "points_possible": 10
    }
  ],
  "total_score": 89,
  "percentage": 64.04,
  "grade": "A"
}


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

[User] Produce a XML document for a news article.

[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-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>


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

[User]
Convert the following JSON to CSV:
{
  "assessment_id": "ASMT-422317",
  "student_id": "STU-280699",
  "course_id": "CRS-7308",
  "type": "exam",
  "date": "2026-01-01",
  "scores": [
    {
      "question": 1,
      "points_earned": 2,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 3,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 10,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 10,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 3,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 7,
      "points_possible": 10
    }
  ],
  "total_score": 75,
  "percentage": 83.59,
  "grade": "B"
}

[Assistant]
Approach:
1. Task: Convert student assessment from JSON to 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. Transform values to match the target format specifications.

Output:
assessment_id,code,course_id,credits,date,grade,grading_final,grading_homework,grading_midterm,instructor,percentage,schedule,scores,student_id,term,title,total_score,type
ASMT-422317,,CRS-7308,,2026-01-01,B,,,,,83.59,,"[{""question"": 1, ""points_earned"": 2, ""points_possible"": 10}, {""question"": 2, ""points_earned"": 3, ""po",STU-280699,,,75,exam
,AGN486,CRS-1989,4,,,40,30,30,Jeremy Diaz,,"[{""week"": 1, ""topic"": ""agency""}, {""week"": 2, ""topic"": ""myself""}, {""week"": 3, ""topic"": ""either""}, {""w",,,Spring 2024,Front-line optimizing model 101,,
,HDJ345,CRS-1730,3,,,40,30,30,Calvin Hernandez,,"[{""week"": 1, ""topic"": ""kind""}, {""week"": 2, ""topic"": ""international""}, {""week"": 3, ""topic"": ""thus""}, ",,,Fall 2021,Streamlined impactful system engine 101,,


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

[User]
Convert the following CSV to YAML:
article_id,author,content,headline,published,section,shares,tags,views
ART-3366717,Linda Clark,"Anything music blood how public to. Even student idea piece if. Whom phone rich long.
Look memory he",Integrated bi-directional complexity,2026-01-07T13:19:20.433040,Tech,1143,"[""necessary"", ""listen"", ""employee"", ""here""]",75939
ART-2300982,Valerie Boyd,"Certain individual something may.
Home forget soldier send write voice above discuss. Environment de",Reverse-engineered optimal paradigm,2026-01-06T10:45:06.021177,Business,3413,"[""poor"", ""there"", ""position"", ""simply""]",51647
ART-9981163,Anthony Evans,Letter tend while visit piece practice experience. Job month use my should weight local. Goal writer,Distributed client-server middleware,2026-01-03T06:40:27.860753,Tech,4279,"[""explain"", ""lay"", ""huge"", ""attention"", ""you""]",94849
ART-9931857,Calvin Allen,"Back wrong central begin nation between how. Already see difference once they.
Just bill true effort",Organic zero tolerance approach,2026-01-07T09:32:14.685651,Business,4614,"[""street"", ""able"", ""option""]",21166
ART-4047081,Leonard Reynolds,Probably second front family within leg politics. Change conference argue market live fact developme,Team-oriented analyzing Graphical User Interface,2026-01-01T21:50:34.262728,Tech,3628,"[""couple"", ""idea"", ""appear"", ""let"", ""around""]",26508

[Assistant]
Approach:
1. Task: Convert news article from CSV 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:
article_id: ART-3366717
headline: Integrated bi-directional complexity
author: Linda Clark
published: '2026-01-07T13:19:20.433040'
section: Tech
content: 'Anything music blood how public to. Even student idea piece if. Whom phone
  rich long.

  Look memory help find reduce. Structure most tough realize ground.

  Around fear reduce ago. Thus moment letter evening stuff three.

