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

[User]
Convert the following JSON to XML:
{
  "reviews": 730,
  "category": "books",
  "stock": 824,
  "is_available": true,
  "specs": {
    "weight_kg": 7.36,
    "dimensions": "46x21x76cm"
  }
}

[Assistant]
Approach:
1. Task: Convert product listing 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"?>
<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>


[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
books,USD,True,Focused neutral encoding,14.45,PROD-986141,4.59,564,42x99x90cm,27.7,826
books,USD,True,Networked real-time alliance,610.3,PROD-613696,1.11,816,81x28x86cm,3.04,6
electronics,USD,True,Profit-focused regional moratorium,736.03,PROD-426577,4.27,873,80x42x56cm,32.78,920
electronics,USD,True,Persevering user-facing approach,878.62,PROD-544349,3.32,682,78x45x53cm,0.64,2
clothing,USD,True,Operative hybrid application,570.35,PROD-304931,1.55,613,60x79x69cm,0.76,242


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

[User] Generate error log data in YAML format.

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

Output:
error_id: 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] Create a JSON representation of transaction record.

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

Output:
{
  "txn_id": "TXN-48552675",
  "customer_id": "19d3d880-f410-4b85-a35d-9c5777705d1f",
  "timestamp": "2026-01-07T13:51:26.837603",
  "items": [
    {
      "sku": "SKU-30306",
      "qty": 4,
      "price": 206.74
    }
  ],
  "subtotal": 1354.26,
  "tax": 33.08,
  "total": 766.28,
  "payment_method": "paypal"
}


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

[User]
Transform this YAML data into CSV format:
result_id: LAB-4827299
patient_mrn: MRN-871984
order_date: '2026-01-06'
panel: Lipid
results:
- test: WBC
  value: 156.4
  unit: g/dL
  flag: high
- test: WBC
  value: 186.0
  unit: mg/dL
  flag: low
- test: WBC
  value: 134.8
  unit: mg/dL
  flag: normal
- test: WBC
  value: 64.1
  unit: mg/dL
  flag: normal

[Assistant]
Approach:
1. Task: Convert lab result from YAML to CSV
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
order_date,panel,patient_mrn,result_id,results
2026-01-06,Lipid,MRN-871984,LAB-4827299,"[{""test"": ""WBC"", ""value"": 156.4, ""unit"": ""g/dL"", ""flag"": ""high""}, {""test"": ""WBC"", ""value"": 186.0, ""u"
2026-01-06,Lipid,MRN-523460,LAB-8432099,"[{""test"": ""WBC"", ""value"": 186.8, ""unit"": ""g/dL"", ""flag"": ""low""}, {""test"": ""Hemoglobin"", ""value"": 113"
2026-01-01,CBC,MRN-578034,LAB-8634747,"[{""test"": ""Glucose"", ""value"": 190.6, ""unit"": ""mg/dL"", ""flag"": ""normal""}, {""test"": ""WBC"", ""value"": 53"
2026-01-04,CBC,MRN-733262,LAB-3745926,"[{""test"": ""Glucose"", ""value"": 182.9, ""unit"": ""mg/dL"", ""flag"": ""low""}, {""test"": ""Glucose"", ""value"": 1"
2026-01-02,CMP,MRN-198669,LAB-3924597,"[{""test"": ""Glucose"", ""value"": 91.9, ""unit"": ""mg/dL"", ""flag"": ""high""}, {""test"": ""WBC"", ""value"": 110.4"


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

[User] Output transaction record as TOML.

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


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

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

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

Output:
category,content,currency,date,helpful_votes,is_available,name,price,product_id,rating,review_id,reviews,specs_dimensions,specs_weight_kg,stock,title,user_id,verified_purchase
,Develop particularly receive thing process sister source. Ago short fact start meeting note need. Ho,,2026-01-03,42,,,,PROD-905516,1,REV-3637179,,,,,Hand ok join you population.,cbc145e7-7f32-438c-ba90-61711bf65a15,False
books,,USD,,,True,Intuitive discrete orchestration,67.54,PROD-251066,2.84,,0,27x46x53cm,36.63,438,,,
clothing,,USD,,,True,Multi-channeled leadingedge core,214.25,PROD-884962,2.26,,580,95x14x63cm,6.53,819,,,
books,,USD,,,True,User-centric dedicated adapter,934.44,PROD-731260,1.02,,107,35x67x45cm,33.66,163,,,
clothing,,USD,,,True,Upgradable user-facing Internet solution,447.24,PROD-338199,3.94,,424,89x39x90cm,38.86,331,,,


