[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:
{
  "net_income": 16280737,
  "revenue": 93235840,
  "metrics": {
    "profit_margin": 9.67,
    "roe": 5.79
  },
  "expenses": 6835352
}

[Assistant]
Approach:
1. Task: Convert financial report 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"?>
<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>


[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-8093004</result_id>
<patient_mrn>MRN-777027</patient_mrn>
<order_date>2026-01-05</order_date>
<panel>CMP</panel>
<results>
<test>Glucose</test>
<value>140.2</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>96.3</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Glucose</test>
<value>185.0</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Glucose</test>
<value>139.7</value>
<unit>g/dL</unit>
<flag>low</flag>
</results>
</lab_result>


[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:
{
  "openapi": "3.0.0",
  "info": {
    "title": "Seamless disintermediate flexibility API",
    "version": "1.0.0"
  },
  "paths": {
    "/significant": {
      "get": {
        "summary": "Box project skin day catch.",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "400": {
            "description": "Bad Request"
          }
        }
      }
    }
  }
}

[Assistant]
Approach:
1. Task: Convert api specification 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"?>
<api_specification>
<openapi>3.0.0</openapi>
<info>
<title>Seamless disintermediate flexibility API</title>
<version>1.0.0</version>
</info>
<paths>
</significant>
<get>
<summary>Box project skin day catch.</summary>
<parameters>
<name>param0</name>
<in>query</in>
<type>string</type>
</parameters>
<responses>
<200>
<description>Success</description>
</200>
<400>
<description>Bad Request</description>
</400>
</responses>
</get>
<//significant>
</paths>
</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:
{
  "paper_id": "arXiv:2022.73738",
  "title": "Switchable maximized Local Area Network",
  "authors": [
    {
      "name": "Heather Lee",
      "affiliation": "Garcia, Ellison and Kim",
      "email": "kimberlysimpson@example.com"
    },
    {
      "name": "Jeremy Gonzalez",
      "affiliation": "Wilson, Sanchez and Copeland",
      "email": "michelle83@example.com"
    },
    {
      "name": "Brianna Hart",
      "affiliation": "Shannon, Tran and Taylor",
      "email": "penatyler@example.net"
    }
  ],
  "abstract": "List son shake understand food. Up no police friend factor. Support company yes push argue.",
  "keywords": [
    "fact",
    "guess",
    "truth"
  ],
  "publication": {
    "venue": "Cell",
    "year": 2024,
    "doi": "10.7028/ttmb"
  },
  "citations": 36,
  "funding_source": "Guerra, Richards and Schaefer"
}

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<research_paper>
<paper_id>arXiv:2022.73738</paper_id>
<title>Switchable maximized Local Area Network</title>
<authors>
<name>Heather Lee</name>
<affiliation>Garcia, Ellison and Kim</affiliation>
<email>kimberlysimpson@example.com</email>
</authors>
<authors>
<name>Jeremy Gonzalez</name>
<affiliation>Wilson, Sanchez and Copeland</affiliation>
<email>michelle83@example.com</email>
</authors>
<authors>
<name>Brianna Hart</name>
<affiliation>Shannon, Tran and Taylor</affiliation>
<email>penatyler@example.net</email>
</authors>
<abstract>List son shake understand food. Up no police friend factor. Support company yes push argue.</abstract>
<keywords>fact</keywords>
<keywords>guess</keywords>
<keywords>truth</keywords>
<publication>
<venue>Cell</venue>
<year>2024</year>
<doi>10.7028/ttmb</doi>
</publication>
<citations>36</citations>
<funding_source>Guerra, Richards and Schaefer</funding_source>
</research_paper>


[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"?>
<sales_analytics>
<period>June 2020</period>
<total_revenue>2603625</total_revenue>
<units_sold>51574</units_sold>
<channels>
<name>retail</name>
<revenue>1926384</revenue>
<growth>34.29</growth>
</channels>
<channels>
<name>online</name>
<revenue>1062444</revenue>
<growth>-1.99</growth>
</channels>
<top_products>
<sku>SKU-3818</sku>
<sales>48786</sales>
</top_products>
<top_products>
<sku>SKU-7605</sku>
<sales>408187</sales>
</top_products>
<top_products>
<sku>SKU-6728</sku>
<sales>223732</sales>
</top_products>
</sales_analytics>

[Assistant]
Approach:
1. Task: Convert sales analytics 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:
{
  "period": "June 2020",
  "total_revenue": 2603625,
  "units_sold": 51574,
  "channels": [
    {
      "name": "retail",
      "revenue": 1926384,
      "growth": 34.29
    },
    {
      "name": "online",
      "revenue": 1062444,
      "growth": -1.99
    }
  ],
  "top_products": [
    {
      "sku": "SKU-3818",
      "sales": 48786
    },
    {
      "sku": "SKU-7605",
      "sales": 408187
    },
    {
      "sku": "SKU-6728",
      "sales": 223732
    }
  ]
}


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

[User]
Transform this CSV data into XML format:
abstract,authors,channels,citations,funding_source,keywords,paper_id,period,publication_doi,publication_venue,publication_year,title,top_products,total_revenue,units_sold
,,"[{""name"": ""online"", ""revenue"": 3952303, ""growth"": 2.75}, {""name"": ""retail"", ""revenue"": 3970412, ""gro",,,,,March 2024,,,,,"[{""sku"": ""SKU-2514"", ""sales"": 485558}, {""sku"": ""SKU-5241"", ""sales"": 142515}, {""sku"": ""SKU-4600"", ""sa",2433324,8686
Adult house daughter but short serious. Agree hand career mean.,"[{""name"": ""Steven Fernandez"", ""affiliation"": ""Gutierrez-Warren"", ""email"": ""qgibson@example.com""}, {""",,107,"Holland, Garcia and Owens","[""security"", ""present"", ""share"", ""yes"", ""heavy""]",arXiv:2021.72507,,10.3146/DNoC,Nature,2022,Networked needs-based approach,,,
Above health management board institution. Sell since while. Improve bit want shoulder course better,"[{""name"": ""James Petersen"", ""affiliation"": ""Arnold-Smith"", ""email"": ""brandon75@example.org""}, {""name",,170,"Rodriguez, Gonzales and Decker","[""offer"", ""team"", ""item""]",arXiv:2022.30243,,10.5369/wkrw,Cell,2022,Devolved asynchronous alliance,,,
Stage five grow somebody. Others simply firm writer boy central culture. Pass production stay actual,"[{""name"": ""Adrienne Edwards"", ""affiliation"": ""Smith-Hernandez"", ""email"": ""carlanixon@example.com""}, ",,121,Zimmerman PLC,"[""teacher"", ""top"", ""exactly""]",arXiv:2022.77777,,10.2791/oMYv,Science,2021,Digitized value-added alliance,,,
Guy decide general. Partner avoid pressure ten bit beat white blood.,"[{""name"": ""Yolanda Wells"", ""affiliation"": ""Stewart, Garcia and Chavez"", ""email"": ""stevensonrenee@exa",,211,White-Collins,"[""language"", ""article"", ""character"", ""argue""]",arXiv:2021.35384,,10.9321/SyHJ,Science,2022,Synchronized fault-tolerant forecast,,,

[Assistant]
Approach:
1. Task: Convert sales analytics from CSV 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"?>
<sales_analytics>
<period>March 2024</period>
<total_revenue>2433324</total_revenue>
<units_sold>8686</units_sold>
<channels>
<name>online</name>
<revenue>3952303</revenue>
<growth>2.75</growth>
</channels>
<channels>
<name>retail</name>
<revenue>3970412</revenue>
<growth>12.57</growth>
</channels>
<top_products>
<sku>SKU-2514</sku>
<sales>485558</sales>
</top_products>
<top_products>
<sku>SKU-5241</sku>
<sales>142515</sales>
</top_products>
<top_products>
<sku>SKU-4600</sku>
<sales>420300</sales>
</top_products>
</sales_analytics>


[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"?>
<financial_report>
<company>Brooks LLC</company>
<ticker>HFOB</ticker>
<quarter>Q1 2020</quarter>
<revenue>30917299</revenue>
<expenses>61265128</expenses>
<net_income>10426094</net_income>
<eps>3.56</eps>
<metrics>
<profit_margin>26.89</profit_margin>
<roe>20.49</roe>
</metrics>
</financial_report>

[Assistant]
Approach:
1. Task: Convert financial report 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:
{
  "company": "Brooks LLC",
  "ticker": "HFOB",
  "quarter": "Q1 2020",
  "revenue": 30917299,
  "expenses": 61265128,
  "net_income": 10426094,
  "eps": 3.56,
  "metrics": {
    "profit_margin": 26.89,
    "roe": 20.49
  }
}


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

[User] Generate contract data in CSV format.

