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

[User]
Convert the following XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<customer_review>
<review_id>REV-7122533</review_id>
<content>Expert difficult hot radio mission. Throw southern he book way media particular.</content>
<rating>1</rating>
<date>2026-01-06</date>
<verified_purchase>False</verified_purchase>
</customer_review>

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

Output:
{
  "review_id": "REV-7122533",
  "content": "Expert difficult hot radio mission. Throw southern he book way media particular.",
  "rating": 1,
  "date": "2026-01-06",
  "verified_purchase": false
}


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

[User]
Convert the following JSON to XML:
{
  "order_date": "2026-01-05",
  "results": [
    {
      "test": "Hemoglobin",
      "value": 95.5,
      "unit": "g/dL",
      "flag": "low"
    },
    {
      "test": "Hemoglobin",
      "value": 52.7,
      "unit": "mg/dL",
      "flag": "high"
    },
    {
      "test": "Glucose",
      "value": 142.9,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "Glucose",
      "value": 146.5,
      "unit": "mg/dL",
      "flag": "high"
    }
  ],
  "panel": "Lipid"
}

[Assistant]
Approach:
1. Task: Convert lab result 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"?>
<lab_result>
<order_date>2026-01-05</order_date>
<results>
<test>Hemoglobin</test>
<value>95.5</value>
<unit>g/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>52.7</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Glucose</test>
<value>142.9</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Glucose</test>
<value>146.5</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
<panel>Lipid</panel>
</lab_result>


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

[User] Output error log as XML.

[Assistant]
Approach:
1. Task: Create error log in XML
2. Complexity: 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"?>
<error_log>
<error_id>2d978d26-6fd7-4641-b202-43ebc0760711</error_id>
<timestamp>2026-01-07T13:51:26.663970</timestamp>
<level>WARNING</level>
<service>someoneService</service>
<message>Inside live none value unit Republican black.</message>
<stack_trace>at role.py:276</stack_trace>
<stack_trace>at loss.py:371</stack_trace>
<context>
<user_id>26f1b94c-c76e-47ab-9121-a3f8ec11053f</user_id>
<request_id>ce096dca-f53f-40bf-822d-ed29d8ca8dd9</request_id>
</context>
</error_log>


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

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

[Assistant]
Approach:
1. Task: Create customer review 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"
user_id = "d6e6e646-61a4-4af9-a9a1-d03d234244eb"
content = "It herself scene five side support onto. Shake fear western believe. Say fact shoulder investment television pretty suffer.\nGround listen official hand at indeed. Effort thus wife control."


[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: complex - 8-10 fields, 3-4 levels
3. Format rules: proper braces/brackets, quoted keys, correct value types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
{
  "period": "March 2022",
  "total_revenue": 6503980,
  "units_sold": 7670,
  "channels": [
    {
      "name": "retail",
      "revenue": 3279241,
      "growth": 22.76
    },
    {
      "name": "wholesale",
      "revenue": 1469640,
      "growth": 16.07
    }
  ],
  "top_products": [
    {
      "sku": "SKU-8081",
      "sales": 71747
    },
    {
      "sku": "SKU-8211",
      "sales": 475825
    },
    {
      "sku": "SKU-4565",
      "sales": 485553
    }
  ]
}


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

[User]
Reformat the data below from JSON to CSV:
{
  "article_id": "ART-8668047",
  "headline": "Seamless local alliance",
  "author": "Alyssa Moore",
  "published": "2026-01-02T06:22:52.413807",
  "section": "Business",
  "content": "Billion up year window similar. Oil two resource next. If this tough pattern.\nRule year push international.\nReceive charge movie remember hear theory stuff explain. Century animal decide everyone direction rather. Month property rock environment fine such hand remain.",
  "tags": [
    "traditional",
    "wish",
    "little",
    "inside",
    "year"
  ],
  "views": 55093,
  "shares": 3713
}

[Assistant]
Approach:
1. Task: Convert news article 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:
article_id,author,content,headline,published,section,shares,tags,views
ART-8668047,Alyssa Moore,"Billion up year window similar. Oil two resource next. If this tough pattern.
Rule year push interna",Seamless local alliance,2026-01-02T06:22:52.413807,Business,3713,"[""traditional"", ""wish"", ""little"", ""inside"", ""year""]",55093
ART-5058129,Michelle Thompson,Hot according close one. Expect almost keep through half answer dark. Economic huge him that age ans,Centralized regional framework,2026-01-04T02:40:13.869114,Tech,1843,"[""go"", ""computer"", ""final"", ""need"", ""price""]",71135
ART-9654004,Stanley Harris,Service peace compare two sea notice where. Hour specific ahead eight camera figure. I not marriage ,Front-line executive alliance,2026-01-04T02:32:11.098617,Tech,3760,"[""carry"", ""hard"", ""reality"", ""mind""]",99447
ART-9078927,Shane Austin,"Idea space what young business art. Age cell partner affect.
Key although lot hit. Office example at",Multi-tiered grid-enabled open system,2026-01-02T00:07:53.674164,Sports,1595,"[""memory"", ""spend"", ""voice"", ""beyond""]",27477
ART-2928310,Patrick Vasquez,"Rather allow law American. Room team every trade try seat black.
Example new short letter doctor. Gr",Distributed holistic project,2026-01-05T20:43:29.555803,Business,992,"[""check"", ""soldier"", ""live"", ""state"", ""despite""]",18644


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

[User] Generate prescription data in TOML format.

[Assistant]
Approach:
1. Task: Create prescription in TOML
2. Complexity: 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 = "2026-01-06"
patient_mrn = "MRN-259808"
rx_id = "RX-8539167"
prescriber = "Joseph Griffin"
[[medications]]
name = "Atorvastatin"
dosage = "10mg"
frequency = "daily"
quantity = 90

[[medications]]
name = "Lisinopril"
dosage = "10mg"
frequency = "TID"
quantity = 30

[[medications]]
name = "Metformin"
dosage = "5mg"
frequency = "BID"
quantity = 30


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

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

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

Output:
article_id: ART-8221745
headline: Polarized zero administration support
author: Maria Johnson
published: '2026-01-03T08:37:58.870132'
section: Business
content: 'Would movement white military what them cup. Available remember control
  staff. None mean hundred PM.

  Improve wear produce price according. He method market computer TV strategy.

  Body into language. Which other hear they look some option.'
tags:
- environment
- name
- receive
- happen
- join
views: 46852
shares: 763


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

[User] Output social media post as CSV.

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

Output:
content,engagement_comments,engagement_likes,engagement_shares,hashtags,platform,post_id,timestamp,user_id,username
"Between past agree writer forget big. Simple glass protect job answer thank.
Government prepare rath",90,1299,900,"[""#fast"", ""#imagine"", ""#carry""]",instagram,POST-19844803,2026-01-07T13:51:25.753060,eee48898-e428-4685-b6ea-d0074f4ffb8a,pinedaryan
Manage medical study fall nice just pretty exactly. Compare mother tell general. Black hit other pap,254,1470,392,"[""#Republican"", ""#center"", ""#huge""]",instagram,POST-69326235,2026-01-07T13:51:25.754323,8a563364-48d7-4595-b100-acd912d5da79,seankim
Trade eye start. Break my blue member all. Though decision discover cause poor. Behavior head quickl,148,8701,622,"[""#five"", ""#particularly"", ""#believe""]",twitter,POST-43289068,2026-01-07T13:51:25.754690,390a4360-d445-460b-b99a-d14e0c8a0492,bestlindsey
Yet wide still every. Peace where city success. Chance a world under player send operation.,394,3968,538,"[""#experience"", ""#popular""]",instagram,POST-89448830,2026-01-07T13:51:25.754966,00483d57-302f-4d07-966b-c36d87cbbcf9,aaron61
"Set wish would loss certain home week allow. Food traditional hot wrong face receive.
Current scene ",243,8429,63,"[""#set"", ""#happen"", ""#front""]",facebook,POST-58395990,2026-01-07T13:51:25.755271,f42556f9-3a62-4d11-9d37-c7e3b8136ca4,agrimes


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

[User] Generate contract data in XML format.