[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:
{
  "error_id": "7af25ba6-3e63-43d5-89e6-c5a6b377b923",
  "timestamp": "2026-01-07T13:51:26.975438",
  "level": "CRITICAL",
  "service": "downService",
  "message": "Prevent true movie stop.",
  "stack_trace": [
    "at hand.py:436",
    "at whose.py:275",
    "at information.py:57",
    "at central.py:306"
  ],
  "context": {
    "user_id": "dc348ded-7e54-44aa-bfc8-75caf977ffdf",
    "request_id": "cc597ab0-ad26-419a-8cbb-1facc38a2f79"
  }
}

[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>7af25ba6-3e63-43d5-89e6-c5a6b377b923</error_id>
<timestamp>2026-01-07T13:51:26.975438</timestamp>
<level>CRITICAL</level>
<service>downService</service>
<message>Prevent true movie stop.</message>
<stack_trace>at hand.py:436</stack_trace>
<stack_trace>at whose.py:275</stack_trace>
<stack_trace>at information.py:57</stack_trace>
<stack_trace>at central.py:306</stack_trace>
<context>
<user_id>dc348ded-7e54-44aa-bfc8-75caf977ffdf</user_id>
<request_id>cc597ab0-ad26-419a-8cbb-1facc38a2f79</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"?>
<financial_report>
<company>Eaton, Mccarty and Lopez</company>
<ticker>OWKB</ticker>
<quarter>Q3 2021</quarter>
<revenue>46227283</revenue>
<expenses>75330506</expenses>
<net_income>13309525</net_income>
<eps>4.82</eps>
<metrics>
<profit_margin>29.61</profit_margin>
<roe>8.93</roe>
</metrics>
</financial_report>

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

Output:
{
  "company": "Eaton, Mccarty and Lopez",
  "ticker": "OWKB",
  "quarter": "Q3 2021",
  "revenue": 46227283,
  "expenses": 75330506,
  "net_income": 13309525,
  "eps": 4.82,
  "metrics": {
    "profit_margin": 29.61,
    "roe": 8.93
  }
}


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

[User]
Convert the following XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<financial_report>
<company>Richard Ltd</company>
<ticker>XHCL</ticker>
<quarter>Q4 2020</quarter>
<revenue>71255223</revenue>
<expenses>54916122</expenses>
<net_income>1190834</net_income>
<eps>4.63</eps>
<metrics>
<profit_margin>14.39</profit_margin>
<roe>19.33</roe>
</metrics>
</financial_report>

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

Output:
{
  "company": "Richard Ltd",
  "ticker": "XHCL",
  "quarter": "Q4 2020",
  "revenue": 71255223,
  "expenses": 54916122,
  "net_income": 1190834,
  "eps": 4.63,
  "metrics": {
    "profit_margin": 14.39,
    "roe": 19.33
  }
}


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

[User]
Transform this CSV data into TOML format:
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
,Team hold course throw center open enjoy old. Huge candidate still act decade civil. Safe middle ind,,2026-01-03,98,,,,PROD-754494,5,REV-4235678,,,,,Help program law water necessary return clearly.,29638f7d-f697-4ae7-8fde-a1a70bcd6476,False
clothing,,USD,,,True,Right-sized intermediate implementation,494.29,PROD-418663,1.88,,553,33x38x27cm,9.4,15,,,
books,,USD,,,True,Multi-layered analyzing instruction set,655.19,PROD-167668,1.85,,862,71x98x79cm,28.46,204,,,

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

Output:
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 CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User]
Convert the following TOML to CSV:
result_id = "LAB-2441646"
patient_mrn = "MRN-228148"
order_date = "2026-01-01"
panel = "Lipid"
[[results]]
test = "Glucose"
value = 81.9
unit = "g/dL"
flag = "high"

[[results]]
test = "WBC"
value = 107.7
unit = "mg/dL"
flag = "high"

[[results]]
test = "Hemoglobin"
value = 101.6
unit = "mg/dL"
flag = "normal"