  Her themselves campaign cup four catch as. Part turn leg.'
tags:
- necessary
- listen
- employee
- here
views: 75939
shares: 1143


[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"?>
<lab_result>
<result_id>LAB-6812810</result_id>
<patient_mrn>MRN-631747</patient_mrn>
<order_date>2026-01-04</order_date>
<panel>Lipid</panel>
<results>
<test>WBC</test>
<value>54.3</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<results>
<test>WBC</test>
<value>70.8</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Glucose</test>
<value>59.6</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>WBC</test>
<value>140.8</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
</lab_result>

[Assistant]
Approach:
1. Task: Convert lab result 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:
{
  "result_id": "LAB-6812810",
  "patient_mrn": "MRN-631747",
  "order_date": "2026-01-04",
  "panel": "Lipid",
  "results": [
    {
      "test": "WBC",
      "value": 54.3,
      "unit": "mg/dL",
      "flag": "high"
    },
    {
      "test": "WBC",
      "value": 70.8,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "Glucose",
      "value": 59.6,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "WBC",
      "value": 140.8,
      "unit": "mg/dL",
      "flag": "high"
    }
  ]
}


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

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

[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>8332f894-7969-41dc-b41e-1d31b796cba0</error_id>
<timestamp>2026-01-07T13:51:27.125156</timestamp>
<level>CRITICAL</level>
<service>imageService</service>
<message>Bad prepare able heavy morning.</message>
<stack_trace>at property.py:50</stack_trace>
<stack_trace>at short.py:272</stack_trace>
<context>
<user_id>a3de195f-baae-4936-bd72-230b1e266327</user_id>
<request_id>35eb846c-1e2f-4451-a60f-b8ee42943a10</request_id>
</context>
</error_log>


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

[User]
Reformat the data below from JSON to TOML:
{
  "authors": [
    {
      "name": "Emily Davis",
      "affiliation": "Nelson, Diaz and Humphrey",
      "email": "jpalmer@example.org"
    },
    {
      "name": "Robert Clark",
      "affiliation": "James Inc",
      "email": "dennis02@example.net"
    }
  ],
  "publication": {
    "venue": "Science",
    "year": 2022,
    "doi": "10.6797/tlMT"
  },
  "keywords": [
    "lead",
    "list",
    "she"
  ],
  "funding_source": null,
  "paper_id": "arXiv:2022.72917"
}

[Assistant]
Approach:
1. Task: Convert research paper from JSON 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:
keywords = [ "lead", "list", "she",]
paper_id = "arXiv:2022.72917"
[[authors]]
name = "Emily Davis"
affiliation = "Nelson, Diaz and Humphrey"
email = "jpalmer@example.org"

[[authors]]
name = "Robert Clark"
affiliation = "James Inc"
email = "dennis02@example.net"

[publication]
venue = "Science"
year = 2022
doi = "10.6797/tlMT"


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

[User] Generate prescription data in TOML format.

[Assistant]
Approach:
1. Task: Create prescription 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:
rx_id = "RX-7309915"
patient_mrn = "MRN-389849"
prescriber = "Briana Dickerson"
date = "2026-01-03"
[[medications]]
name = "Metformin"
dosage = "20mg"
frequency = "daily"
quantity = 30


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

[User] Produce a CSV document for a course syllabus.

[Assistant]
Approach:
1. Task: Create course syllabus 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:
code,course_id,credits,grading_final,grading_homework,grading_midterm,instructor,schedule,term,title
NZW166,CRS-1689,4,40,30,30,David Chen,"[{""week"": 1, ""topic"": ""in""}, {""week"": 2, ""topic"": ""summer""}, {""week"": 3, ""topic"": ""unit""}, {""week"": ",Fall 2021,Operative modular system engine 101
SHL494,CRS-1305,3,40,30,30,Michael Walsh,"[{""week"": 1, ""topic"": ""wonder""}, {""week"": 2, ""topic"": ""usually""}, {""week"": 3, ""topic"": ""nice""}, {""we",Spring 2024,User-friendly transitional product 101
ULQ145,CRS-1487,4,40,30,30,Jacob Kelly,"[{""week"": 1, ""topic"": ""entire""}, {""week"": 2, ""topic"": ""woman""}, {""week"": 3, ""topic"": ""cut""}, {""week""",Spring 2023,Public-key tertiary matrices 101


[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: 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:
{
  "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 JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Output research paper as JSON.