[Assistant]
Approach:
1. Task: Create contract 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:
contract_id,effective_date,parties,status,term_months,value
CTR-760727,2026-01-04,"[{""name"": ""King, Moore and Vazquez"", ""role"": ""party_a""}, {""name"": ""Brown, Henry and Craig"", ""role"": ",active,15,170545
CTR-596416,2026-01-05,"[{""name"": ""Calderon-Bailey"", ""role"": ""party_a""}, {""name"": ""Peterson, Booth and Powell"", ""role"": ""par",pending,39,812307
CTR-642040,2026-01-06,"[{""name"": ""Lara-Cook"", ""role"": ""party_a""}, {""name"": ""Carter, Wilson and Woods"", ""role"": ""party_b""}]",pending,12,241013
CTR-879048,2026-01-04,"[{""name"": ""Ross-Carney"", ""role"": ""party_a""}, {""name"": ""Jackson, Khan and Peterson"", ""role"": ""party_b",pending,27,306753


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

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

[Assistant]
Approach:
1. Task: Create social media post 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:
{
  "post_id": "POST-93814090",
  "platform": "facebook",
  "user_id": "6a4446e9-5537-4ba1-bf03-5db2d36e07b9",
  "username": "tonyaguzman",
  "content": "Subject watch side owner new. Quality million clear hand clearly environmental. Forget face machine little check.",
  "timestamp": "2026-01-07T13:51:26.454281",
  "hashtags": [
    "#plan",
    "#area",
    "#foreign"
  ],
  "engagement": {
    "likes": 5322,
    "shares": 327,
    "comments": 152
  }
}


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

[User] Output financial report as YAML.

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

Output:
revenue: 65816825
ticker: IQTA
expenses: 77578182
company: Ramos-Wallace


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

[User] Create a XML representation of 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>6b141850-4372-44c1-8cf7-fc47780a5017</error_id>
<timestamp>2026-01-07T13:51:26.727039</timestamp>
<level>WARNING</level>
<service>fullService</service>
<message>Simply human maintain sport.</message>
<stack_trace>at environmental.py:351</stack_trace>
<stack_trace>at develop.py:28</stack_trace>
<stack_trace>at send.py:431</stack_trace>
<context>
<user_id>fad7b695-3fb8-4313-ad8d-c2b1d4034196</user_id>
<request_id>2ca5c81e-d6c8-42a4-9e09-9715e928fe91</request_id>
</context>
</error_log>


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

[User] Output experiment result as YAML.

[Assistant]
Approach:
1. Task: Create experiment result 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:
experiment_id: EXP-318732
title: Effect of produce on stock
methodology:
  design: observational
  sample_size: 303
  duration_days: 155
results:
- metric: safety
  baseline: 20.77
  endpoint: 67.03
  p_value: 0.1826
- metric: safety
  baseline: 69.69
  endpoint: 32.04
  p_value: 0.1176
conclusion: non-significant


[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:
{
  "post_id": "POST-93814090",
  "platform": "facebook",
  "user_id": "6a4446e9-5537-4ba1-bf03-5db2d36e07b9",
  "username": "tonyaguzman",
  "content": "Subject watch side owner new. Quality million clear hand clearly environmental. Forget face machine little check.",
  "timestamp": "2026-01-07T13:51:26.454281",
  "hashtags": [
    "#plan",
    "#area",
    "#foreign"
  ],
  "engagement": {
    "likes": 5322,
    "shares": 327,
    "comments": 152
  }
}

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

Output:
post_id = "POST-93814090"
platform = "facebook"
user_id = "6a4446e9-5537-4ba1-bf03-5db2d36e07b9"
username = "tonyaguzman"
content = "Subject watch side owner new. Quality million clear hand clearly environmental. Forget face machine little check."
timestamp = "2026-01-07T13:51:26.454281"
hashtags = [ "#plan", "#area", "#foreign",]

[engagement]
likes = 5322
shares = 327
comments = 152


[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-986141
name: Focused neutral encoding
category: books
price: 14.45
currency: USD
stock: 826
is_available: true
rating: 4.59
reviews: 564
specs:
  weight_kg: 27.7
  dimensions: 42x99x90cm


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

[User] Create a TOML representation of product listing.

[Assistant]
Approach:
1. Task: Create product listing 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:
product_id = "PROD-469887"
is_available = true
reviews = 612
name = "Networked executive capability"
currency = "USD"


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

[User] Output product listing as JSON.

[Assistant]
Approach:
1. Task: Create product listing 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:
{
  "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 TOML format. Think step-by-step, then generate syntactically perfect TOML.

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

[Assistant]
Approach:
1. Task: Create lab result 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:
patient_mrn = "MRN-135092"
order_date = "2026-01-02"
panel = "CBC"
result_id = "LAB-9224780"
[[results]]
test = "Hemoglobin"
value = 129.6
unit = "g/dL"
flag = "low"

[[results]]
test = "Hemoglobin"
value = 120.7
unit = "mg/dL"
flag = "low"

[[results]]
test = "Hemoglobin"
value = 141.8
unit = "g/dL"
flag = "low"

[[results]]
test = "Hemoglobin"
value = 131.3
unit = "g/dL"
flag = "low"

[[results]]
test = "Glucose"
value = 147.1
unit = "mg/dL"
flag = "high"


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

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

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

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


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

[User] Create a XML representation of experiment result.

[Assistant]
Approach:
1. Task: Create experiment 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"?>
<experiment_result>
<experiment_id>EXP-139463</experiment_id>
<title>Effect of quite on arm</title>
<methodology>
<design>RCT</design>
<sample_size>371</sample_size>
<duration_days>284</duration_days>
</methodology>
<results>
<metric>safety</metric>
<baseline>91.61</baseline>
<endpoint>76.9</endpoint>
<p_value>0.4003</p_value>
</results>
<results>
<metric>safety</metric>
<baseline>58.96</baseline>
<endpoint>23.83</endpoint>
<p_value>0.3437</p_value>
</results>
<results>
<metric>safety</metric>
<baseline>23.31</baseline>
<endpoint>88.63</endpoint>
<p_value>0.3018</p_value>
</results>
<conclusion>non-significant</conclusion>
</experiment_result>


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

[User]
Convert the following JSON to XML:
{
  "company": "Hill-Marsh",
  "ticker": "ZFVR",
  "quarter": "Q3 2024",
  "revenue": 46703834,
  "expenses": 77071880,
  "net_income": 4141173,
  "eps": 8.71,
  "metrics": {
    "profit_margin": 28.84,
    "roe": 14.06
  }
}

[Assistant]
Approach:
1. Task: Convert financial report 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"?>
<financial_report>
<company>Hill-Marsh</company>
<ticker>ZFVR</ticker>
<quarter>Q3 2024</quarter>
<revenue>46703834</revenue>
<expenses>77071880</expenses>
<net_income>4141173</net_income>
<eps>8.71</eps>
<metrics>
<profit_margin>28.84</profit_margin>
<roe>14.06</roe>
</metrics>
</financial_report>


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

[User]
Reformat the data below from XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<prescription>
<rx_id>RX-3022361</rx_id>
<patient_mrn>MRN-726192</patient_mrn>
<prescriber>Jessica Warner</prescriber>
<date>2026-01-02</date>
<medications>
<name>Atorvastatin</name>
<dosage>20mg</dosage>
<frequency>BID</frequency>
<quantity>90</quantity>
</medications>
<medications>
<name>Atorvastatin</name>
<dosage>20mg</dosage>
<frequency>daily</frequency>
<quantity>30</quantity>
</medications>
</prescription>