[[results]]
test = "Hemoglobin"
value = 197.9
unit = "g/dL"
flag = "low"

[Assistant]
Approach:
1. Task: Convert lab result from TOML to CSV
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
order_date,panel,patient_mrn,result_id,results
2026-01-01,Lipid,MRN-228148,LAB-2441646,"[{""test"": ""Glucose"", ""value"": 81.9, ""unit"": ""g/dL"", ""flag"": ""high""}, {""test"": ""WBC"", ""value"": 107.7,"
2026-01-05,Lipid,MRN-127739,LAB-8358813,"[{""test"": ""WBC"", ""value"": 75.5, ""unit"": ""g/dL"", ""flag"": ""high""}, {""test"": ""WBC"", ""value"": 78.9, ""uni"
2026-01-05,CMP,MRN-631274,LAB-1949999,"[{""test"": ""Glucose"", ""value"": 89.3, ""unit"": ""g/dL"", ""flag"": ""high""}, {""test"": ""Hemoglobin"", ""value"":"
2026-01-03,Lipid,MRN-439326,LAB-3095529,"[{""test"": ""WBC"", ""value"": 115.9, ""unit"": ""mg/dL"", ""flag"": ""low""}, {""test"": ""WBC"", ""value"": 108.5, ""u"


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

[User] Create a XML representation of experiment result.

[Assistant]
Approach:
1. Task: Create experiment result in XML
2. Complexity: 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"?>
<experiment_result>
<experiment_id>EXP-883806</experiment_id>
<title>Effect of there on wife</title>
<methodology>
<design>observational</design>
<sample_size>200</sample_size>
<duration_days>32</duration_days>
</methodology>
<results>
<metric>efficacy</metric>
<baseline>15.64</baseline>
<endpoint>83.3</endpoint>
<p_value>0.4248</p_value>
</results>
<results>
<metric>efficacy</metric>
<baseline>36.36</baseline>
<endpoint>63.05</endpoint>
<p_value>0.0775</p_value>
</results>
<conclusion>significant</conclusion>
</experiment_result>


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

[User]
Reformat the data below from YAML to XML:
result_id: LAB-5956795
patient_mrn: MRN-953048
order_date: '2026-01-05'
panel: CBC
results:
- test: Glucose
  value: 138.6
  unit: g/dL
  flag: high
- test: Hemoglobin
  value: 69.4
  unit: g/dL
  flag: normal
- test: Hemoglobin
  value: 89.7
  unit: g/dL
  flag: normal
- test: Hemoglobin
  value: 191.9
  unit: g/dL
  flag: low
- test: WBC
  value: 169.5
  unit: mg/dL
  flag: low
- test: Glucose
  value: 187.7
  unit: mg/dL
  flag: low
- test: WBC
  value: 120.0
  unit: mg/dL
  flag: high

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<lab_result>
<result_id>LAB-5956795</result_id>
<patient_mrn>MRN-953048</patient_mrn>
<order_date>2026-01-05</order_date>
<panel>CBC</panel>
<results>
<test>Glucose</test>
<value>138.6</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>69.4</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>89.7</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>191.9</value>
<unit>g/dL</unit>
<flag>low</flag>
</results>
<results>
<test>WBC</test>
<value>169.5</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Glucose</test>
<value>187.7</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>WBC</test>
<value>120.0</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
</lab_result>


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

[User]
Reformat the data below from XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<clinical_note>
<note_id>CN-7937835</note_id>
<patient>
<mrn>MRN-749785</mrn>
<age>66</age>
<gender>M</gender>
</patient>
<encounter_date>2026-01-06</encounter_date>
<chief_complaint>chest pain</chief_complaint>
<vitals>
<bp>179/85</bp>

81

<temp_f>101.7</temp_f>
</vitals>
<assessment>
<problem>hypertension</problem>
<icd10>I52</icd10>
</assessment>
<assessment>
<problem>diabetes</problem>
<icd10>J76</icd10>
</assessment>
</clinical_note>

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

Output:
{
  "note_id": "CN-7937835",
  "patient": {
    "mrn": "MRN-749785",
    "age": 66,
    "gender": "M"
  },
  "encounter_date": "2026-01-06",
  "chief_complaint": "chest pain",
  "vitals": {
    "bp": "179/85",
    "hr": 81,
    "temp_f": 101.7
  },
  "assessment": [
    {
      "problem": "hypertension",
      "icd10": "I52"
    },
    {
      "problem": "diabetes",
      "icd10": "J76"
    }
  ]
}


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

[User] Create a CSV representation of clinical note.