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<contract>
<contract_id>CTR-457828</contract_id>
<effective_date>2026-01-01</effective_date>
<term_months>32</term_months>
</contract>


[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: medium - 5-8 fields, 2-3 levels
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
rx_id = "RX-3305496"
patient_mrn = "MRN-551064"
prescriber = "Eric Jackson"
date = "2026-01-03"
[[medications]]
name = "Atorvastatin"
dosage = "20mg"
frequency = "daily"
quantity = 90


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

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

[Assistant]
Approach:
1. Task: Create financial report 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:
company,eps,expenses,metrics_profit_margin,metrics_roe,net_income,quarter,revenue,ticker
Ortega-Owens,1.51,44591506,5.54,6.32,3197580,Q3 2022,37598920,YLLB
White-Olson,7.4,43583851,20.88,6.11,15633315,Q2 2021,7675359,QPRL
"Obrien, Stafford and Johnson",3.17,27196822,21.87,10.54,7469164,Q4 2024,79145487,TUNG


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

[User] Produce a CSV document for a contract.

[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-544771,2026-01-02,"[{""name"": ""Prince-Finley"", ""role"": ""party_a""}, {""name"": ""Stone LLC"", ""role"": ""party_b""}]",active,52,394049
CTR-432722,2026-01-04,"[{""name"": ""Rivera, Armstrong and Martinez"", ""role"": ""party_a""}, {""name"": ""Powers Ltd"", ""role"": ""part",executed,42,172253
CTR-571964,2026-01-02,"[{""name"": ""Mercado-Fields"", ""role"": ""party_a""}, {""name"": ""Dominguez PLC"", ""role"": ""party_b""}]",executed,45,436546


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

[User] Create a JSON representation of contract.

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

Output:
{
  "value": 911148,
  "contract_id": "CTR-919818",
  "parties": [
    {
      "name": "Riggs, Carey and Rojas",
      "role": "party_a"
    },
    {
      "name": "Mcdowell-Koch",
      "role": "party_b"
    }
  ],
  "status": "active",
  "term_months": 18
}


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

[User] Generate research paper data in TOML format.

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

Output:
paper_id = "arXiv:2020.10600"
title = "Reactive bi-directional middleware"
abstract = "Carry prove certain tell event break. Thus not actually teacher girl perhaps."
keywords = [ "public", "stock", "student", "hair", "hard",]
citations = 273
funding_source = "Salazar, Mullen and Johnson"
[[authors]]
name = "Sarah Rose"
affiliation = "Martin, Taylor and Mills"
email = "adrianporter@example.com"

[[authors]]
name = "Samantha Garcia"
affiliation = "Santiago LLC"
email = "cbrewer@example.net"

[publication]
venue = "Cell"
year = 2022
doi = "10.6112/KodW"


[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"?>
<news_article>
<article_id>ART-2188860</article_id>
<headline>Advanced well-modulated service-desk</headline>
<author>Michael Logan</author>
<published>2026-01-03T08:09:52.608144</published>

Politics

<content>Include start build no effort continue. Mention sister mention threat.
Choose herself case writer question over. Hand quickly house send shoulder expert hour.
All probably suffer law consider kitchen relationship. Data population personal past security but across.</content>
<tags>hear</tags>
<tags>civil</tags>
<tags>might</tags>
<tags>way</tags>
<tags>win</tags>
<views>35184</views>
<shares>1092</shares>
</news_article>

[Assistant]
Approach:
1. Task: Convert news article 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:
{
  "article_id": "ART-2188860",
  "headline": "Advanced well-modulated service-desk",
  "author": "Michael Logan",
  "published": "2026-01-03T08:09:52.608144",
  "section": "Politics",
  "content": "Include start build no effort continue. Mention sister mention threat.\nChoose herself case writer question over. Hand quickly house send shoulder expert hour.\nAll probably suffer law consider kitchen relationship. Data population personal past security but across.",
  "tags": [
    "hear",
    "civil",
    "might",
    "way",
    "win"
  ],
  "views": 35184,
  "shares": 1092
}


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

[User] Create a JSON representation of product listing.

[Assistant]
Approach:
1. Task: Create product listing in JSON
2. Complexity: 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-942707",
  "name": "Organic demand-driven installation",
  "category": "books",
  "price": 698.66,
  "currency": "USD",
  "stock": 819,
  "is_available": true,
  "rating": 4.45,
  "reviews": 103,
  "specs": {
    "weight_kg": 32.63,
    "dimensions": "66x30x83cm"
  }
}


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

[User]
Transform this JSON data into XML format:
{
  "review_id": "REV-3637179",
  "product_id": "PROD-905516",
  "user_id": "cbc145e7-7f32-438c-ba90-61711bf65a15",
  "rating": 1,
  "title": "Hand ok join you population.",
  "content": "Develop particularly receive thing process sister source. Ago short fact start meeting note need. However music factor my million scene use.",
  "date": "2026-01-03",
  "verified_purchase": false,
  "helpful_votes": 42
}

[Assistant]
Approach:
1. Task: Convert customer review 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"?>
<customer_review>
<review_id>REV-3637179</review_id>
<product_id>PROD-905516</product_id>
<user_id>cbc145e7-7f32-438c-ba90-61711bf65a15</user_id>
<rating>1</rating>
<title>Hand ok join you population.</title>
<content>Develop particularly receive thing process sister source. Ago short fact start meeting note need. However music factor my million scene use.</content>
<date>2026-01-03</date>
<verified_purchase>False</verified_purchase>
<helpful_votes>42</helpful_votes>
</customer_review>


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

[User] Output contract as YAML.

[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-299234
parties:
- name: Murphy, Hawkins and Mckenzie
  role: party_a
- name: Collins-Miller
  role: party_b
effective_date: '2026-01-06'
term_months: 46
value: 506357
status: pending


[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:
{
  "level": "ERROR",
  "service": "writeService",
  "error_id": "85223c4c-4b76-4351-95ee-55a2460043f3"
}

[Assistant]
Approach:
1. Task: Convert error log 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"?>
<error_log>
<level>ERROR</level>
<service>writeService</service>
<error_id>85223c4c-4b76-4351-95ee-55a2460043f3</error_id>
</error_log>


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

[User] Generate course syllabus data in XML format.

[Assistant]
Approach:
1. Task: Create course syllabus 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"?>
<course_syllabus>
<course_id>CRS-8855</course_id>
DBP130
<title>Team-oriented 3rdgeneration implementation 101</title>
<instructor>Carl Davis</instructor>
<credits>3</credits>
<term>Fall 2024</term>
<schedule>
<week>1</week>
<topic>manage</topic>
</schedule>
<schedule>
<week>2</week>
<topic>someone</topic>
</schedule>
<schedule>
<week>3</week>
<topic>bring</topic>
</schedule>
<schedule>
<week>4</week>
<topic>board</topic>
</schedule>
<schedule>
<week>5</week>
<topic>important</topic>
</schedule>
<schedule>
<week>6</week>
<topic>he</topic>
</schedule>
<schedule>
<week>7</week>
<topic>possible</topic>
</schedule>
<schedule>
<week>8</week>
<topic>office</topic>
</schedule>
<schedule>
<week>9</week>
<topic>board</topic>
</schedule>
<schedule>
<week>10</week>
<topic>drive</topic>
</schedule>
<schedule>
<week>11</week>
<topic>Mrs</topic>
</schedule>
<grading>
<homework>30</homework>
<midterm>30</midterm>
<final>40</final>
</grading>
</course_syllabus>


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

[User] Generate lab result data in TOML format.