[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:2023.57642",
  "title": "Organized clear-thinking projection",
  "authors": [
    {
      "name": "Thomas Harrison",
      "affiliation": "Benjamin, Peterson and James",
      "email": "martinjoseph@example.com"
    },
    {
      "name": "Samantha Phillips",
      "affiliation": "Carpenter Ltd",
      "email": "hawkinssarah@example.com"
    }
  ],
  "abstract": "Leg third those over. Almost state money hear condition little.\nManager minute writer manager. Walk hand situation similar media back fill huge.",
  "keywords": [
    "decade",
    "kitchen",
    "discover",
    "wide"
  ],
  "publication": {
    "venue": "Cell",
    "year": 2023,
    "doi": "10.5371/HWME"
  },
  "citations": 306,
  "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:
{
  "note_id": "CN-2341842",
  "patient": {
    "mrn": "MRN-739833",
    "age": 85,
    "gender": "F"
  },
  "encounter_date": "2026-01-01",
  "chief_complaint": "fever",
  "vitals": {
    "bp": "174/60",
    "hr": 60,
    "temp_f": 98.7
  },
  "assessment": [
    {
      "problem": "diabetes",
      "icd10": "I65"
    },
    {
      "problem": "diabetes",
      "icd10": "J29"
    }
  ]
}

[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-2341842</note_id>
<patient>
<mrn>MRN-739833</mrn>
<age>85</age>
<gender>F</gender>
</patient>
<encounter_date>2026-01-01</encounter_date>
<chief_complaint>fever</chief_complaint>
<vitals>
<bp>174/60</bp>

60

<temp_f>98.7</temp_f>
</vitals>
<assessment>
<problem>diabetes</problem>
<icd10>I65</icd10>
</assessment>
<assessment>
<problem>diabetes</problem>
<icd10>J29</icd10>
</assessment>
</clinical_note>


[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: 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:
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.

  Just 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] 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:2023.54834",
  "title": "Profit-focused radical throughput",
  "authors": [
    {
      "name": "Scott Dunlap",
      "affiliation": "Daniels PLC",
      "email": "williamssheri@example.com"
    },
    {
      "name": "Shawn Payne",
      "affiliation": "Dorsey, Price and Burns",
      "email": "smoyer@example.net"
    },
    {
      "name": "Chase Lee",
      "affiliation": "Garcia Inc",
      "email": "burgesssarah@example.org"
    }
  ],
  "abstract": "Read deal buy final thus. Sure home game rest lose.\nThose any reality if teach.\nMain never still little study clearly today.",
  "keywords": [
    "measure",
    "stage",
    "scene",
    "reason"
  ],
  "publication": {
    "venue": "Science",
    "year": 2020,
    "doi": "10.8776/UhOT"
  },
  "citations": 388,
  "funding_source": "Reese-Perez"
}


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

[User]
Reformat the data below from YAML to JSON:
rx_id: RX-3684509
patient_mrn: MRN-644382
prescriber: Chelsea Henson
date: '2026-01-04'
medications:
- name: Atorvastatin
  dosage: 10mg
  frequency: BID
  quantity: 60
- name: Metformin
  dosage: 20mg
  frequency: daily
  quantity: 60
- name: Metformin
  dosage: 10mg
  frequency: TID
  quantity: 60

[Assistant]
Approach:
1. Task: Convert prescription from YAML 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-3684509",
  "patient_mrn": "MRN-644382",
  "prescriber": "Chelsea Henson",
  "date": "2026-01-04",
  "medications": [
    {
      "name": "Atorvastatin",
      "dosage": "10mg",
      "frequency": "BID",
      "quantity": 60
    },
    {
      "name": "Metformin",
      "dosage": "20mg",
      "frequency": "daily",
      "quantity": 60
    },
    {
      "name": "Metformin",
      "dosage": "10mg",
      "frequency": "TID",
      "quantity": 60
    }
  ]
}


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

[User]
Reformat the data below from YAML to XML:
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

[Assistant]
Approach:
1. Task: Convert experiment result from YAML 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-506156</experiment_id>
<title>Effect of window on soldier</title>
<methodology>
<design>observational</design>
<sample_size>884</sample_size>
<duration_days>87</duration_days>
</methodology>
<results>
<metric>safety</metric>
<baseline>32.86</baseline>
<endpoint>81.82</endpoint>
<p_value>0.3265</p_value>
</results>
<results>
<metric>efficacy</metric>
<baseline>88.84</baseline>
<endpoint>10.2</endpoint>
<p_value>0.448</p_value>
</results>
<results>
<metric>safety</metric>
<baseline>27.68</baseline>
<endpoint>60.55</endpoint>
<p_value>0.1062</p_value>
</results>
<results>
<metric>safety</metric>
<baseline>51.8</baseline>
<endpoint>89.23</endpoint>
<p_value>0.4543</p_value>
</results>
<conclusion>significant</conclusion>
</experiment_result>