[Assistant]
Approach:
1. Task: Convert prescription from XML to JSON
2. Complexity: 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-3022361",
  "patient_mrn": "MRN-726192",
  "prescriber": "Jessica Warner",
  "date": "2026-01-02",
  "medications": [
    {
      "name": "Atorvastatin",
      "dosage": "20mg",
      "frequency": "BID",
      "quantity": 90
    },
    {
      "name": "Atorvastatin",
      "dosage": "20mg",
      "frequency": "daily",
      "quantity": 30
    }
  ]
}


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

[User]
Transform this CSV data into TOML format:
assessment,chief_complaint,encounter_date,note_id,patient_age,patient_gender,patient_mrn,vitals_bp,vitals_hr,vitals_temp_f
"[{""problem"": ""diabetes"", ""icd10"": ""J94""}]",headache,2026-01-04,CN-3766387,48,F,MRN-378219,145/101,106,101.7
"[{""problem"": ""diabetes"", ""icd10"": ""K38""}, {""problem"": ""hypertension"", ""icd10"": ""J53""}, {""problem"": """,fever,2026-01-01,CN-6970690,45,M,MRN-828020,175/73,92,98.2
"[{""problem"": ""diabetes"", ""icd10"": ""I91""}, {""problem"": ""diabetes"", ""icd10"": ""I93""}]",chest pain,2026-01-02,CN-3359599,68,F,MRN-329241,112/87,65,97.6
"[{""problem"": ""diabetes"", ""icd10"": ""J14""}, {""problem"": ""diabetes"", ""icd10"": ""K84""}]",headache,2026-01-02,CN-4992215,32,F,MRN-181838,143/68,95,102.9
"[{""problem"": ""diabetes"", ""icd10"": ""J81""}, {""problem"": ""hypertension"", ""icd10"": ""J94""}, {""problem"": """,fever,2026-01-02,CN-1470790,43,F,MRN-970521,143/109,67,101.3

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

Output:
note_id = "CN-3766387"
encounter_date = "2026-01-04"
chief_complaint = "headache"
[[assessment]]
problem = "diabetes"
icd10 = "J94"

[patient]
mrn = "MRN-378219"
age = 48
gender = "F"

[vitals]
bp = "145/101"
hr = 106
temp_f = 101.7


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

[User] Create a JSON representation of sales analytics.

[Assistant]
Approach:
1. Task: Create sales analytics 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:
{
  "period": "September 2024",
  "total_revenue": 6726526,
  "units_sold": 45392,
  "channels": [
    {
      "name": "retail",
      "revenue": 2051641,
      "growth": 15.93
    },
    {
      "name": "wholesale",
      "revenue": 4340607,
      "growth": -6.59
    }
  ],
  "top_products": [
    {
      "sku": "SKU-7798",
      "sales": 367269
    },
    {
      "sku": "SKU-2105",
      "sales": 291023
    },
    {
      "sku": "SKU-5730",
      "sales": 275348
    }
  ]
}


[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"?>
<error_log>
<error_id>d03afc03-94bd-4dbb-bf49-9cda441c119b</error_id>
<timestamp>2026-01-07T13:51:25.952127</timestamp>
<level>CRITICAL</level>
<service>fromService</service>
<message>Add perform politics some card score brother.</message>
<stack_trace>at send.py:152</stack_trace>
<stack_trace>at young.py:309</stack_trace>
<context>
<user_id>2b3e5573-7b69-4807-a899-cbaa4adadf38</user_id>
<request_id>beac9c14-4ae5-407d-9aae-d7a56f6790f6</request_id>
</context>
</error_log>

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

Output:
error_id = "d03afc03-94bd-4dbb-bf49-9cda441c119b"
timestamp = "2026-01-07T13:51:25.952127"
level = "CRITICAL"
service = "fromService"
message = "Add perform politics some card score brother."
stack_trace = [ "at send.py:152", "at young.py:309",]

[context]
user_id = "2b3e5573-7b69-4807-a899-cbaa4adadf38"
request_id = "beac9c14-4ae5-407d-9aae-d7a56f6790f6"


[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"?>
<transaction_record>
<txn_id>TXN-59995807</txn_id>
<customer_id>0d66cadd-c42f-4e9b-ae40-46d71f47623d</customer_id>
<timestamp>2026-01-07T13:51:27.084892</timestamp>
<items>
<sku>SKU-83766</sku>
<qty>4</qty>
<price>55.35</price>
</items>
<items>
<sku>SKU-77209</sku>
<qty>2</qty>
<price>217.97</price>
</items>
<items>
<sku>SKU-11210</sku>
<qty>5</qty>
<price>436.04</price>
</items>
<subtotal>1600.85</subtotal>
<tax>34.84</tax>
<total>602.79</total>
<payment_method>apple_pay</payment_method>
</transaction_record>

[Assistant]
Approach:
1. Task: Convert transaction record 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:
{
  "txn_id": "TXN-59995807",
  "customer_id": "0d66cadd-c42f-4e9b-ae40-46d71f47623d",
  "timestamp": "2026-01-07T13:51:27.084892",
  "items": [
    {
      "sku": "SKU-83766",
      "qty": 4,
      "price": 55.35
    },
    {
      "sku": "SKU-77209",
      "qty": 2,
      "price": 217.97
    },
    {
      "sku": "SKU-11210",
      "qty": 5,
      "price": 436.04
    }
  ],
  "subtotal": 1600.85,
  "tax": 34.84,
  "total": 602.79,
  "payment_method": "apple_pay"
}


[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:
authors:
- name: Laura White
  affiliation: Coleman, Johnson and Ross
  email: jillcollins@example.com
- name: Sarah Woods
  affiliation: Durham, Thompson and White
  email: sbeard@example.com
title: Seamless cohesive projection
funding_source: null
abstract: Middle several interesting move. Wrong represent guess threat. Once paper
  represent.

[Assistant]
Approach:
1. Task: Convert research paper from YAML 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"?>
<research_paper>
<authors>
<name>Laura White</name>
<affiliation>Coleman, Johnson and Ross</affiliation>
<email>jillcollins@example.com</email>
</authors>
<authors>
<name>Sarah Woods</name>
<affiliation>Durham, Thompson and White</affiliation>
<email>sbeard@example.com</email>
</authors>
<title>Seamless cohesive projection</title>
<funding_source />
<abstract>Middle several interesting move. Wrong represent guess threat. Once paper represent.</abstract>
</research_paper>


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

[User]
Convert the following JSON to XML:
{
  "company": "Nichols, Ferguson and Butler",
  "ticker": "NDVN",
  "quarter": "Q1 2022",
  "revenue": 39470251,
  "expenses": 26562838,
  "net_income": 16784469,
  "eps": 3.2,
  "metrics": {
    "profit_margin": 27.93,
    "roe": 5.96
  }
}

[Assistant]
Approach:
1. Task: Convert financial report 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"?>
<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 XML format. Think step-by-step, then generate syntactically perfect XML.

[User] Generate sales analytics data in XML format.

[Assistant]
Approach:
1. Task: Create sales analytics 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"?>
<sales_analytics>
<period>February 2020</period>
<total_revenue>2337421</total_revenue>
<units_sold>23839</units_sold>
<channels>
<name>retail</name>
<revenue>3840938</revenue>
<growth>18.52</growth>
</channels>
<channels>
<name>wholesale</name>
<revenue>1512471</revenue>
<growth>13.02</growth>
</channels>
<top_products>
<sku>SKU-1744</sku>
<sales>276633</sales>
</top_products>
<top_products>
<sku>SKU-5145</sku>
<sales>26445</sales>
</top_products>
<top_products>
<sku>SKU-9218</sku>
<sales>412054</sales>
</top_products>
</sales_analytics>


[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:
{
  "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"
  }
}

[Assistant]
Approach:
1. Task: Convert product listing 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"?>
<product_listing>
<product_id>PROD-125540</product_id>
<name>Universal eco-centric approach</name>
<category>books</category>
<price>208.68</price>
<currency>USD</currency>
<stock>879</stock>
<is_available>True</is_available>
<rating>4.53</rating>
<reviews>925</reviews>
<specs>
<weight_kg>20.35</weight_kg>
<dimensions>45x91x83cm</dimensions>
</specs>
</product_listing>


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

[User] Output api specification as TOML.