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

Output:
result_id = "LAB-3990665"
patient_mrn = "MRN-508219"
order_date = "2026-01-04"
panel = "Lipid"
[[results]]
test = "Hemoglobin"
value = 80.7
unit = "g/dL"
flag = "low"

[[results]]
test = "Glucose"
value = 90.9
unit = "g/dL"
flag = "normal"

[[results]]
test = "Hemoglobin"
value = 138.0
unit = "g/dL"
flag = "low"

[[results]]
test = "WBC"
value = 131.3
unit = "g/dL"
flag = "normal"

[[results]]
test = "Glucose"
value = 172.4
unit = "g/dL"
flag = "normal"

[[results]]
test = "WBC"
value = 151.6
unit = "g/dL"
flag = "low"

[[results]]
test = "Hemoglobin"
value = 188.6
unit = "g/dL"
flag = "high"


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

[User] Output financial report as CSV.

[Assistant]
Approach:
1. Task: Create financial report in CSV
2. Complexity: 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:
company,eps,expenses,metrics_profit_margin,metrics_roe,net_income,quarter,revenue,ticker
Taylor Ltd,1.63,52626087,23.4,21.22,18580243,Q1 2024,69752271,AOED
"Berg, Edwards and Washington",2.64,21304251,24.59,21.54,15746169,Q4 2022,1384082,IKVG
Lopez Group,9.45,39741607,22.5,19.87,16715861,Q3 2024,12652451,YMDT
Santana Group,1.95,7640424,11.86,13.46,5440388,Q4 2023,6451824,POVS


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

[User] Output api specification as CSV.

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

Output:
abstract,authors,citations,funding_source,info_title,info_version,keywords,openapi,paper_id,paths_/sense_get_parameters,paths_/sense_get_responses_200_description,paths_/sense_get_responses_400_description,paths_/sense_get_summary,publication_doi,publication_venue,publication_year,title
,,,,Customer-focused zero-defect array API,1.0.0,,3.0.0,,"[{""name"": ""param0"", ""in"": ""query"", ""type"": ""string""}]",Success,Bad Request,Pay yes agree executive year ahead program.,,,,
Soldier almost phone result white reach. Phone course daughter customer expert factor always mean. O,"[{""name"": ""Sheena Cain"", ""affiliation"": ""Wilson Ltd"", ""email"": ""dennisrichard@example.com""}, {""name""",23,None,,,"[""doctor"", ""seek"", ""hot"", ""week"", ""claim""]",,arXiv:2023.87145,,,,,10.1817/ymvR,Science,2020,Public-key web-enabled complexity
According field out reduce. Attorney difference actually store without door.,"[{""name"": ""William Doyle"", ""affiliation"": ""Berry, Cardenas and King"", ""email"": ""keith92@example.org""",28,Holloway-Randall,,,"[""newspaper"", ""summer"", ""store"", ""financial""]",,arXiv:2020.25497,,,,,10.4338/xXXh,Science,2024,Automated grid-enabled projection


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

[User]
Transform this XML data into JSON format:
<?xml version="1.0" encoding="UTF-8"?>
<error_log>
<error_id>2a52fc1b-775b-4f0d-9bad-8f4f44e2d81f</error_id>
<timestamp>2026-01-07T13:51:25.611338</timestamp>
<level>ERROR</level>
<service>typeService</service>
<message>Suggest involve part skill write left participant.</message>
<stack_trace>at risk.py:441</stack_trace>
<stack_trace>at strategy.py:390</stack_trace>
<stack_trace>at performance.py:267</stack_trace>
<stack_trace>at kind.py:265</stack_trace>
<context>
<user_id>3651fff2-dc6a-4fe5-a964-ed26913f3e4b</user_id>
<request_id>f4fc48f0-6374-4e80-b666-89c2abac0904</request_id>
</context>
</error_log>

[Assistant]
Approach:
1. Task: Convert error log 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:
{
  "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 JSON format. Think step-by-step, then generate syntactically perfect JSON.

[User] Output lab result as JSON.

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

Output:
{
  "result_id": "LAB-5383313",
  "patient_mrn": "MRN-584871",
  "order_date": "2026-01-06",
  "panel": "CMP",
  "results": [
    {
      "test": "Glucose",
      "value": 194.0,
      "unit": "g/dL",
      "flag": "normal"
    },
    {
      "test": "Hemoglobin",
      "value": 67.1,
      "unit": "g/dL",
      "flag": "low"
    },
    {
      "test": "Glucose",
      "value": 195.1,
      "unit": "g/dL",
      "flag": "low"
    },
    {
      "test": "Hemoglobin",
      "value": 146.6,
      "unit": "mg/dL",
      "flag": "high"
    },
    {
      "test": "WBC",
      "value": 119.8,
      "unit": "mg/dL",
      "flag": "normal"
    }
  ]
}


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

[User]
Convert the following YAML to JSON:
article_id: ART-2650245
headline: Enhanced multimedia hub
author: Jennifer Davis
published: '2026-01-02T17:44:08.333575'
section: Tech
content: 'Late situation both data share market. These foot health box.

  Pay me together western ever story cup. Game production million happy reveal. Attorney
  bill shake crime eat finally drive.

  Choice four concern cold test. Road executive policy official season increase.'
tags:
- future
- here
- new
views: 46115
shares: 2819

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


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

[User] Generate prescription data in CSV format.

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

Output:
date,medications,patient_mrn,prescriber,rx_id
2026-01-05,"[{""name"": ""Lisinopril"", ""dosage"": ""10mg"", ""frequency"": ""BID"", ""quantity"": 60}]",MRN-674224,Emily Herrera,RX-1995080
2026-01-05,"[{""name"": ""Metformin"", ""dosage"": ""20mg"", ""frequency"": ""daily"", ""quantity"": 90}, {""name"": ""Lisinopril",MRN-764986,Deborah Massey,RX-5252523
2026-01-02,"[{""name"": ""Metformin"", ""dosage"": ""20mg"", ""frequency"": ""BID"", ""quantity"": 60}, {""name"": ""Atorvastatin",MRN-498610,Stephanie Fleming,RX-9411892


[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: 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:
message: Baby inside high our should character study quickly.
stack_trace:
- at standard.py:486
- at memory.py:53
- at team.py:342
context:
  user_id: b89e754b-fd03-47c7-9600-09a7cb6d3517
  request_id: 0aea7b7e-f431-4209-a4db-008320d27043
timestamp: '2026-01-07T13:51:25.507604'


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

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

[Assistant]
Approach:
1. Task: Create experiment result 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:
conclusion,experiment_id,methodology_design,methodology_duration_days,methodology_sample_size,results,title
non-significant,EXP-818679,RCT,157,180,"[{""metric"": ""safety"", ""baseline"": 17.77, ""endpoint"": 32.37, ""p_value"": 0.477}, {""metric"": ""safety"", ",Effect of begin on task
significant,EXP-273309,RCT,323,605,"[{""metric"": ""efficacy"", ""baseline"": 51.67, ""endpoint"": 62.51, ""p_value"": 0.1511}, {""metric"": ""safety",Effect of tough on happen
significant,EXP-506939,RCT,151,841,"[{""metric"": ""safety"", ""baseline"": 47.72, ""endpoint"": 47.03, ""p_value"": 0.2063}, {""metric"": ""efficacy",Effect of large on certainly


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

[User] Produce a XML document for a contract.

[Assistant]
Approach:
1. Task: Create contract 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"?>
<contract>
<contract_id>CTR-299234</contract_id>
<parties>
<name>Murphy, Hawkins and Mckenzie</name>
<role>party_a</role>
</parties>
<parties>
<name>Collins-Miller</name>
<role>party_b</role>
</parties>
<effective_date>2026-01-06</effective_date>
<term_months>46</term_months>
<value>506357</value>
<status>pending</status>
</contract>


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

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

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

Output:
post_id = "POST-49469407"
platform = "instagram"
user_id = "c776cd7c-2538-4c81-83c8-03afd724bed1"
username = "donald18"
content = "Knowledge between friend wife property kid out. Trouble recent member sort arrive foot space good."
timestamp = "2026-01-07T13:51:26.031233"
hashtags = [ "#daughter", "#after", "#affect", "#goal",]

[engagement]
likes = 2564
shares = 187
comments = 95


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

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

[Assistant]
Approach:
1. Task: Create product listing in JSON
2. Complexity: 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:
{
  "specs": {
    "weight_kg": 41.33,
    "dimensions": "19x24x69cm"
  },
  "price": 743.81,
  "is_available": true,
  "rating": 2.69,
  "stock": 485
}


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

[User] Produce a JSON document for a contract.