[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:
{
  "company": "Woods and Sons",
  "ticker": "ZCNM",
  "quarter": "Q1 2020",
  "revenue": 42196406,
  "expenses": 48284794,
  "net_income": 2937548,
  "eps": 8.84,
  "metrics": {
    "profit_margin": 12.42,
    "roe": 23.86
  }
}

[Assistant]
Approach:
1. Task: Convert financial report 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"?>
<financial_report>
<company>Woods and Sons</company>
<ticker>ZCNM</ticker>
<quarter>Q1 2020</quarter>
<revenue>42196406</revenue>
<expenses>48284794</expenses>
<net_income>2937548</net_income>
<eps>8.84</eps>
<metrics>
<profit_margin>12.42</profit_margin>
<roe>23.86</roe>
</metrics>
</financial_report>


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

[User] Produce a XML document for a transaction record.

[Assistant]
Approach:
1. Task: Create transaction record 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"?>
<transaction_record>
<txn_id>TXN-48552675</txn_id>
<customer_id>19d3d880-f410-4b85-a35d-9c5777705d1f</customer_id>
<timestamp>2026-01-07T13:51:26.837603</timestamp>
<items>
<sku>SKU-30306</sku>
<qty>4</qty>
<price>206.74</price>
</items>
<subtotal>1354.26</subtotal>
<tax>33.08</tax>
<total>766.28</total>
<payment_method>paypal</payment_method>
</transaction_record>


[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>
<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 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:
{
  "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.\nSkill 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 JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Generate api specification data in JSON format.

[Assistant]
Approach:
1. Task: Create api specification 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:
{
  "openapi": "3.0.0",
  "info": {
    "title": "Upgradable 4thgeneration middleware API",
    "version": "1.0.0"
  },
  "paths": {
    "/pass": {
      "get": {
        "summary": "Story amount nor bed night get message.",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "400": {
            "description": "Bad Request"
          }
        }
      }
    }
  }
}


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

[User] Generate lab result data in TOML format.

[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-6812810"
patient_mrn = "MRN-631747"
order_date = "2026-01-04"
panel = "Lipid"
[[results]]
test = "WBC"
value = 54.3
unit = "mg/dL"
flag = "high"

[[results]]
test = "WBC"
value = 70.8
unit = "mg/dL"
flag = "low"

[[results]]
test = "Glucose"
value = 59.6
unit = "mg/dL"
flag = "low"

[[results]]
test = "WBC"
value = 140.8
unit = "mg/dL"
flag = "high"


[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"?>
<lab_result>
<result_id>LAB-1056021</result_id>
<patient_mrn>MRN-977601</patient_mrn>
<order_date>2026-01-02</order_date>
<panel>CMP</panel>
<results>
<test>WBC</test>
<value>124.0</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>114.5</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>WBC</test>
<value>70.6</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Glucose</test>
<value>119.6</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>171.0</value>
<unit>mg/dL</unit>
<flag>normal</flag>
</results>
</lab_result>

[Assistant]
Approach:
1. Task: Convert lab result 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:
{
  "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 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: 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:
openapi = "3.0.0"

[info]
title = "Business-focused zero administration encryption API"
version = "1.0.0"

[paths."/teach".get]
summary = "Government trade direction personal reason hundred"
[[paths."/teach".get.parameters]]
name = "param0"
in = "query"
type = "string"

[[paths."/teach".get.parameters]]
name = "param1"
in = "query"
type = "string"

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

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


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

[User] Generate research paper data in TOML format.

[Assistant]
Approach:
1. Task: Create research paper 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:
paper_id = "arXiv:2021.87801"
title = "Cross-group 24/7 methodology"
abstract = "Reality maybe court material response cell. Middle instead husband win thousand collection."
keywords = [ "girl", "face", "protect",]
citations = 193
funding_source = "Williams Ltd"
[[authors]]
name = "Scott Webb"
affiliation = "Chapman LLC"
email = "juanbooth@example.com"

[[authors]]
name = "Robert Mullins"
affiliation = "Robbins, Adkins and Rogers"
email = "michaeldunn@example.org"

[publication]
venue = "Cell"
year = 2023
doi = "10.4960/byWV"


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

[User] Generate experiment result data in YAML format.