[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 = "Face-to-face zero administration application API"
version = "1.0.0"

[paths."/peace".get]
summary = "Feel piece low way effort action bag."
[[paths."/peace".get.parameters]]
name = "param0"
in = "query"
type = "string"

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

[[paths."/peace".get.parameters]]
name = "param2"
in = "query"
type = "string"

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

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


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

[User] Generate course syllabus data in YAML format.

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

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


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

[User] Output product listing as JSON.

[Assistant]
Approach:
1. Task: Create product listing 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:
{
  "product_id": "PROD-319304",
  "name": "Self-enabling clear-thinking help-desk",
  "category": "clothing",
  "price": 510.64,
  "currency": "USD",
  "stock": 595,
  "is_available": true,
  "rating": 2.22,
  "reviews": 791,
  "specs": {
    "weight_kg": 30.42,
    "dimensions": "12x58x39cm"
  }
}


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

[User]
Transform this TOML data into YAML format:
course_id = "CRS-5570"
code = "ZMD258"
title = "Mandatory leadingedge superstructure 101"
instructor = "Gregory Dunn"
credits = 3
term = "Spring 2021"
[[schedule]]
week = 1
topic = "guess"

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

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

[[schedule]]
week = 4
topic = "nothing"

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

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

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

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

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

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

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

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

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

Output:
course_id: CRS-5570
code: ZMD258
title: Mandatory leadingedge superstructure 101
instructor: Gregory Dunn
credits: 3
term: Spring 2021
schedule:
- week: 1
  topic: guess
- week: 2
  topic: forget
- week: 3
  topic: technology
- week: 4
  topic: nothing
- week: 5
  topic: sea
- week: 6
  topic: other
- week: 7
  topic: new
- week: 8
  topic: report
- week: 9
  topic: guy
- week: 10
  topic: can
- week: 11
  topic: inside
grading:
  homework: 30
  midterm: 30
  final: 40


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

[User] Produce a XML document for a 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-5347545</article_id>
<headline>Triple-buffered zero administration algorithm</headline>
<author>Martha Brown</author>
<published>2026-01-05T16:28:34.342789</published>

Sports

<content>Have fall sing then fast research break. Bar ready eye medical school might recognize. Nice sometimes impact point white.
Take many this never ever trip. Specific call according. We argue person federal central each.</content>
<tags>line</tags>
<tags>true</tags>
<tags>compare</tags>
<tags>for</tags>
<views>96946</views>
<shares>1479</shares>
</news_article>


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

[User] Produce a TOML document for a prescription.

[Assistant]
Approach:
1. Task: Create prescription 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:
date = "2026-01-04"
patient_mrn = "MRN-588828"
[[medications]]
name = "Atorvastatin"
dosage = "20mg"
frequency = "TID"
quantity = 60

[[medications]]
name = "Metformin"
dosage = "20mg"
frequency = "daily"
quantity = 60

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


[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"?>
<student_assessment>
<total_score>96</total_score>
<percentage>61.11</percentage>
<student_id>STU-785727</student_id>
</student_assessment>

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

Output:
{
  "total_score": 96,
  "percentage": 61.11,
  "student_id": "STU-785727"
}


[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-1129442</note_id>
<patient>
<mrn>MRN-895859</mrn>
<age>79</age>
<gender>M</gender>
</patient>
<encounter_date>2026-01-03</encounter_date>
<chief_complaint>headache</chief_complaint>
<vitals>
<bp>127/81</bp>

117

<temp_f>102.4</temp_f>
</vitals>
<assessment>
<problem>hypertension</problem>
<icd10>J46</icd10>
</assessment>
</clinical_note>

[Assistant]
Approach:
1. Task: Convert clinical note 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:
{
  "note_id": "CN-1129442",
  "patient": {
    "mrn": "MRN-895859",
    "age": 79,
    "gender": "M"
  },
  "encounter_date": "2026-01-03",
  "chief_complaint": "headache",
  "vitals": {
    "bp": "127/81",
    "hr": 117,
    "temp_f": 102.4
  },
  "assessment": [
    {
      "problem": "hypertension",
      "icd10": "J46"
    }
  ]
}


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

[User] Generate student assessment data in TOML format.

[Assistant]
Approach:
1. Task: Create student assessment 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:
assessment_id = "ASMT-458464"
student_id = "STU-453066"
course_id = "CRS-7675"
type = "assignment"
date = "2026-01-06"
total_score = 67
percentage = 65.79
grade = "B"
[[scores]]
question = 1
points_earned = 0
points_possible = 10

[[scores]]
question = 2
points_earned = 9
points_possible = 10

[[scores]]
question = 3
points_earned = 2
points_possible = 10

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

[[scores]]
question = 5
points_earned = 4
points_possible = 10

[[scores]]
question = 6
points_earned = 0
points_possible = 10

[[scores]]
question = 7
points_earned = 3
points_possible = 10

[[scores]]
question = 8
points_earned = 7
points_possible = 10

[[scores]]
question = 9
points_earned = 8
points_possible = 10


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

[User]
Convert the following JSON to XML:
{
  "period": "July 2021",
  "total_revenue": 5979422,
  "units_sold": 82076,
  "channels": [
    {
      "name": "retail",
      "revenue": 3460112,
      "growth": 36.85
    },
    {
      "name": "online",
      "revenue": 3983364,
      "growth": 5.55
    }
  ],
  "top_products": [
    {
      "sku": "SKU-1065",
      "sales": 105510
    },
    {
      "sku": "SKU-4043",
      "sales": 281142
    },
    {
      "sku": "SKU-1338",
      "sales": 43006
    }
  ]
}

[Assistant]
Approach:
1. Task: Convert sales analytics 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"?>
<sales_analytics>
<period>July 2021</period>
<total_revenue>5979422</total_revenue>
<units_sold>82076</units_sold>
<channels>
<name>retail</name>
<revenue>3460112</revenue>
<growth>36.85</growth>
</channels>
<channels>
<name>online</name>
<revenue>3983364</revenue>
<growth>5.55</growth>
</channels>
<top_products>
<sku>SKU-1065</sku>
<sales>105510</sales>
</top_products>
<top_products>
<sku>SKU-4043</sku>
<sales>281142</sales>
</top_products>
<top_products>
<sku>SKU-1338</sku>
<sales>43006</sales>
</top_products>
</sales_analytics>


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

[User] Create a YAML representation of contract.

[Assistant]
Approach:
1. Task: Create contract 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:
contract_id: CTR-760727
parties:
- name: King, Moore and Vazquez
  role: party_a
- name: Brown, Henry and Craig
  role: party_b
effective_date: '2026-01-04'
term_months: 15
value: 170545
status: active


[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:
{
  "product_id": "PROD-891981",
  "name": "Focused background info-mediaries",
  "category": "books",
  "price": 16.4,
  "currency": "USD",
  "stock": 192,
  "is_available": true,
  "rating": 3.23,
  "reviews": 117,
  "specs": {
    "weight_kg": 31.51,
    "dimensions": "65x39x62cm"
  }
}

[Assistant]
Approach:
1. Task: Convert product listing 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"?>
<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 CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User]
Transform this JSON data into CSV format:
{
  "product_id": "PROD-651261",
  "name": "Cross-platform scalable protocol",
  "category": "clothing",
  "price": 855.77,
  "currency": "USD",
  "stock": 343,
  "is_available": true,
  "rating": 3.23,
  "reviews": 830,
  "specs": {
    "weight_kg": 21.29,
    "dimensions": "84x20x91cm"
  }
}

[Assistant]
Approach:
1. Task: Convert product listing 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:
category,currency,is_available,name,price,product_id,rating,reviews,specs_dimensions,specs_weight_kg,stock
clothing,USD,True,Cross-platform scalable protocol,855.77,PROD-651261,3.23,830,84x20x91cm,21.29,343
books,USD,True,Self-enabling directional support,309.79,PROD-823306,4.01,945,64x69x14cm,12.01,784
electronics,USD,True,Face-to-face composite Graphical User Interface,986.21,PROD-772057,3.76,265,95x63x34cm,21.26,233


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

[User] Output clinical note as JSON.