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

Output:
{
  "contract_id": "CTR-274282",
  "parties": [
    {
      "name": "Schroeder LLC",
      "role": "party_a"
    },
    {
      "name": "Kemp-Jones",
      "role": "party_b"
    }
  ],
  "effective_date": "2026-01-01",
  "term_months": 28,
  "value": 285721,
  "status": "active"
}


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

[User]
Convert the following JSON to XML:
{
  "error_id": "249c5a8d-effc-4f5a-9a18-79860cd86eba",
  "timestamp": "2026-01-07T13:51:25.844189",
  "level": "CRITICAL",
  "service": "createService",
  "message": "Capital carry as agree member lead one.",
  "stack_trace": [
    "at sign.py:359",
    "at college.py:115",
    "at try.py:133"
  ],
  "context": {
    "user_id": "e110a198-5d8a-4242-b2e4-6ec6ae49ca80",
    "request_id": "939e8b37-2ce6-4ba0-bde6-ca285c8d396c"
  }
}

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<error_log>
<error_id>249c5a8d-effc-4f5a-9a18-79860cd86eba</error_id>
<timestamp>2026-01-07T13:51:25.844189</timestamp>
<level>CRITICAL</level>
<service>createService</service>
<message>Capital carry as agree member lead one.</message>
<stack_trace>at sign.py:359</stack_trace>
<stack_trace>at college.py:115</stack_trace>
<stack_trace>at try.py:133</stack_trace>
<context>
<user_id>e110a198-5d8a-4242-b2e4-6ec6ae49ca80</user_id>
<request_id>939e8b37-2ce6-4ba0-bde6-ca285c8d396c</request_id>
</context>
</error_log>


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

[User] Generate sales analytics data in YAML format.

[Assistant]
Approach:
1. Task: Create sales analytics 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:
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 XML format. Think step-by-step, then generate syntactically perfect XML.

[User]
Transform this JSON data into XML format:
{
  "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"
}

[Assistant]
Approach:
1. Task: Convert transaction record 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"?>
<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>


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

[User] Generate transaction record data in JSON format.

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

Output:
{
  "customer_id": "5642be10-2f96-4bf3-93bb-ea73b498db7e",
  "timestamp": "2026-01-07T13:51:27.704310",
  "total": 2159.3,
  "tax": 150.65,
  "items": [
    {
      "sku": "SKU-21107",
      "qty": 2,
      "price": 436.86
    },
    {
      "sku": "SKU-34269",
      "qty": 3,
      "price": 260.71
    },
    {
      "sku": "SKU-81249",
      "qty": 3,
      "price": 416.92
    },
    {
      "sku": "SKU-97216",
      "qty": 3,
      "price": 177.96
    }
  ]
}


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

[User] Output transaction record as YAML.

[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-60159725
customer_id: 13707a49-23b0-4133-8216-7e83986c9575
timestamp: '2026-01-07T13:51:27.689251'
items:
- sku: SKU-82899
  qty: 5
  price: 298.29
- sku: SKU-51515
  qty: 4
  price: 343.78
- sku: SKU-53336
  qty: 3
  price: 488.21
- sku: SKU-52517
  qty: 2
  price: 291.79
subtotal: 1914.1
tax: 175.88
total: 1360.6
payment_method: credit_card


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

[User] Output transaction record as CSV.

[Assistant]
Approach:
1. Task: Create transaction record in CSV
2. Complexity: 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
19d3d880-f410-4b85-a35d-9c5777705d1f,"[{""sku"": ""SKU-30306"", ""qty"": 4, ""price"": 206.74}]",paypal,1354.26,33.08,2026-01-07T13:51:26.837603,766.28,TXN-48552675
c695c55e-b596-46d9-bc47-c390aa7c872d,"[{""sku"": ""SKU-94750"", ""qty"": 1, ""price"": 472.93}, {""sku"": ""SKU-10504"", ""qty"": 5, ""price"": 264.67}, {",credit_card,955.84,108.17,2026-01-07T13:51:26.838863,316.49,TXN-54465557
ea98c41e-a3d5-4d9e-bee6-000a1d4f7744,"[{""sku"": ""SKU-88594"", ""qty"": 3, ""price"": 235.35}, {""sku"": ""SKU-18567"", ""qty"": 4, ""price"": 150.13}, {",paypal,1958.34,96.97,2026-01-07T13:51:26.838975,742.44,TXN-94273766
1fda9b96-679b-4609-9cb3-ed3a593ec4fa,"[{""sku"": ""SKU-79958"", ""qty"": 4, ""price"": 360.91}, {""sku"": ""SKU-10817"", ""qty"": 1, ""price"": 162.91}]",apple_pay,1973.64,63.95,2026-01-07T13:51:26.839044,1436.79,TXN-91097399
7a04b941-9bb9-448b-9c1b-9def668658cc,"[{""sku"": ""SKU-98459"", ""qty"": 3, ""price"": 489.56}, {""sku"": ""SKU-59276"", ""qty"": 4, ""price"": 271.5}]",paypal,1582.95,92.26,2026-01-07T13:51:26.839081,1227.49,TXN-33338045


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

[User] Generate api specification data in JSON format.

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

Output:
{
  "openapi": "3.0.0",
  "info": {
    "title": "Pre-emptive interactive encryption API",
    "version": "1.0.0"
  },
  "paths": {
    "/air": {
      "get": {
        "summary": "Final democratic whatever event director guy.",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "400": {
            "description": "Bad Request"
          }
        }
      }
    }
  }
}


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

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


[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>January 2022</period>
<total_revenue>7456708</total_revenue>
<units_sold>82349</units_sold>
<channels>
<name>wholesale</name>
<revenue>3622404</revenue>
<growth>-8.05</growth>
</channels>
<channels>
<name>online</name>
<revenue>1893923</revenue>
<growth>2.21</growth>
</channels>
<top_products>
<sku>SKU-2073</sku>
<sales>98550</sales>
</top_products>
<top_products>
<sku>SKU-3707</sku>
<sales>233488</sales>
</top_products>
<top_products>
<sku>SKU-7843</sku>
<sales>172526</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": "January 2022",
  "total_revenue": 7456708,
  "units_sold": 82349,
  "channels": [
    {
      "name": "wholesale",
      "revenue": 3622404,
      "growth": -8.05
    },
    {
      "name": "online",
      "revenue": 1893923,
      "growth": 2.21
    }
  ],
  "top_products": [
    {
      "sku": "SKU-2073",
      "sales": 98550
    },
    {
      "sku": "SKU-3707",
      "sales": 233488
    },
    {
      "sku": "SKU-7843",
      "sales": 172526
    }
  ]
}


[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"?>
<customer_review>
<review_id>REV-5911912</review_id>
<product_id>PROD-652506</product_id>
<user_id>e81c58cf-1a13-4302-a93a-5e1fbbfd28db</user_id>
<rating>3</rating>
<title>Their husband whole president threat argue.</title>
<content>Happen member step can deal animal fine. Like among study from book.
Democratic significant every sport next want. List religious computer rise wrong.</content>
<date>2026-01-02</date>
<verified_purchase>True</verified_purchase>
<helpful_votes>61</helpful_votes>
</customer_review>

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

Output:
{
  "review_id": "REV-5911912",
  "product_id": "PROD-652506",
  "user_id": "e81c58cf-1a13-4302-a93a-5e1fbbfd28db",
  "rating": 3,
  "title": "Their husband whole president threat argue.",
  "content": "Happen member step can deal animal fine. Like among study from book.\nDemocratic significant every sport next want. List religious computer rise wrong.",
  "date": "2026-01-02",
  "verified_purchase": true,
  "helpful_votes": 61
}


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

[User] Create a YAML representation of financial report.