[Assistant]
Approach:
1. Task: Create experiment 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:
experiment_id: EXP-734376
title: Effect of probably on skin
methodology:
  design: cohort
  sample_size: 800
  duration_days: 273
results:
- metric: efficacy
  baseline: 94.92
  endpoint: 99.12
  p_value: 0.3163
- metric: safety
  baseline: 43.8
  endpoint: 81.58
  p_value: 0.1627
- metric: efficacy
  baseline: 31.59
  endpoint: 36.02
  p_value: 0.4619
- metric: efficacy
  baseline: 82.54
  endpoint: 59.85
  p_value: 0.3654
conclusion: significant


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

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

[Assistant]
Approach:
1. Task: Create student assessment 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:
assessment_id,code,course_id,credits,date,grade,grading_final,grading_homework,grading_midterm,instructor,percentage,schedule,scores,student_id,term,title,total_score,type
ASMT-482875,,CRS-2679,,2026-01-01,C,,,,,98.87,,"[{""question"": 1, ""points_earned"": 1, ""points_possible"": 10}, {""question"": 2, ""points_earned"": 9, ""po",STU-646713,,,62,assignment
,ECL178,CRS-7949,3,,,40,30,30,Jordan Sanchez,,"[{""week"": 1, ""topic"": ""benefit""}, {""week"": 2, ""topic"": ""someone""}, {""week"": 3, ""topic"": ""exist""}, {""",,,Fall 2023,Versatile incremental Graphic Interface 101,,
,YLF247,CRS-9884,3,,,40,30,30,Alan Hurley,,"[{""week"": 1, ""topic"": ""now""}, {""week"": 2, ""topic"": ""story""}, {""week"": 3, ""topic"": ""year""}, {""week"": ",,,Fall 2024,Face-to-face dynamic artificial intelligence 101,,
,UXD195,CRS-5870,3,,,40,30,30,Juan Thomas,,"[{""week"": 1, ""topic"": ""view""}, {""week"": 2, ""topic"": ""down""}, {""week"": 3, ""topic"": ""difficult""}, {""we",,,Spring 2022,De-engineered 6thgeneration database 101,,


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

[User]
Convert the following XML to TOML:
<?xml version="1.0" encoding="UTF-8"?>
<customer_review>
<helpful_votes>39</helpful_votes>
<date>2026-01-04</date>
<rating>5</rating>
<review_id>REV-1864702</review_id>
<title>State standard bad message use.</title>
</customer_review>

[Assistant]
Approach:
1. Task: Convert customer review from XML 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:
helpful_votes = 39
date = "2026-01-04"
rating = 5
review_id = "REV-1864702"
title = "State standard bad message use."


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

[User] Produce a CSV document for a lab result.

[Assistant]
Approach:
1. Task: Create lab result 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:
order_date,panel,patient_mrn,result_id,results
2026-01-01,Lipid,MRN-952425,LAB-8958063,"[{""test"": ""Glucose"", ""value"": 142.4, ""unit"": ""g/dL"", ""flag"": ""normal""}, {""test"": ""Hemoglobin"", ""valu"
2026-01-01,Lipid,MRN-713052,LAB-3738735,"[{""test"": ""WBC"", ""value"": 110.5, ""unit"": ""mg/dL"", ""flag"": ""normal""}, {""test"": ""WBC"", ""value"": 109.3,"
2026-01-05,Lipid,MRN-706888,LAB-2994829,"[{""test"": ""WBC"", ""value"": 70.8, ""unit"": ""mg/dL"", ""flag"": ""high""}, {""test"": ""Hemoglobin"", ""value"": 19"


[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:2024.67669",
  "title": "Triple-buffered dedicated knowledgebase",
  "authors": [
    {
      "name": "Julie Mclaughlin",
      "affiliation": "Andrews Group",
      "email": "brianna81@example.org"
    },
    {
      "name": "Peggy Kramer",
      "affiliation": "Beasley, Edwards and Webb",
      "email": "susan35@example.com"
    }
  ],
  "abstract": "Account fish possible democratic ok no. Accept short mention guy.\nPlace finish religious mean end success. Travel into music they.",
  "keywords": [
    "thank",
    "far",
    "fund",
    "get",
    "role"
  ],
  "publication": {
    "venue": "Nature",
    "year": 2023,
    "doi": "10.1349/JgWc"
  },
  "citations": 60,
  "funding_source": "Diaz-Salinas"
}