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

Output:
assessment,chief_complaint,encounter_date,note_id,patient_age,patient_gender,patient_mrn,vitals_bp,vitals_hr,vitals_temp_f
"[{""problem"": ""hypertension"", ""icd10"": ""J35""}, {""problem"": ""hypertension"", ""icd10"": ""K44""}, {""problem",headache,2026-01-03,CN-6952117,50,F,MRN-227055,146/78,63,97.9
"[{""problem"": ""hypertension"", ""icd10"": ""I94""}]",chest pain,2026-01-05,CN-2067833,71,F,MRN-675833,168/65,95,98.3
"[{""problem"": ""diabetes"", ""icd10"": ""K57""}, {""problem"": ""hypertension"", ""icd10"": ""I87""}, {""problem"": """,headache,2026-01-01,CN-4751009,29,F,MRN-712542,100/106,116,99.4
"[{""problem"": ""hypertension"", ""icd10"": ""I60""}, {""problem"": ""hypertension"", ""icd10"": ""I39""}, {""problem",headache,2026-01-02,CN-5621336,33,M,MRN-749573,179/64,91,98.7
"[{""problem"": ""diabetes"", ""icd10"": ""K12""}]",chest pain,2026-01-04,CN-9266051,42,M,MRN-584393,97/87,74,100.8


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

[User] Output research paper as YAML.

[Assistant]
Approach:
1. Task: Create research paper 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:
paper_id: arXiv:2020.10600
title: Reactive bi-directional middleware
authors:
- name: Sarah Rose
  affiliation: Martin, Taylor and Mills
  email: adrianporter@example.com
- name: Samantha Garcia
  affiliation: Santiago LLC
  email: cbrewer@example.net
abstract: Carry prove certain tell event break. Thus not actually teacher girl perhaps.
keywords:
- public
- stock
- student
- hair
- hard
publication:
  venue: Cell
  year: 2022
  doi: 10.6112/KodW
citations: 273
funding_source: Salazar, Mullen and Johnson


[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: 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:
items:
- sku: SKU-89816
  qty: 1
  price: 437.44
- sku: SKU-65650
  qty: 1
  price: 455.72
- sku: SKU-98183
  qty: 3
  price: 337.62
total: 1900.4
payment_method: apple_pay


[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>March 2022</period>
<total_revenue>7500818</total_revenue>
<units_sold>73950</units_sold>
<channels>
<name>retail</name>
<revenue>528123</revenue>
<growth>12.05</growth>
</channels>
<channels>
<name>online</name>
<revenue>1160273</revenue>
<growth>23.93</growth>
</channels>
<top_products>
<sku>SKU-6153</sku>
<sales>328911</sales>
</top_products>
<top_products>
<sku>SKU-4483</sku>
<sales>21251</sales>
</top_products>
<top_products>
<sku>SKU-5114</sku>
<sales>21486</sales>
</top_products>
</sales_analytics>

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

Output:
{
  "period": "March 2022",
  "total_revenue": 7500818,
  "units_sold": 73950,
  "channels": [
    {
      "name": "retail",
      "revenue": 528123,
      "growth": 12.05
    },
    {
      "name": "online",
      "revenue": 1160273,
      "growth": 23.93
    }
  ],
  "top_products": [
    {
      "sku": "SKU-6153",
      "sales": 328911
    },
    {
      "sku": "SKU-4483",
      "sales": 21251
    },
    {
      "sku": "SKU-5114",
      "sales": 21486
    }
  ]
}


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

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

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

Output:
course_id: CRS-9279
code: WQT213
title: Ameliorated 3rdgeneration groupware 101
instructor: Mariah Wilson
credits: 3
term: Fall 2020
schedule:
- week: 1
  topic: together
- week: 2
  topic: statement
- week: 3
  topic: these
- week: 4
  topic: American
- week: 5
  topic: read
- week: 6
  topic: age
- week: 7
  topic: course
grading:
  homework: 30
  midterm: 30
  final: 40


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

[User] Generate lab result data in XML format.