[Assistant]
Approach:
1. Task: Create financial report 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:
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


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

[User] Create a JSON representation of course syllabus.

[Assistant]
Approach:
1. Task: Create course syllabus 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:
{
  "course_id": "CRS-7514",
  "code": "NOO384",
  "title": "Fundamental encompassing database 101",
  "instructor": "Lori Martinez",
  "credits": 4,
  "term": "Fall 2024",
  "schedule": [
    {
      "week": 1,
      "topic": "bar"
    },
    {
      "week": 2,
      "topic": "population"
    },
    {
      "week": 3,
      "topic": "thus"
    },
    {
      "week": 4,
      "topic": "manage"
    },
    {
      "week": 5,
      "topic": "purpose"
    },
    {
      "week": 6,
      "topic": "look"
    },
    {
      "week": 7,
      "topic": "nothing"
    },
    {
      "week": 8,
      "topic": "remember"
    },
    {
      "week": 9,
      "topic": "song"
    },
    {
      "week": 10,
      "topic": "ahead"
    },
    {
      "week": 11,
      "topic": "pass"
    }
  ],
  "grading": {
    "homework": 30,
    "midterm": 30,
    "final": 40
  }
}


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

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

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

Output:
error_id: 03dcbba2-8070-483e-a807-51e4c6301bf5
timestamp: '2026-01-07T13:51:27.875594'
level: WARNING
service: stockService
message: Feel garden dark.
stack_trace:
- at another.py:304
- at happy.py:331
- at record.py:415
context:
  user_id: cedcf075-a01b-42e9-acdc-a300ea843992
  request_id: acbfd7fd-a4ab-431b-a098-ac4537826f14


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

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

[Assistant]
Approach:
1. Task: Create product listing 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,currency,is_available,name,price,product_id,rating,reviews,specs_dimensions,specs_weight_kg,stock
books,USD,True,Decentralized heuristic migration,124.58,PROD-791864,3.59,538,30x16x25cm,9.83,601
clothing,USD,True,Future-proofed systematic hardware,992.92,PROD-537096,4.54,344,18x72x25cm,5.0,464
clothing,USD,True,Reduced multimedia framework,219.85,PROD-359731,4.05,390,77x18x94cm,47.87,513


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

[User]
Convert the following CSV to YAML:
content,engagement_comments,engagement_likes,engagement_shares,hashtags,platform,post_id,timestamp,user_id,username
Use Mr only week. Crime form attention. Right world stand surface rate three.,4,9998,653,"[""#economic"", ""#speak""]",instagram,POST-64636774,2026-01-07T13:51:26.942814,180a1898-015f-483f-94a7-e771a6fb148e,melissa16
Maybe wish bit allow share. Reduce student sure eat thing charge particular. Each factor trouble val,247,217,964,"[""#so"", ""#soon"", ""#their"", ""#ready""]",instagram,POST-72911154,2026-01-07T13:51:26.945630,1981183e-f73f-4c00-ab72-cbf1d7ecf731,reginatorres
"Son education require. Nor military relate paper. Field technology crime everyone write.
Between wil",257,1206,949,"[""#raise"", ""#indeed"", ""#one"", ""#tonight""]",facebook,POST-35525154,2026-01-07T13:51:26.946054,b98f51d8-23b8-416c-a8b8-6dcfc723dc1a,stephen38
"Must laugh money be. Community day real.
Expert writer reach simply seem party.
Involve person socie",271,1283,925,"[""#character"", ""#and""]",facebook,POST-19696784,2026-01-07T13:51:26.946507,eec1b96a-3937-464c-a8d8-af73cf0653bd,lkline
"Foreign thus not though. Relate mouth usually ball risk. Shoulder sure someone either.
Perform charg",490,7715,362,"[""#so"", ""#particularly""]",facebook,POST-23301440,2026-01-07T13:51:26.946842,6a103282-d9f4-48b3-9a9b-bd61f3001063,znguyen

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

Output:
post_id: POST-64636774
platform: instagram
user_id: 180a1898-015f-483f-94a7-e771a6fb148e
username: melissa16
content: Use Mr only week. Crime form attention. Right world stand surface rate three.
timestamp: '2026-01-07T13:51:26.942814'
hashtags:
- '#economic'
- '#speak'
engagement:
  likes: 9998
  shares: 653
  comments: 4


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

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

[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-330074
title: Effect of politics on stop
methodology:
  design: RCT
  sample_size: 328
  duration_days: 247
results:
- metric: efficacy
  baseline: 34.54
  endpoint: 53.32
  p_value: 0.0487
- metric: safety
  baseline: 10.4
  endpoint: 13.84
  p_value: 0.1164
- metric: efficacy
  baseline: 78.27
  endpoint: 47.78
  p_value: 0.0781
conclusion: non-significant


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

[User]
Transform this JSON data into YAML format:
{
  "openapi": "3.0.0",
  "info": {
    "title": "Assimilated neutral archive API",
    "version": "1.0.0"
  },
  "paths": {
    "/sea": {
      "get": {
        "summary": "Team happen challenge address expert manage beyond",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param1",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param2",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "400": {
            "description": "Bad Request"
          }
        }
      }
    }
  }
}

[Assistant]
Approach:
1. Task: Convert api specification from JSON 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:
openapi: 3.0.0
info:
  title: Assimilated neutral archive API
  version: 1.0.0
paths:
  /sea:
    get:
      summary: Team happen challenge address expert manage beyond
      parameters:
      - name: param0
        in: query
        type: string
      - name: param1
        in: query
        type: string
      - name: param2
        in: query
        type: string
      responses:
        '200':
          description: Success
        '400':
          description: Bad Request


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

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

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

Output:
{
  "paper_id": "arXiv: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"
}


[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>e01a43f8-d4eb-4ec7-a325-ad2b31726d0f</error_id>
<timestamp>2026-01-07T13:51:25.462522</timestamp>
<level>ERROR</level>
<service>winService</service>
<message>Western energy project push building.</message>
<stack_trace>at could.py:42</stack_trace>
<stack_trace>at seem.py:198</stack_trace>
<context>
<user_id>7197800d-0acd-4eee-b9ab-24b5158dfa0a</user_id>
<request_id>00c4c00d-dc22-4013-8495-1ed2381fc937</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": "e01a43f8-d4eb-4ec7-a325-ad2b31726d0f",
  "timestamp": "2026-01-07T13:51:25.462522",
  "level": "ERROR",
  "service": "winService",
  "message": "Western energy project push building.",
  "stack_trace": [
    "at could.py:42",
    "at seem.py:198"
  ],
  "context": {
    "user_id": "7197800d-0acd-4eee-b9ab-24b5158dfa0a",
    "request_id": "00c4c00d-dc22-4013-8495-1ed2381fc937"
  }
}


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

[User] Generate contract data in JSON format.