[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:2024.67669</paper_id>
<title>Triple-buffered dedicated knowledgebase</title>
<authors>
<name>Julie Mclaughlin</name>
<affiliation>Andrews Group</affiliation>
<email>brianna81@example.org</email>
</authors>
<authors>
<name>Peggy Kramer</name>
<affiliation>Beasley, Edwards and Webb</affiliation>
<email>susan35@example.com</email>
</authors>
<abstract>Account fish possible democratic ok no. Accept short mention guy.
Place finish religious mean end success. Travel into music they.</abstract>
<keywords>thank</keywords>
<keywords>far</keywords>
<keywords>fund</keywords>
<keywords>get</keywords>
<keywords>role</keywords>
<publication>
<venue>Nature</venue>
<year>2023</year>
<doi>10.1349/JgWc</doi>
</publication>
<citations>60</citations>
<funding_source>Diaz-Salinas</funding_source>
</research_paper>


[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"?>
<prescription>
<rx_id>RX-3305496</rx_id>
<patient_mrn>MRN-551064</patient_mrn>
<prescriber>Eric Jackson</prescriber>
<date>2026-01-03</date>
<medications>
<name>Atorvastatin</name>
<dosage>20mg</dosage>
<frequency>daily</frequency>
<quantity>90</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-3305496",
  "patient_mrn": "MRN-551064",
  "prescriber": "Eric Jackson",
  "date": "2026-01-03",
  "medications": [
    {
      "name": "Atorvastatin",
      "dosage": "20mg",
      "frequency": "daily",
      "quantity": 90
    }
  ]
}


[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:
{
  "term_months": 33,
  "parties": [
    {
      "name": "Hart-Villanueva",
      "role": "party_a"
    },
    {
      "name": "Roberts Ltd",
      "role": "party_b"
    }
  ],
  "effective_date": "2026-01-05",
  "value": 95653
}

[Assistant]
Approach:
1. Task: Convert contract 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"?>
<contract>
<term_months>33</term_months>
<parties>
<name>Hart-Villanueva</name>
<role>party_a</role>
</parties>
<parties>
<name>Roberts Ltd</name>
<role>party_b</role>
</parties>
<effective_date>2026-01-05</effective_date>
<value>95653</value>
</contract>


[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:
{
  "rx_id": "RX-1995080",
  "patient_mrn": "MRN-674224",
  "prescriber": "Emily Herrera",
  "date": "2026-01-05",
  "medications": [
    {
      "name": "Lisinopril",
      "dosage": "10mg",
      "frequency": "BID",
      "quantity": 60
    }
  ]
}

[Assistant]
Approach:
1. Task: Convert prescription 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"?>
<prescription>
<rx_id>RX-1995080</rx_id>
<patient_mrn>MRN-674224</patient_mrn>
<prescriber>Emily Herrera</prescriber>
<date>2026-01-05</date>
<medications>
<name>Lisinopril</name>
<dosage>10mg</dosage>
<frequency>BID</frequency>
<quantity>60</quantity>
</medications>
</prescription>


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

[User]
Convert the following JSON to XML:
{
  "error_id": "e78ff1cc-d984-499d-9fcf-126ccada9355",
  "timestamp": "2026-01-07T13:51:26.607679",
  "level": "WARNING",
  "service": "generationService",
  "message": "Bag difference growth have leader yeah him.",
  "stack_trace": [
    "at teach.py:30",
    "at poor.py:409"
  ],
  "context": {
    "user_id": "e8daac99-0bad-4fb0-ae26-78525528db85",
    "request_id": "a6fe9e0f-a1a6-42b2-ab77-98b8bb662a52"
  }
}

[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>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 TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User] Generate lab result data in TOML format.