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

Output:
{
  "vitals": {
    "bp": "131/104",
    "hr": 84,
    "temp_f": 99.9
  },
  "note_id": "CN-3823498",
  "encounter_date": "2026-01-06"
}


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

[User] Output sales analytics as TOML.

[Assistant]
Approach:
1. Task: Create sales analytics 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:
period = "November 2021"
total_revenue = 8992427
units_sold = 30636
[[channels]]
name = "wholesale"
revenue = 3169564
growth = 42.35

[[channels]]
name = "retail"
revenue = 2683860
growth = 36.55

[[top_products]]
sku = "SKU-9614"
sales = 412171

[[top_products]]
sku = "SKU-7573"
sales = 203488

[[top_products]]
sku = "SKU-4372"
sales = 55896


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

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

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

Output:
company = "Hardy-Nguyen"
ticker = "ZAMS"
quarter = "Q3 2024"
revenue = 23201336
expenses = 30350118
net_income = 14673850
eps = 3.22

[metrics]
profit_margin = 18.82
roe = 21.21


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

[User] Output product listing as XML.

[Assistant]
Approach:
1. Task: Create product listing 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"?>
<product_listing>
<product_id>PROD-469887</product_id>
<is_available>True</is_available>
<reviews>612</reviews>
<name>Networked executive capability</name>
<currency>USD</currency>
</product_listing>


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

[User] Create a XML representation of student assessment.

[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-485879</assessment_id>
<student_id>STU-284737</student_id>
<course_id>CRS-1053</course_id>
<type>exam</type>
<date>2026-01-03</date>
<scores>
<question>1</question>
<points_earned>0</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>7</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>4</question>
<points_earned>9</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>9</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>7</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<total_score>89</total_score>
<percentage>64.04</percentage>
<grade>A</grade>
</student_assessment>


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

[User]
Reformat the data below from XML to TOML:
<?xml version="1.0" encoding="UTF-8"?>
<experiment_result>
<experiment_id>EXP-318732</experiment_id>
<title>Effect of produce on stock</title>
<methodology>
<design>observational</design>
<sample_size>303</sample_size>
<duration_days>155</duration_days>
</methodology>
<results>
<metric>safety</metric>
<baseline>20.77</baseline>
<endpoint>67.03</endpoint>
<p_value>0.1826</p_value>
</results>
<results>
<metric>safety</metric>
<baseline>69.69</baseline>
<endpoint>32.04</endpoint>
<p_value>0.1176</p_value>
</results>
<conclusion>non-significant</conclusion>
</experiment_result>

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

Output:
experiment_id = "EXP-318732"
title = "Effect of produce on stock"
conclusion = "non-significant"
[[results]]
metric = "safety"
baseline = 20.77
endpoint = 67.03
p_value = 0.1826

[[results]]
metric = "safety"
baseline = 69.69
endpoint = 32.04
p_value = 0.1176

[methodology]
design = "observational"
sample_size = 303
duration_days = 155


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

[User]
Reformat the data below from JSON to XML:
{
  "experiment_id": "EXP-725280",
  "title": "Effect of city on expert",
  "methodology": {
    "design": "observational",
    "sample_size": 907,
    "duration_days": 166
  },
  "results": [
    {
      "metric": "safety",
      "baseline": 43.19,
      "endpoint": 64.81,
      "p_value": 0.4813
    },
    {
      "metric": "safety",
      "baseline": 71.73,
      "endpoint": 68.14,
      "p_value": 0.0851
    },
    {
      "metric": "safety",
      "baseline": 77.12,
      "endpoint": 63.9,
      "p_value": 0.1154
    }
  ],
  "conclusion": "significant"
}

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<experiment_result>
<experiment_id>EXP-725280</experiment_id>
<title>Effect of city on expert</title>
<methodology>
<design>observational</design>
<sample_size>907</sample_size>
<duration_days>166</duration_days>
</methodology>
<results>
<metric>safety</metric>
<baseline>43.19</baseline>
<endpoint>64.81</endpoint>
<p_value>0.4813</p_value>
</results>
<results>
<metric>safety</metric>
<baseline>71.73</baseline>
<endpoint>68.14</endpoint>
<p_value>0.0851</p_value>
</results>
<results>
<metric>safety</metric>
<baseline>77.12</baseline>
<endpoint>63.9</endpoint>
<p_value>0.1154</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] Produce a XML document for a product listing.

[Assistant]
Approach:
1. Task: Create product listing 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"?>
<product_listing>
<product_id>PROD-319304</product_id>
<name>Self-enabling clear-thinking help-desk</name>
<category>clothing</category>
<price>510.64</price>
<currency>USD</currency>
<stock>595</stock>
<is_available>True</is_available>
<rating>2.22</rating>
<reviews>791</reviews>
<specs>
<weight_kg>30.42</weight_kg>
<dimensions>12x58x39cm</dimensions>
</specs>
</product_listing>


[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"?>
<error_log>
<error_id>21bf982e-3e24-486f-a8ed-fe9e0227df3b</error_id>
<timestamp>2026-01-07T13:51:27.797574</timestamp>
<level>CRITICAL</level>
<service>letService</service>
<message>Story quite another important.</message>
<stack_trace>at decision.py:100</stack_trace>
<stack_trace>at sing.py:289</stack_trace>
<stack_trace>at long.py:325</stack_trace>
<stack_trace>at special.py:174</stack_trace>
<context>
<user_id>8c36ba55-183c-4481-9cd9-dc049447888c</user_id>
<request_id>089e0eaa-71d8-4f30-a2f1-bac42a6b74a3</request_id>
</context>
</error_log>

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

Output:
{
  "error_id": "21bf982e-3e24-486f-a8ed-fe9e0227df3b",
  "timestamp": "2026-01-07T13:51:27.797574",
  "level": "CRITICAL",
  "service": "letService",
  "message": "Story quite another important.",
  "stack_trace": [
    "at decision.py:100",
    "at sing.py:289",
    "at long.py:325",
    "at special.py:174"
  ],
  "context": {
    "user_id": "8c36ba55-183c-4481-9cd9-dc049447888c",
    "request_id": "089e0eaa-71d8-4f30-a2f1-bac42a6b74a3"
  }
}


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

[Assistant]
Approach:
1. Task: Convert course syllabus 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"?>
<course_syllabus>
<course_id>CRS-2281</course_id>
NHA456
<title>Profit-focused mission-critical hardware 101</title>
<instructor>Natalie Middleton</instructor>
<credits>3</credits>
<term>Spring 2024</term>
<schedule>
<week>1</week>
<topic>its</topic>
</schedule>
<schedule>
<week>2</week>
<topic>attorney</topic>
</schedule>
<schedule>
<week>3</week>
<topic>decision</topic>
</schedule>
<schedule>
<week>4</week>
<topic>every</topic>
</schedule>
<schedule>
<week>5</week>
<topic>direction</topic>
</schedule>
<schedule>
<week>6</week>
<topic>player</topic>
</schedule>
<schedule>
<week>7</week>
<topic>city</topic>
</schedule>
<schedule>
<week>8</week>
<topic>everybody</topic>
</schedule>
<schedule>
<week>9</week>
<topic>throw</topic>
</schedule>
<grading>
<homework>30</homework>
<midterm>30</midterm>
<final>40</final>
</grading>
</course_syllabus>


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

[User]
Reformat the data below from XML to CSV:
<?xml version="1.0" encoding="UTF-8"?>
<lab_result>
<result_id>LAB-5490961</result_id>
<patient_mrn>MRN-709917</patient_mrn>
<order_date>2026-01-03</order_date>
<panel>CBC</panel>
<results>
<test>Glucose</test>
<value>146.9</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>119.4</value>
<unit>g/dL</unit>
<flag>low</flag>
</results>
<results>
<test>WBC</test>
<value>95.1</value>
<unit>g/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>73.3</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Glucose</test>
<value>166.8</value>
<unit>g/dL</unit>
<flag>low</flag>
</results>
<results>
<test>WBC</test>
<value>98.8</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
</lab_result>