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

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


[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: simple - 3-5 fields, minimal nesting
3. Format rules: key=value syntax, [sections], proper types
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
openapi = "3.0.0"

[info]
title = "Object-based multimedia secured line API"
version = "1.0.0"

[paths."/party".get]
summary = "Discussion partner stay wear again."
[[paths."/party".get.parameters]]
name = "param0"
in = "query"
type = "string"

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

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

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


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

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

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

Output:
content,engagement_comments,engagement_likes,engagement_shares,hashtags,platform,post_id,timestamp,user_id,username
Example suggest around pattern small everybody. Rate watch blue walk test camera. Partner decide lea,117,5828,957,"[""#door"", ""#relate""]",facebook,POST-32645861,2026-01-07T13:51:27.526455,b5521d05-1410-43ee-b828-2c240f95461c,yanderson
"Church tax health modern herself society. Forward minute may ready PM.
Unit price safe body daughter",266,2760,319,"[""#occur"", ""#surface"", ""#let""]",twitter,POST-70591393,2026-01-07T13:51:27.527639,c2466254-8916-4851-a1b2-150b9c8fe248,evega
Woman whom hard pick bar foreign. Media peace whose despite body central watch people. Happen blue f,5,1997,404,"[""#them"", ""#stock"", ""#writer"", ""#inside""]",twitter,POST-28582452,2026-01-07T13:51:27.527959,b7e8a634-9233-4ab9-b500-d08f7151d990,bgutierrez
Camera almost deep always. Ask owner church seek discover make. Oil professional enough you suffer r,401,6854,683,"[""#teach"", ""#note""]",instagram,POST-25478663,2026-01-07T13:51:27.528432,a994d9d0-0d14-4331-8320-b6eb349239b1,brownmatthew
New long bag national and. Worker month represent wide under. Would mission official morning vote. S,367,462,437,"[""#number"", ""#by"", ""#still"", ""#page""]",facebook,POST-81348381,2026-01-07T13:51:27.528806,feddd3fc-b473-4661-ac94-64685d2e60a3,gonzaleztimothy


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

[User]
Convert the following JSON to XML:
{
  "error_id": "bcec7cd1-70fc-42ba-97dd-2c6e2159fc7d",
  "timestamp": "2026-01-07T13:51:27.288021",
  "level": "CRITICAL",
  "service": "reasonService",
  "message": "Recent security meet three Congress find seat.",
  "stack_trace": [
    "at current.py:236",
    "at your.py:146",
    "at without.py:194"
  ],
  "context": {
    "user_id": "fc408ea4-9df4-4d81-87da-9d63d4bc8744",
    "request_id": "d8c95012-b6bb-42d7-8cb4-e57e77353a5b"
  }
}

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<error_log>
<error_id>bcec7cd1-70fc-42ba-97dd-2c6e2159fc7d</error_id>
<timestamp>2026-01-07T13:51:27.288021</timestamp>
<level>CRITICAL</level>
<service>reasonService</service>
<message>Recent security meet three Congress find seat.</message>
<stack_trace>at current.py:236</stack_trace>
<stack_trace>at your.py:146</stack_trace>
<stack_trace>at without.py:194</stack_trace>
<context>
<user_id>fc408ea4-9df4-4d81-87da-9d63d4bc8744</user_id>
<request_id>d8c95012-b6bb-42d7-8cb4-e57e77353a5b</request_id>
</context>
</error_log>


[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"?>
<api_specification>
<info>
<title>Multi-tiered directional knowledge user API</title>
<version>1.0.0</version>
</info>
<openapi>3.0.0</openapi>
<paths>
</require>
<get>
<summary>Suffer kid pull bed decision rest.</summary>
<parameters>
<name>param0</name>
<in>query</in>
<type>string</type>
</parameters>
<parameters>
<name>param1</name>
<in>query</in>
<type>string</type>
</parameters>
<parameters>
<name>param2</name>
<in>query</in>
<type>string</type>
</parameters>
<responses>
<200>
<description>Success</description>
</200>
<400>
<description>Bad Request</description>
</400>
</responses>
</get>
<//require>
</paths>
</api_specification>

[Assistant]
Approach:
1. Task: Convert api specification 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:
{
  "info": {
    "title": "Multi-tiered directional knowledge user API",
    "version": "1.0.0"
  },
  "openapi": "3.0.0",
  "paths": {
    "/require": {
      "get": {
        "summary": "Suffer kid pull bed decision rest.",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param1",
            "in": "query",
            "type": "string"
          },
          {
            "name": "param2",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "400": {
            "description": "Bad Request"
          }
        }
      }
    }
  }
}


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

[User] Create a CSV representation of product listing.

[Assistant]
Approach:
1. Task: Create product listing 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,currency,is_available,name,price,product_id,rating,reviews,specs_dimensions,specs_weight_kg,stock
electronics,USD,True,Function-based dedicated installation,364.53,PROD-738287,1.64,854,48x33x88cm,19.72,956
electronics,USD,True,Quality-focused holistic forecast,453.49,PROD-220410,3.4,204,71x68x73cm,22.05,907
electronics,USD,True,Synchronized high-level implementation,551.15,PROD-194348,2.7,28,21x96x93cm,12.15,925


[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>03dcbba2-8070-483e-a807-51e4c6301bf5</error_id>
<timestamp>2026-01-07T13:51:27.875594</timestamp>
<level>WARNING</level>
<service>stockService</service>
<message>Feel garden dark.</message>
<stack_trace>at another.py:304</stack_trace>
<stack_trace>at happy.py:331</stack_trace>
<stack_trace>at record.py:415</stack_trace>
<context>
<user_id>cedcf075-a01b-42e9-acdc-a300ea843992</user_id>
<request_id>acbfd7fd-a4ab-431b-a098-ac4537826f14</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": "03dcbba2-8070-483e-a807-51e4c6301bf5",
  "timestamp": "2026-01-07T13:51:27.875594",
  "level": "WARNING",
  "service": "stockService",
  "message": "Feel garden dark.",
  "stack_trace": [
    "at another.py:304",
    "at happy.py:331",
    "at record.py:415"
  ],
  "context": {
    "user_id": "cedcf075-a01b-42e9-acdc-a300ea843992",
    "request_id": "acbfd7fd-a4ab-431b-a098-ac4537826f14"
  }
}


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

[User] Create a JSON representation of lab result.

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

Output:
{
  "result_id": "LAB-2604144",
  "patient_mrn": "MRN-906692",
  "order_date": "2026-01-01",
  "panel": "Lipid",
  "results": [
    {
      "test": "Hemoglobin",
      "value": 180.3,
      "unit": "mg/dL",
      "flag": "high"
    },
    {
      "test": "Glucose",
      "value": 97.8,
      "unit": "mg/dL",
      "flag": "normal"
    },
    {
      "test": "Glucose",
      "value": 88.1,
      "unit": "g/dL",
      "flag": "high"
    },
    {
      "test": "Glucose",
      "value": 65.1,
      "unit": "mg/dL",
      "flag": "high"
    },
    {
      "test": "Hemoglobin",
      "value": 95.1,
      "unit": "g/dL",
      "flag": "high"
    },
    {
      "test": "Glucose",
      "value": 86.1,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "Hemoglobin",
      "value": 152.8,
      "unit": "mg/dL",
      "flag": "normal"
    }
  ]
}


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

[User]
Reformat the data below from CSV to JSON:
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
electronics,,USD,,,True,Synergistic regional success,90.86,PROD-274336,2.93,,602,23x29x34cm,41.78,989,,,
clothing,,USD,,,True,Synchronized bandwidth-monitored database,891.71,PROD-686329,4.85,,277,82x52x99cm,46.05,538,,,
books,,USD,,,True,Sharable 24/7 Graphic Interface,180.34,PROD-508713,2.29,,602,43x66x90cm,47.52,362,,,

[Assistant]
Approach:
1. Task: Convert customer review 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:
{
  "review_id": "REV-1369973",
  "product_id": "PROD-861575",
  "user_id": "a55bb48c-5dcb-464a-b952-9d377a4cb9c4",
  "rating": 1,
  "title": "Relationship job find one allow law through.",
  "content": "Now decide senior fact big window yourself. Difference appear seat coach rather once politics. Modern always able physical Republican best draw land.",
  "date": "2026-01-02",
  "verified_purchase": true,
  "helpful_votes": 8
}


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

[User] Create a TOML representation of clinical note.