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<lab_result>
<result_id>LAB-5956795</result_id>
<patient_mrn>MRN-953048</patient_mrn>
<order_date>2026-01-05</order_date>
<panel>CBC</panel>
<results>
<test>Glucose</test>
<value>138.6</value>
<unit>g/dL</unit>
<flag>high</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>69.4</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>89.7</value>
<unit>g/dL</unit>
<flag>normal</flag>
</results>
<results>
<test>Hemoglobin</test>
<value>191.9</value>
<unit>g/dL</unit>
<flag>low</flag>
</results>
<results>
<test>WBC</test>
<value>169.5</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>Glucose</test>
<value>187.7</value>
<unit>mg/dL</unit>
<flag>low</flag>
</results>
<results>
<test>WBC</test>
<value>120.0</value>
<unit>mg/dL</unit>
<flag>high</flag>
</results>
</lab_result>


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

[User] Output customer review as XML.

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<customer_review>
<review_id>REV-1384784</review_id>
<product_id>PROD-499626</product_id>
<user_id>4c396b1f-b1b7-4ef7-9587-0fda03a6f7f9</user_id>
<rating>1</rating>
<title>Respond out five.</title>
<content>I stay little Democrat money where stop. Than development activity.
Along ahead their standard early nor reason. Fight with minute learn soldier understand.</content>
<date>2026-01-05</date>
<verified_purchase>False</verified_purchase>
<helpful_votes>94</helpful_votes>
</customer_review>


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

[User]
Convert the following XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<financial_report>
<company>Hardy-Nguyen</company>
<ticker>ZAMS</ticker>
<quarter>Q3 2024</quarter>
<revenue>23201336</revenue>
<expenses>30350118</expenses>
<net_income>14673850</net_income>
<eps>3.22</eps>
<metrics>
<profit_margin>18.82</profit_margin>
<roe>21.21</roe>
</metrics>
</financial_report>

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

Output:
{
  "company": "Hardy-Nguyen",
  "ticker": "ZAMS",
  "quarter": "Q3 2024",
  "revenue": 23201336,
  "expenses": 30350118,
  "net_income": 14673850,
  "eps": 3.22,
  "metrics": {
    "profit_margin": 18.82,
    "roe": 21.21
  }
}


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

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

[Assistant]
Approach:
1. Task: Create sales analytics 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"?>
<sales_analytics>
<total_revenue>6798538</total_revenue>
<channels>
<name>online</name>
<revenue>2139770</revenue>
<growth>-9.99</growth>
</channels>
<channels>
<name>retail</name>
<revenue>1809716</revenue>
<growth>7.01</growth>
</channels>
<top_products>
<sku>SKU-3049</sku>
<sales>22854</sales>
</top_products>
<top_products>
<sku>SKU-5505</sku>
<sales>203281</sales>
</top_products>
<top_products>
<sku>SKU-9853</sku>
<sales>260538</sales>
</top_products>
</sales_analytics>


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

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

[Assistant]
Approach:
1. Task: Create lab result 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"?>
<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 TOML format. Think step-by-step, then generate syntactically perfect TOML.

[User]
Transform this YAML data into TOML format:
note_id: CN-6224145
patient:
  mrn: MRN-560569
  age: 88
  gender: F
encounter_date: '2026-01-04'
chief_complaint: headache
vitals:
  bp: 119/73
  hr: 63
  temp_f: 100.0
assessment:
- problem: hypertension
  icd10: I32

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

Output:
note_id = "CN-6224145"
encounter_date = "2026-01-04"
chief_complaint = "headache"
[[assessment]]
problem = "hypertension"
icd10 = "I32"

[patient]
mrn = "MRN-560569"
age = 88
gender = "F"

[vitals]
bp = "119/73"
hr = 63
temp_f = 100.0


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

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


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

[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-8350891</rx_id>
<patient_mrn>MRN-446802</patient_mrn>
<prescriber>James Bryant</prescriber>
<date>2026-01-03</date>
<medications>
<name>Lisinopril</name>
<dosage>10mg</dosage>
<frequency>daily</frequency>
<quantity>90</quantity>
</medications>
<medications>
<name>Metformin</name>
<dosage>20mg</dosage>
<frequency>TID</frequency>
<quantity>30</quantity>
</medications>
</prescription>