[Assistant]
Approach:
1. Task: Create lab result 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:
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"

[[results]]
test = "Hemoglobin"
value = 100.6
unit = "mg/dL"
flag = "high"

[[results]]
test = "WBC"
value = 148.7
unit = "mg/dL"
flag = "high"

[[results]]
test = "Hemoglobin"
value = 95.1
unit = "g/dL"
flag = "normal"


[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": "91d16731-5e7b-4a47-8285-7a54d79d3b76",
  "timestamp": "2026-01-07T13:51:25.635487",
  "level": "WARNING",
  "service": "AmericanService",
  "message": "Drug sell certainly major total.",
  "stack_trace": [
    "at although.py:151",
    "at mission.py:443"
  ],
  "context": {
    "user_id": "11d85442-7276-4e30-9249-06df8089bd39",
    "request_id": "f3cc0b9c-f4db-4c9d-bea8-f49774b1a7e6"
  }
}

[Assistant]
Approach:
1. Task: Convert error log 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"?>
<error_log>
<error_id>91d16731-5e7b-4a47-8285-7a54d79d3b76</error_id>
<timestamp>2026-01-07T13:51:25.635487</timestamp>
<level>WARNING</level>
<service>AmericanService</service>
<message>Drug sell certainly major total.</message>
<stack_trace>at although.py:151</stack_trace>
<stack_trace>at mission.py:443</stack_trace>
<context>
<user_id>11d85442-7276-4e30-9249-06df8089bd39</user_id>
<request_id>f3cc0b9c-f4db-4c9d-bea8-f49774b1a7e6</request_id>
</context>
</error_log>


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

[User] Create a XML representation of product listing.

[Assistant]
Approach:
1. Task: Create product listing 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"?>
<product_listing>
<product_id>PROD-891981</product_id>
<name>Focused background info-mediaries</name>
<category>books</category>
<price>16.4</price>
<currency>USD</currency>
<stock>192</stock>
<is_available>True</is_available>
<rating>3.23</rating>
<reviews>117</reviews>
<specs>
<weight_kg>31.51</weight_kg>
<dimensions>65x39x62cm</dimensions>
</specs>
</product_listing>


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

[User] Generate contract data in TOML format.

[Assistant]
Approach:
1. Task: Create contract 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:
contract_id = "CTR-123472"
effective_date = "2026-01-06"
term_months = 59
value = 388976
status = "pending"
[[parties]]
name = "Palmer-White"
role = "party_a"

[[parties]]
name = "Rodriguez-Moore"
role = "party_b"


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

[User] Generate prescription data in TOML format.

[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-5160762"
patient_mrn = "MRN-568012"
prescriber = "Linda Gonzalez"
date = "2026-01-06"
[[medications]]
name = "Lisinopril"
dosage = "20mg"
frequency = "TID"
quantity = 30

[[medications]]
name = "Atorvastatin"
dosage = "5mg"
frequency = "daily"
quantity = 60


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

[User] Create a CSV representation of experiment result.

[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-734376,cohort,273,800,"[{""metric"": ""efficacy"", ""baseline"": 94.92, ""endpoint"": 99.12, ""p_value"": 0.3163}, {""metric"": ""safety",Effect of probably on skin
significant,EXP-564834,observational,61,866,"[{""metric"": ""safety"", ""baseline"": 24.65, ""endpoint"": 11.78, ""p_value"": 0.1767}, {""metric"": ""efficacy",Effect of range on national
non-significant,EXP-910883,observational,160,848,"[{""metric"": ""efficacy"", ""baseline"": 17.04, ""endpoint"": 55.95, ""p_value"": 0.2857}, {""metric"": ""effica",Effect of action on medical


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

[User] Generate social media post data in TOML format.

[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-23186111"
platform = "twitter"
user_id = "85951ac5-a9b8-4154-8edc-693bcde245cf"
username = "srodriguez"
content = "Stand leave decade year collection. Republican point edge yet business.\nSuffer accept central against. Cup theory mouth side message.\nConcern magazine exactly maybe oil difficult."
timestamp = "2026-01-07T13:51:25.750515"
hashtags = [ "#community", "#whatever",]

[engagement]
likes = 4745
shares = 251
comments = 51


[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-831490
name: Seamless optimal task-force
category: electronics
price: 975.8
currency: USD
stock: 924
is_available: true
rating: 2.71
reviews: 121
specs:
  weight_kg: 17.77
  dimensions: 52x27x46cm