[Assistant]
Approach:
1. Task: Create clinical note 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:
note_id = "CN-8293808"
encounter_date = "2026-01-05"
chief_complaint = "fever"
[[assessment]]
problem = "diabetes"
icd10 = "J39"

[patient]
mrn = "MRN-980604"
age = 24
gender = "F"

[vitals]
bp = "150/98"
hr = 93
temp_f = 102.4


[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: 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:
period = "February 2022"
total_revenue = 6033804
units_sold = 27183
[[channels]]
name = "wholesale"
revenue = 4147239
growth = 40.37

[[channels]]
name = "online"
revenue = 2663137
growth = 9.96

[[top_products]]
sku = "SKU-2538"
sales = 420940

[[top_products]]
sku = "SKU-8680"
sales = 364206

[[top_products]]
sku = "SKU-2062"
sales = 350623


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

[User]
Reformat the data below from JSON to XML:
{
  "rx_id": "RX-5160762",
  "patient_mrn": "MRN-568012",
  "prescriber": "Linda Gonzalez",
  "date": "2026-01-06",
  "medications": [
    {
      "name": "Lisinopril",
      "dosage": "20mg",
      "frequency": "TID",
      "quantity": 30
    },
    {
      "name": "Atorvastatin",
      "dosage": "5mg",
      "frequency": "daily",
      "quantity": 60
    }
  ]
}

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<prescription>
<rx_id>RX-5160762</rx_id>
<patient_mrn>MRN-568012</patient_mrn>
<prescriber>Linda Gonzalez</prescriber>
<date>2026-01-06</date>
<medications>
<name>Lisinopril</name>
<dosage>20mg</dosage>
<frequency>TID</frequency>
<quantity>30</quantity>
</medications>
<medications>
<name>Atorvastatin</name>
<dosage>5mg</dosage>
<frequency>daily</frequency>
<quantity>60</quantity>
</medications>
</prescription>


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

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

[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-139463
title: Effect of quite on arm
methodology:
  design: RCT
  sample_size: 371
  duration_days: 284
results:
- metric: safety
  baseline: 91.61
  endpoint: 76.9
  p_value: 0.4003
- metric: safety
  baseline: 58.96
  endpoint: 23.83
  p_value: 0.3437
- metric: safety
  baseline: 23.31
  endpoint: 88.63
  p_value: 0.3018
conclusion: non-significant


[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"?>
<experiment_result>
<experiment_id>EXP-168652</experiment_id>
<title>Effect of green on from</title>
<methodology>
<design>cohort</design>
<sample_size>797</sample_size>
<duration_days>186</duration_days>
</methodology>
<results>
<metric>safety</metric>
<baseline>24.16</baseline>
<endpoint>44.82</endpoint>
<p_value>0.0559</p_value>
</results>
<results>
<metric>efficacy</metric>
<baseline>16.54</baseline>
<endpoint>28.48</endpoint>
<p_value>0.2997</p_value>
</results>
<conclusion>non-significant</conclusion>
</experiment_result>

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

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


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

[User] Produce a JSON document for a prescription.

[Assistant]
Approach:
1. Task: Create prescription 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:
{
  "date": "2026-01-06",
  "patient_mrn": "MRN-259808",
  "rx_id": "RX-8539167",
  "prescriber": "Joseph Griffin",
  "medications": [
    {
      "name": "Atorvastatin",
      "dosage": "10mg",
      "frequency": "daily",
      "quantity": 90
    },
    {
      "name": "Lisinopril",
      "dosage": "10mg",
      "frequency": "TID",
      "quantity": 30
    },
    {
      "name": "Metformin",
      "dosage": "5mg",
      "frequency": "BID",
      "quantity": 30
    }
  ]
}


[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"?>
<student_assessment>
<assessment_id>ASMT-946690</assessment_id>
<student_id>STU-268675</student_id>
<course_id>CRS-1216</course_id>
<type>assignment</type>
<date>2026-01-01</date>
<scores>
<question>1</question>
<points_earned>10</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>2</question>
<points_earned>1</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>3</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>4</question>
<points_earned>8</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>5</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>6</question>
<points_earned>5</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>7</question>
<points_earned>8</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>0</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>10</question>
<points_earned>4</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>11</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>12</question>
<points_earned>2</points_earned>
<points_possible>10</points_possible>
</scores>
<total_score>81</total_score>
<percentage>50.49</percentage>
<grade>B</grade>
</student_assessment>

[Assistant]
Approach:
1. Task: Convert student assessment 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:
{
  "assessment_id": "ASMT-946690",
  "student_id": "STU-268675",
  "course_id": "CRS-1216",
  "type": "assignment",
  "date": "2026-01-01",
  "scores": [
    {
      "question": 1,
      "points_earned": 10,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 2,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 8,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 2,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 8,
      "points_possible": 10
    },
    {
      "question": 8,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 9,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 10,
      "points_earned": 4,
      "points_possible": 10
    },
    {
      "question": 11,
      "points_earned": 2,
      "points_possible": 10
    },
    {
      "question": 12,
      "points_earned": 2,
      "points_possible": 10
    }
  ],
  "total_score": 81,
  "percentage": 50.49,
  "grade": "B"
}


[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"?>
<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>

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

Output:
{
  "experiment_id": "EXP-139463",
  "title": "Effect of quite on arm",
  "methodology": {
    "design": "RCT",
    "sample_size": 371,
    "duration_days": 284
  },
  "results": [
    {
      "metric": "safety",
      "baseline": 91.61,
      "endpoint": 76.9,
      "p_value": 0.4003
    },
    {
      "metric": "safety",
      "baseline": 58.96,
      "endpoint": 23.83,
      "p_value": 0.3437
    },
    {
      "metric": "safety",
      "baseline": 23.31,
      "endpoint": 88.63,
      "p_value": 0.3018
    }
  ],
  "conclusion": "non-significant"
}


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

[User] Generate customer review data in YAML format.

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

Output:
review_id: REV-4235678
product_id: PROD-754494
user_id: 29638f7d-f697-4ae7-8fde-a1a70bcd6476
rating: 5
title: Help program law water necessary return clearly.
content: Team hold course throw center open enjoy old. Huge candidate still act decade
  civil. Safe middle indeed.
date: '2026-01-03'
verified_purchase: false
helpful_votes: 98


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

[User]
Reformat the data below from TOML to JSON:
error_id = "c6e14566-270a-4a5f-a250-5239ca9174ac"
timestamp = "2026-01-07T13:51:26.338486"
level = "ERROR"
service = "humanService"
message = "Fear she east talk better central."
stack_trace = [ "at college.py:218", "at economy.py:209", "at let.py:252", "at whole.py:18",]

[context]
user_id = "bda2bdb3-cc4e-4711-8927-aa42dc72d462"
request_id = "c7d51186-07ee-4743-a193-c5688ac0d6c5"

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

Output:
{
  "error_id": "c6e14566-270a-4a5f-a250-5239ca9174ac",
  "timestamp": "2026-01-07T13:51:26.338486",
  "level": "ERROR",
  "service": "humanService",
  "message": "Fear she east talk better central.",
  "stack_trace": [
    "at college.py:218",
    "at economy.py:209",
    "at let.py:252",
    "at whole.py:18"
  ],
  "context": {
    "user_id": "bda2bdb3-cc4e-4711-8927-aa42dc72d462",
    "request_id": "c7d51186-07ee-4743-a193-c5688ac0d6c5"
  }
}


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

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

[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>January 2024</period>
<total_revenue>3091656</total_revenue>
<units_sold>63856</units_sold>
<channels>
<name>wholesale</name>
<revenue>2739604</revenue>
<growth>-0.52</growth>
</channels>
<channels>
<name>online</name>
<revenue>247982</revenue>
<growth>14.46</growth>
</channels>
<top_products>
<sku>SKU-7268</sku>
<sales>409348</sales>
</top_products>
<top_products>
<sku>SKU-4030</sku>
<sales>70087</sales>
</top_products>
<top_products>
<sku>SKU-4380</sku>
<sales>298376</sales>
</top_products>
</sales_analytics>


[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"?>
<product_listing>
<reviews>730</reviews>
<category>books</category>
<stock>824</stock>
<is_available>True</is_available>
<specs>
<weight_kg>7.36</weight_kg>
<dimensions>46x21x76cm</dimensions>
</specs>
</product_listing>

[Assistant]
Approach:
1. Task: Convert product listing 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:
{
  "reviews": 730,
  "category": "books",
  "stock": 824,
  "is_available": true,
  "specs": {
    "weight_kg": 7.36,
    "dimensions": "46x21x76cm"
  }
}


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

[User] Produce a JSON document for a prescription.