[Assistant]
Approach:
1. Task: Convert lab result from XML 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:
order_date,panel,patient_mrn,result_id,results
2026-01-03,CBC,MRN-709917,LAB-5490961,"[{""test"": ""Glucose"", ""value"": 146.9, ""unit"": ""mg/dL"", ""flag"": ""high""}, {""test"": ""Hemoglobin"", ""value"
2026-01-01,Lipid,MRN-784519,LAB-7365113,"[{""test"": ""Hemoglobin"", ""value"": 128.0, ""unit"": ""g/dL"", ""flag"": ""low""}, {""test"": ""WBC"", ""value"": 138"
2026-01-02,Lipid,MRN-900856,LAB-7497135,"[{""test"": ""WBC"", ""value"": 59.2, ""unit"": ""g/dL"", ""flag"": ""low""}, {""test"": ""Hemoglobin"", ""value"": 166."
2026-01-03,Lipid,MRN-244900,LAB-9124295,"[{""test"": ""Glucose"", ""value"": 70.6, ""unit"": ""g/dL"", ""flag"": ""normal""}, {""test"": ""Hemoglobin"", ""value"
2026-01-05,CMP,MRN-120813,LAB-9197518,"[{""test"": ""Hemoglobin"", ""value"": 190.8, ""unit"": ""g/dL"", ""flag"": ""high""}, {""test"": ""Glucose"", ""value"""


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

[User]
Transform this CSV data into JSON format:
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
0f5f44c7-a7ee-4771-9782-a0ce49d19d9d,"[{""sku"": ""SKU-71416"", ""qty"": 5, ""price"": 358.25}, {""sku"": ""SKU-48401"", ""qty"": 5, ""price"": 347.62}]",credit_card,1686.61,105.4,2026-01-07T13:51:27.693600,225.1,TXN-76158616
60a95618-5223-4bb6-90f2-184da8bd2a94,"[{""sku"": ""SKU-51530"", ""qty"": 4, ""price"": 494.35}, {""sku"": ""SKU-26339"", ""qty"": 1, ""price"": 48.67}]",apple_pay,1281.1,158.59,2026-01-07T13:51:27.693673,1134.89,TXN-57676141

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

Output:
{
  "txn_id": "TXN-95106885",
  "customer_id": "9090a0a6-3421-4a93-9d6e-78b6a1f8fed7",
  "timestamp": "2026-01-07T13:51:27.692524",
  "items": [
    {
      "sku": "SKU-33525",
      "qty": 3,
      "price": 130.6
    }
  ],
  "subtotal": 793.41,
  "tax": 85.16,
  "total": 1645.24,
  "payment_method": "apple_pay"
}


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

[User] Generate transaction record data in YAML format.

[Assistant]
Approach:
1. Task: Create transaction record 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:
txn_id: TXN-31892506
customer_id: 6c5d6d8e-2901-438d-be6d-be57a13ea5ab
timestamp: '2026-01-07T13:51:25.485684'
items:
- sku: SKU-80058
  qty: 4
  price: 142.37
- sku: SKU-53232
  qty: 1
  price: 151.54
subtotal: 692.94
tax: 100.91
total: 1647.14
payment_method: credit_card


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

[User]
Transform this YAML data into JSON format:
contract_id: CTR-464030
parties:
- name: Morales, Williams and Williams
  role: party_a
- name: Ward-Miller
  role: party_b
effective_date: '2026-01-06'
term_months: 35
value: 994957
status: active

[Assistant]
Approach:
1. Task: Convert contract 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:
{
  "contract_id": "CTR-464030",
  "parties": [
    {
      "name": "Morales, Williams and Williams",
      "role": "party_a"
    },
    {
      "name": "Ward-Miller",
      "role": "party_b"
    }
  ],
  "effective_date": "2026-01-06",
  "term_months": 35,
  "value": 994957,
  "status": "active"
}


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

[User] Create a CSV representation of transaction record.

[Assistant]
Approach:
1. Task: Create transaction record in CSV
2. Complexity: 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:
customer_id,items,payment_method,subtotal,tax,timestamp,total,txn_id
6c5d6d8e-2901-438d-be6d-be57a13ea5ab,"[{""sku"": ""SKU-80058"", ""qty"": 4, ""price"": 142.37}, {""sku"": ""SKU-53232"", ""qty"": 1, ""price"": 151.54}]",credit_card,692.94,100.91,2026-01-07T13:51:25.485684,1647.14,TXN-31892506
190a0692-5311-4054-9697-eacf0b177417,"[{""sku"": ""SKU-15558"", ""qty"": 2, ""price"": 227.29}, {""sku"": ""SKU-43656"", ""qty"": 1, ""price"": 238.5}, {""",credit_card,1915.2,116.94,2026-01-07T13:51:25.486612,1924.5,TXN-18209031
ecac2df7-dff7-4caf-92a9-bfcf8a0626d2,"[{""sku"": ""SKU-80911"", ""qty"": 3, ""price"": 331.39}, {""sku"": ""SKU-99329"", ""qty"": 2, ""price"": 298.06}, {",paypal,1616.95,50.66,2026-01-07T13:51:25.486671,493.4,TXN-77704630
e3a778bb-00fd-4562-90df-ed8f2a763138,"[{""sku"": ""SKU-80038"", ""qty"": 5, ""price"": 475.78}, {""sku"": ""SKU-53164"", ""qty"": 1, ""price"": 486.58}, {",credit_card,1304.79,163.85,2026-01-07T13:51:25.486736,1284.07,TXN-65972559
4d3d7b5d-c27f-4c71-8965-b38044aeb365,"[{""sku"": ""SKU-18432"", ""qty"": 5, ""price"": 197.63}, {""sku"": ""SKU-99824"", ""qty"": 1, ""price"": 262.9}, {""",credit_card,1444.13,95.96,2026-01-07T13:51:25.486779,1677.28,TXN-19004201


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

[User] Generate error log data in JSON format.

[Assistant]
Approach:
1. Task: Create error log 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:
{
  "level": "ERROR",
  "service": "writeService",
  "error_id": "85223c4c-4b76-4351-95ee-55a2460043f3"
}


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

[User] Output error log as YAML.

[Assistant]
Approach:
1. Task: Create error log 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:
error_id: 2a52fc1b-775b-4f0d-9bad-8f4f44e2d81f
timestamp: '2026-01-07T13:51:25.611338'
level: ERROR
service: typeService
message: Suggest involve part skill write left participant.
stack_trace:
- at risk.py:441
- at strategy.py:390
- at performance.py:267
- at kind.py:265
context:
  user_id: 3651fff2-dc6a-4fe5-a964-ed26913f3e4b
  request_id: f4fc48f0-6374-4e80-b666-89c2abac0904


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

[User] Output experiment result as TOML.

[Assistant]
Approach:
1. Task: Create experiment result 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:
conclusion = "non-significant"
[[results]]
metric = "efficacy"
baseline = 40.85
endpoint = 40.08
p_value = 0.4196

[[results]]
metric = "efficacy"
baseline = 82.87
endpoint = 28.4
p_value = 0.208

[[results]]
metric = "efficacy"
baseline = 74.0
endpoint = 18.43
p_value = 0.3638

[[results]]
metric = "efficacy"
baseline = 50.48
endpoint = 15.59
p_value = 0.1146

[methodology]
design = "observational"
sample_size = 351
duration_days = 339


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

[User] Output contract as CSV.

[Assistant]
Approach:
1. Task: Create contract 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:
contract_id,effective_date,parties,status,term_months,value
CTR-291572,2026-01-03,"[{""name"": ""Smith and Sons"", ""role"": ""party_a""}, {""name"": ""Avila-Campbell"", ""role"": ""party_b""}]",pending,47,297446
CTR-330674,2026-01-02,"[{""name"": ""Johnston, Brown and Mckee"", ""role"": ""party_a""}, {""name"": ""Wilson PLC"", ""role"": ""party_b""}",executed,27,255219
CTR-717647,2026-01-01,"[{""name"": ""Brown-Garcia"", ""role"": ""party_a""}, {""name"": ""Rogers-Dyer"", ""role"": ""party_b""}]",active,59,213240


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

[User] Generate transaction record data in XML format.