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

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


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

[User] Output lab result as CSV.

[Assistant]
Approach:
1. Task: Create lab result 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:
order_date,panel,patient_mrn,result_id,results
2026-01-03,CBC,MRN-250544,LAB-4911961,"[{""test"": ""Hemoglobin"", ""value"": 121.1, ""unit"": ""g/dL"", ""flag"": ""normal""}, {""test"": ""Hemoglobin"", ""v"
2026-01-04,Lipid,MRN-558855,LAB-1684566,"[{""test"": ""Glucose"", ""value"": 116.8, ""unit"": ""g/dL"", ""flag"": ""normal""}, {""test"": ""Hemoglobin"", ""valu"
2026-01-04,CMP,MRN-113007,LAB-9575892,"[{""test"": ""Hemoglobin"", ""value"": 196.3, ""unit"": ""mg/dL"", ""flag"": ""high""}, {""test"": ""WBC"", ""value"": 9"
2026-01-03,CBC,MRN-158877,LAB-5161649,"[{""test"": ""WBC"", ""value"": 103.5, ""unit"": ""g/dL"", ""flag"": ""normal""}, {""test"": ""Glucose"", ""value"": 192"
2026-01-02,CBC,MRN-307360,LAB-4032045,"[{""test"": ""WBC"", ""value"": 199.0, ""unit"": ""g/dL"", ""flag"": ""normal""}, {""test"": ""WBC"", ""value"": 91.3, """


[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: complex - 8-10 fields, 3-4 levels
3. Format rules: consistent indentation, colon-space syntax, hyphen lists
4. Structure: Organize data logically with appropriate nesting
5. Populate fields with realistic example data based on the schema.

Output:
error_id: 234c0781-6e40-4e46-8b0d-ad1b39309eb0
timestamp: '2026-01-07T13:51:25.659911'
level: WARNING
service: beyondService
message: Section material entire top community.
stack_trace:
- at reason.py:320
- at always.py:365
- at red.py:27
context:
  user_id: 6765bd92-f82e-426f-a383-fa0a088a0752
  request_id: 4ccfb0b5-8185-4d26-902a-0379b3534400


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

[User] Generate api specification data in XML format.

[Assistant]
Approach:
1. Task: Create api specification 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"?>
<api_specification>
<openapi>3.0.0</openapi>
<info>
<title>Re-engineered mobile superstructure API</title>
<version>1.0.0</version>
</info>
<paths>
</benefit>
<get>
<summary>Chair media space recognize doctor would second me</summary>
<parameters>
<name>param0</name>
<in>query</in>
<type>string</type>
</parameters>
<parameters>
<name>param1</name>
<in>query</in>
<type>string</type>
</parameters>
<parameters>
<name>param2</name>
<in>query</in>
<type>string</type>
</parameters>
<responses>
<200>
<description>Success</description>
</200>
<400>
<description>Bad Request</description>
</400>
</responses>
</get>
<//benefit>
</paths>
</api_specification>


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

[User]
Transform this JSON data into XML format:
{
  "period": "March 2022",
  "total_revenue": 6503980,
  "units_sold": 7670,
  "channels": [
    {
      "name": "retail",
      "revenue": 3279241,
      "growth": 22.76
    },
    {
      "name": "wholesale",
      "revenue": 1469640,
      "growth": 16.07
    }
  ],
  "top_products": [
    {
      "sku": "SKU-8081",
      "sales": 71747
    },
    {
      "sku": "SKU-8211",
      "sales": 475825
    },
    {
      "sku": "SKU-4565",
      "sales": 485553
    }
  ]
}

[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>March 2022</period>
<total_revenue>6503980</total_revenue>
<units_sold>7670</units_sold>
<channels>
<name>retail</name>
<revenue>3279241</revenue>
<growth>22.76</growth>
</channels>
<channels>
<name>wholesale</name>
<revenue>1469640</revenue>
<growth>16.07</growth>
</channels>
<top_products>
<sku>SKU-8081</sku>
<sales>71747</sales>
</top_products>
<top_products>
<sku>SKU-8211</sku>
<sales>475825</sales>
</top_products>
<top_products>
<sku>SKU-4565</sku>
<sales>485553</sales>
</top_products>
</sales_analytics>


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

[User] Output research paper as TOML.

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

Output:
paper_id = "arXiv:2023.47681"
title = "Devolved bi-directional info-mediaries"
abstract = "Help field ten it. Pick eight last star pull. Act politics discuss."
keywords = [ "hard", "open", "right", "could", "sea",]
citations = 411
[[authors]]
name = "Christian Randolph"
affiliation = "Hunt-Haynes"
email = "patrickmaddox@example.net"

[[authors]]
name = "Allen Lewis"
affiliation = "Owen PLC"
email = "alexanderhenry@example.net"

[[authors]]
name = "Cody Martin"
affiliation = "Mcknight PLC"
email = "michael00@example.org"

[publication]
venue = "Cell"
year = 2023
doi = "10.8057/cNYF"


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

[User]
Reformat the data below from CSV to XML:
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
,"Issue suddenly such maybe. Military they second staff end agreement.
Close that speech alone know wi",,2026-01-02,34,,,,PROD-245093,1,REV-2691236,,,,,Now new sell exist just.,98b0ec97-f0e6-4eda-907b-c1488d5de2f5,False
books,,USD,,,True,Inverse logistical alliance,268.54,PROD-629069,4.05,,723,41x65x28cm,45.44,114,,,
clothing,,USD,,,True,Reactive value-added capability,376.36,PROD-583841,4.47,,150,15x87x45cm,42.87,661,,,

[Assistant]
Approach:
1. Task: Convert customer review 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"?>
<customer_review>
<review_id>REV-2691236</review_id>
<product_id>PROD-245093</product_id>
<user_id>98b0ec97-f0e6-4eda-907b-c1488d5de2f5</user_id>
<rating>1</rating>
<title>Now new sell exist just.</title>
<content>Issue suddenly such maybe. Military they second staff end agreement.
Close that speech alone know wish matter. Finally adult statement method.</content>
<date>2026-01-02</date>
<verified_purchase>False</verified_purchase>
<helpful_votes>34</helpful_votes>
</customer_review>


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

[User] Output research paper as XML.

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<research_paper>
<publication>
<venue>Cell</venue>
<year>2021</year>
<doi>10.3653/hXNF</doi>
</publication>
<funding_source>Kramer Ltd</funding_source>
<title>Configurable bandwidth-monitored strategy</title>
<authors>
<name>James Brown</name>
<affiliation>Ward-Davidson</affiliation>
<email>wilsonkristin@example.org</email>
</authors>
<authors>
<name>Luke Liu</name>
<affiliation>Obrien-Huynh</affiliation>
<email>jason08@example.org</email>
</authors>
<authors>
<name>Paula Gonzalez</name>
<affiliation>Jones-Howell</affiliation>
<email>brent41@example.org</email>
</authors>
</research_paper>


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

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

[Assistant]
Approach:
1. Task: Create error log 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:
error_id = "249c5a8d-effc-4f5a-9a18-79860cd86eba"
timestamp = "2026-01-07T13:51:25.844189"
level = "CRITICAL"
service = "createService"
message = "Capital carry as agree member lead one."
stack_trace = [ "at sign.py:359", "at college.py:115", "at try.py:133",]

[context]
user_id = "e110a198-5d8a-4242-b2e4-6ec6ae49ca80"
request_id = "939e8b37-2ce6-4ba0-bde6-ca285c8d396c"


[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"?>
<contract>
<contract_id>CTR-760727</contract_id>
<parties>
<name>King, Moore and Vazquez</name>
<role>party_a</role>
</parties>
<parties>
<name>Brown, Henry and Craig</name>
<role>party_b</role>
</parties>
<effective_date>2026-01-04</effective_date>
<term_months>15</term_months>
<value>170545</value>
<status>active</status>
</contract>

[Assistant]
Approach:
1. Task: Convert contract 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:
{
  "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"
}