[Assistant]
Approach:
1. Task: Create transaction record 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"?>
<transaction_record>
<timestamp>2026-01-07T13:51:25.400660</timestamp>
<items>
<sku>SKU-41387</sku>
<qty>1</qty>
<price>386.75</price>
</items>
<txn_id>TXN-44773795</txn_id>
<customer_id>acd22e98-51a5-48fa-ad66-a69b8fba3f2b</customer_id>
<tax>171.02</tax>
</transaction_record>


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

[User] Produce a XML document for a prescription.

[Assistant]
Approach:
1. Task: Create prescription in XML
2. Complexity: 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"?>
<prescription>
<rx_id>RX-6036951</rx_id>
<patient_mrn>MRN-440463</patient_mrn>
<prescriber>Lisa Mathews</prescriber>
<date>2026-01-05</date>
<medications>
<name>Metformin</name>
<dosage>20mg</dosage>
<frequency>TID</frequency>
<quantity>90</quantity>
</medications>
</prescription>


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

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

[Assistant]
Approach:
1. Task: Create student assessment in XML
2. Complexity: 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"?>
<student_assessment>
<assessment_id>ASMT-893180</assessment_id>
<student_id>STU-881633</student_id>
<course_id>CRS-6394</course_id>
<type>quiz</type>
<date>2026-01-03</date>
<scores>
<question>1</question>
<points_earned>10</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>2</question>
<points_earned>8</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>6</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>5</question>
<points_earned>4</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>6</question>
<points_earned>6</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>10</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>9</question>
<points_earned>5</points_earned>
<points_possible>10</points_possible>
</scores>
<total_score>98</total_score>
<percentage>73.06</percentage>
<grade>A</grade>
</student_assessment>


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

[User] Create a XML representation of research paper.

[Assistant]
Approach:
1. Task: Create research paper 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"?>
<research_paper>
<paper_id>arXiv:2023.99214</paper_id>
<title>Integrated mobile database</title>
<authors>
<name>Clayton Martinez</name>
<affiliation>Flores, Levy and Farmer</affiliation>
<email>kathy80@example.org</email>
</authors>
<authors>
<name>Susan Bennett</name>
<affiliation>Collins, Daniel and Lyons</affiliation>
<email>shawnjefferson@example.org</email>
</authors>
<abstract>Full with spend role first drive. Want man instead today bed hand character.</abstract>
<keywords>bed</keywords>
<keywords>very</keywords>
<keywords>population</keywords>
<publication>
<venue>Nature</venue>
<year>2023</year>
<doi>10.4640/CkqB</doi>
</publication>
<citations>395</citations>
<funding_source />
</research_paper>


[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"?>
<sales_analytics>
<period>February 2022</period>
<total_revenue>6033804</total_revenue>
<units_sold>27183</units_sold>
<channels>
<name>wholesale</name>
<revenue>4147239</revenue>
<growth>40.37</growth>
</channels>
<channels>
<name>online</name>
<revenue>2663137</revenue>
<growth>9.96</growth>
</channels>
<top_products>
<sku>SKU-2538</sku>
<sales>420940</sales>
</top_products>
<top_products>
<sku>SKU-8680</sku>
<sales>364206</sales>
</top_products>
<top_products>
<sku>SKU-2062</sku>
<sales>350623</sales>
</top_products>
</sales_analytics>

[Assistant]
Approach:
1. Task: Convert sales analytics 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:
{
  "period": "February 2022",
  "total_revenue": 6033804,
  "units_sold": 27183,
  "channels": [
    {
      "name": "wholesale",
      "revenue": 4147239,
      "growth": 40.37
    },
    {
      "name": "online",
      "revenue": 2663137,
      "growth": 9.96
    }
  ],
  "top_products": [
    {
      "sku": "SKU-2538",
      "sales": 420940
    },
    {
      "sku": "SKU-8680",
      "sales": 364206
    },
    {
      "sku": "SKU-2062",
      "sales": 350623
    }
  ]
}


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

[User] Output financial report as XML.

[Assistant]
Approach:
1. Task: Create financial report 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"?>
<financial_report>
<expenses>65628233</expenses>
<eps>5.75</eps>
<quarter>Q2 2023</quarter>
<revenue>73745517</revenue>
<net_income>6367407</net_income>
</financial_report>


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

[User] Create a YAML representation of lab result.

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

Output:
result_id: LAB-2441646
patient_mrn: MRN-228148
order_date: '2026-01-01'
panel: Lipid
results:
- test: Glucose
  value: 81.9
  unit: g/dL
  flag: high
- test: WBC
  value: 107.7
  unit: mg/dL
  flag: high
- test: Hemoglobin
  value: 101.6
  unit: mg/dL
  flag: normal
- test: Hemoglobin
  value: 197.9
  unit: g/dL
  flag: low


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

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

[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:
course_id = "CRS-6496"
code = "TMG245"
instructor = "Maria Bowers"
[[schedule]]
week = 1
topic = "situation"

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

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

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

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

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

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

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


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

[User]
Convert the following JSON to XML:
{
  "paper_id": "arXiv:2021.87801",
  "title": "Cross-group 24/7 methodology",
  "authors": [
    {
      "name": "Scott Webb",
      "affiliation": "Chapman LLC",
      "email": "juanbooth@example.com"
    },
    {
      "name": "Robert Mullins",
      "affiliation": "Robbins, Adkins and Rogers",
      "email": "michaeldunn@example.org"
    }
  ],
  "abstract": "Reality maybe court material response cell. Middle instead husband win thousand collection.",
  "keywords": [
    "girl",
    "face",
    "protect"
  ],
  "publication": {
    "venue": "Cell",
    "year": 2023,
    "doi": "10.4960/byWV"
  },
  "citations": 193,
  "funding_source": "Williams Ltd"
}

[Assistant]
Approach:
1. Task: Convert research paper 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"?>
<research_paper>
<paper_id>arXiv:2021.87801</paper_id>
<title>Cross-group 24/7 methodology</title>
<authors>
<name>Scott Webb</name>
<affiliation>Chapman LLC</affiliation>
<email>juanbooth@example.com</email>
</authors>
<authors>
<name>Robert Mullins</name>
<affiliation>Robbins, Adkins and Rogers</affiliation>
<email>michaeldunn@example.org</email>
</authors>
<abstract>Reality maybe court material response cell. Middle instead husband win thousand collection.</abstract>
<keywords>girl</keywords>
<keywords>face</keywords>
<keywords>protect</keywords>
<publication>
<venue>Cell</venue>
<year>2023</year>
<doi>10.4960/byWV</doi>
</publication>
<citations>193</citations>
<funding_source>Williams Ltd</funding_source>
</research_paper>


[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-6846954",
  "headline": "Triple-buffered intangible knowledgebase",
  "author": "Steven Burgess",
  "published": "2026-01-02T13:52:55.283034",
  "section": "Politics",
  "content": "Police report arm sense find civil during. New avoid old future quality finally bad. Meeting child effect go play issue.\nThousand security article two color early. Avoid commercial science. Low method cultural personal night often.",
  "tags": [
    "couple",
    "specific",
    "him"
  ],
  "views": 35509,
  "shares": 3662
}

[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-6846954</article_id>
<headline>Triple-buffered intangible knowledgebase</headline>
<author>Steven Burgess</author>
<published>2026-01-02T13:52:55.283034</published>

Politics

<content>Police report arm sense find civil during. New avoid old future quality finally bad. Meeting child effect go play issue.
Thousand security article two color early. Avoid commercial science. Low method cultural personal night often.</content>
<tags>couple</tags>
<tags>specific</tags>
<tags>him</tags>
<views>35509</views>
<shares>3662</shares>
</news_article>


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

[User] Generate news article data in YAML format.

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

Output:
article_id: ART-8172649
headline: Triple-buffered responsive model
author: Rachel Clark
published: '2026-01-03T00:35:22.551008'
section: Business
content: 'Pattern capital time compare include night top. Large than important morning.

  Card authority protect present return while middle. Development miss write five.'
tags:
- consider
- whatever
- part
- however
- special
views: 49705
shares: 392


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

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

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

Output:
post_id = "POST-19357850"
platform = "twitter"
user_id = "664b960b-1331-492f-addd-e9b37d21656f"
username = "mbaldwin"
content = "Forward front agreement cover hour we remember ask. Read glass few word mother deep TV."
timestamp = "2026-01-07T13:51:27.605837"
hashtags = [ "#cost", "#he", "#relationship",]

[engagement]
likes = 304
shares = 796
comments = 124


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

[User] Generate customer review data in TOML format.

[Assistant]
Approach:
1. Task: Create customer review 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:
review_id = "REV-1521978"
product_id = "PROD-894495"
user_id = "0e93d4dc-0d39-4af2-8ec3-d8d1250011b9"
rating = 4
title = "Great how data open such."
content = "City not either situation all along might. Under could expect build member. Fish ready onto white majority."
date = "2026-01-05"
verified_purchase = false
helpful_votes = 57


[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: 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:
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] Generate product listing data in JSON format.

[Assistant]
Approach:
1. Task: Create product listing 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:
{
  "product_id": "PROD-986141",
  "name": "Focused neutral encoding",
  "category": "books",
  "price": 14.45,
  "currency": "USD",
  "stock": 826,
  "is_available": true,
  "rating": 4.59,
  "reviews": 564,
  "specs": {
    "weight_kg": 27.7,
    "dimensions": "42x99x90cm"
  }
}


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

[User] Create a XML representation of sales analytics.

[Assistant]
Approach:
1. Task: Create sales analytics 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"?>
<sales_analytics>
<period>February 2020</period>
<total_revenue>2007174</total_revenue>
<units_sold>24558</units_sold>
<channels>
<name>online</name>
<revenue>235807</revenue>
<growth>46.03</growth>
</channels>
<channels>
<name>wholesale</name>
<revenue>4419318</revenue>
<growth>24.35</growth>
</channels>
<top_products>
<sku>SKU-6554</sku>
<sales>258652</sales>
</top_products>
<top_products>
<sku>SKU-8652</sku>
<sales>200091</sales>
</top_products>
<top_products>
<sku>SKU-9581</sku>
<sales>65110</sales>
</top_products>
</sales_analytics>


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

[User] Output customer review as CSV.

[Assistant]
Approach:
1. Task: Create customer review 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:
category,content,currency,date,helpful_votes,is_available,name,price,product_id,rating,review_id,reviews,specs_dimensions,specs_weight_kg,stock,title,user_id,verified_purchase
,Now decide senior fact big window yourself. Difference appear seat coach rather once politics. Moder,,2026-01-02,8,,,,PROD-861575,1,REV-1369973,,,,,Relationship job find one allow law through.,a55bb48c-5dcb-464a-b952-9d377a4cb9c4,True
books,,USD,,,True,Cloned maximized application,219.99,PROD-734195,1.61,,912,32x36x71cm,45.63,793,,,
electronics,,USD,,,True,Switchable zero administration hub,314.79,PROD-729808,2.08,,697,78x45x86cm,32.98,855,,,


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

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

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

Output:
post_id: POST-93814090
platform: facebook
user_id: 6a4446e9-5537-4ba1-bf03-5db2d36e07b9
username: tonyaguzman
content: Subject watch side owner new. Quality million clear hand clearly environmental.
  Forget face machine little check.
timestamp: '2026-01-07T13:51:26.454281'
hashtags:
- '#plan'
- '#area'
- '#foreign'
engagement:
  likes: 5322
  shares: 327
  comments: 152


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

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

[Assistant]
Approach:
1. Task: Create social media post 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:
post_id = "POST-29576154"
platform = "instagram"
user_id = "b3774d2a-dced-4164-bd7b-7d043d7ec920"
username = "boydkaren"
content = "Responsibility this statement area thing imagine free. Under quickly treatment ever end social military. Majority before treatment decide."
timestamp = "2026-01-07T13:51:27.160344"
hashtags = [ "#involve", "#fact", "#rest",]

[engagement]
likes = 923
shares = 56
comments = 396


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

[User]
Transform this XML data into JSON format:
<?xml version="1.0" encoding="UTF-8"?>
<student_assessment>
<total_score>97</total_score>
<scores>
<question>1</question>
<points_earned>0</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>2</question>
<points_earned>4</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>5</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>5</question>
<points_earned>4</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>6</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>7</question>
<points_earned>1</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>8</question>
<points_earned>5</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>9</question>
<points_earned>3</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>10</question>
<points_earned>9</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>11</question>
<points_earned>5</points_earned>
<points_possible>10</points_possible>
</scores>
<percentage>78.32</percentage>
<course_id>CRS-4089</course_id>
</student_assessment>

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

Output:
{
  "total_score": 97,
  "scores": [
    {
      "question": 1,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 4,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 10,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 4,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 2,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 8,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 9,
      "points_earned": 3,
      "points_possible": 10
    },
    {
      "question": 10,
      "points_earned": 9,
      "points_possible": 10
    },
    {
      "question": 11,
      "points_earned": 5,
      "points_possible": 10
    }
  ],
  "percentage": 78.32,
  "course_id": "CRS-4089"
}


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

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

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

Output:
{
  "result_id": "LAB-5796495",
  "patient_mrn": "MRN-496025",
  "order_date": "2026-01-06",
  "panel": "Lipid",
  "results": [
    {
      "test": "Hemoglobin",
      "value": 74.9,
      "unit": "g/dL",
      "flag": "low"
    },
    {
      "test": "Hemoglobin",
      "value": 68.3,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "WBC",
      "value": 121.2,
      "unit": "mg/dL",
      "flag": "normal"
    },
    {
      "test": "Hemoglobin",
      "value": 142.6,
      "unit": "g/dL",
      "flag": "normal"
    }
  ]
}


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

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

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

Output:
review_id: REV-3982215
product_id: PROD-143836
user_id: 0dd51960-e54b-4127-90d4-b32b17839969
rating: 2
title: Fight and building toward office.
content: Hear media safe I visit language. Difference part thousand. Society inside
  describe decision.
date: '2026-01-06'
verified_purchase: true
helpful_votes: 16


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

[User]
Reformat the data below from XML to TOML:
<?xml version="1.0" encoding="UTF-8"?>
<student_assessment>
<assessment_id>ASMT-485879</assessment_id>
<student_id>STU-284737</student_id>
<course_id>CRS-1053</course_id>
<type>exam</type>
<date>2026-01-03</date>
<scores>
<question>1</question>
<points_earned>0</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>7</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>4</question>
<points_earned>9</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>9</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>7</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<total_score>89</total_score>
<percentage>64.04</percentage>
<grade>A</grade>
</student_assessment>

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

Output:
assessment_id = "ASMT-485879"
student_id = "STU-284737"
course_id = "CRS-1053"
type = "exam"
date = "2026-01-03"
total_score = 89
percentage = 64.04
grade = "A"
[[scores]]
question = 1
points_earned = 0
points_possible = 10

[[scores]]
question = 2
points_earned = 6
points_possible = 10

[[scores]]
question = 3
points_earned = 7
points_possible = 10

[[scores]]
question = 4
points_earned = 9
points_possible = 10

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

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

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


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

[User] Create a YAML representation of 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: March 2022
total_revenue: 2472943
units_sold: 2053
channels:
- name: wholesale
  revenue: 3703587
  growth: 38.49
- name: retail
  revenue: 3200920
  growth: -8.82
top_products:
- sku: SKU-8959
  sales: 220308
- sku: SKU-7189
  sales: 206126
- sku: SKU-5359
  sales: 225947


[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-9595817",
  "patient": {
    "mrn": "MRN-972408",
    "age": 32,
    "gender": "F"
  },
  "encounter_date": "2026-01-02",
  "chief_complaint": "chest pain",
  "vitals": {
    "bp": "102/103",
    "hr": 113,
    "temp_f": 102.5
  },
  "assessment": [
    {
      "problem": "hypertension",
      "icd10": "K99"
    }
  ]
}

[Assistant]
Approach:
1. Task: Convert clinical note 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"?>
<clinical_note>
<note_id>CN-9595817</note_id>
<patient>
<mrn>MRN-972408</mrn>
<age>32</age>
<gender>F</gender>
</patient>
<encounter_date>2026-01-02</encounter_date>
<chief_complaint>chest pain</chief_complaint>
<vitals>
<bp>102/103</bp>

113

<temp_f>102.5</temp_f>
</vitals>
<assessment>
<problem>hypertension</problem>
<icd10>K99</icd10>
</assessment>
</clinical_note>