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

[User]
Convert the following CSV to XML:
category,currency,is_available,name,price,product_id,rating,reviews,specs_dimensions,specs_weight_kg,stock
clothing,USD,True,Self-enabling clear-thinking help-desk,510.64,PROD-319304,2.22,791,12x58x39cm,30.42,595
electronics,USD,True,Adaptive static help-desk,289.47,PROD-369125,3.74,254,41x45x47cm,10.12,883
electronics,USD,True,Multi-tiered contextually-based hardware,618.65,PROD-556941,2.93,107,37x66x61cm,0.43,230
clothing,USD,True,Persevering cohesive workforce,310.82,PROD-553118,4.84,102,44x63x58cm,37.66,785

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


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

[User]
Reformat the data below from JSON to YAML:
{
  "assessment_id": "ASMT-613322",
  "student_id": "STU-814570",
  "course_id": "CRS-3548",
  "type": "assignment",
  "date": "2026-01-01",
  "scores": [
    {
      "question": 1,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 10,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 8,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 6,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 7,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 7,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 1,
      "points_possible": 10
    }
  ],
  "total_score": 64,
  "percentage": 69.35,
  "grade": "C"
}

[Assistant]
Approach:
1. Task: Convert student assessment 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:
assessment_id: ASMT-613322
student_id: STU-814570
course_id: CRS-3548
type: assignment
date: '2026-01-01'
scores:
- question: 1
  points_earned: 1
  points_possible: 10
- question: 2
  points_earned: 10
  points_possible: 10
- question: 3
  points_earned: 8
  points_possible: 10
- question: 4
  points_earned: 6
  points_possible: 10
- question: 5
  points_earned: 7
  points_possible: 10
- question: 6
  points_earned: 7
  points_possible: 10
- question: 7
  points_earned: 1
  points_possible: 10
total_score: 64
percentage: 69.35
grade: C


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

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

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

Output:
error_id = "042db0a1-c9b1-49a9-9d69-5247a8c47159"
timestamp = "2026-01-07T13:51:27.377057"
level = "ERROR"
service = "whomService"
message = "Each reality trip red enough tough common."
stack_trace = [ "at note.py:398", "at purpose.py:452", "at onto.py:106", "at member.py:439",]

[context]
user_id = "70c0c786-0e43-42b2-8ec7-e975f39a71f7"
request_id = "95ca942a-a33a-413f-b1a1-a21e12fa0f30"


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

[User]
Reformat the data below from JSON to XML:
{
  "product_id": "PROD-791864",
  "name": "Decentralized heuristic migration",
  "category": "books",
  "price": 124.58,
  "currency": "USD",
  "stock": 601,
  "is_available": true,
  "rating": 3.59,
  "reviews": 538,
  "specs": {
    "weight_kg": 9.83,
    "dimensions": "30x16x25cm"
  }
}

[Assistant]
Approach:
1. Task: Convert product listing 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"?>
<product_listing>
<product_id>PROD-791864</product_id>
<name>Decentralized heuristic migration</name>
<category>books</category>
<price>124.58</price>
<currency>USD</currency>
<stock>601</stock>
<is_available>True</is_available>
<rating>3.59</rating>
<reviews>538</reviews>
<specs>
<weight_kg>9.83</weight_kg>
<dimensions>30x16x25cm</dimensions>
</specs>
</product_listing>


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

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

[Assistant]
Approach:
1. Task: Create error log in JSON
2. Complexity: 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:
{
  "error_id": "6b141850-4372-44c1-8cf7-fc47780a5017",
  "timestamp": "2026-01-07T13:51:26.727039",
  "level": "WARNING",
  "service": "fullService",
  "message": "Simply human maintain sport.",
  "stack_trace": [
    "at environmental.py:351",
    "at develop.py:28",
    "at send.py:431"
  ],
  "context": {
    "user_id": "fad7b695-3fb8-4313-ad8d-c2b1d4034196",
    "request_id": "2ca5c81e-d6c8-42a4-9e09-9715e928fe91"
  }
}


[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:
{
  "company": "Mcintyre, French and Sherman",
  "ticker": "GRIC",
  "quarter": "Q4 2021",
  "revenue": 78125638,
  "expenses": 13448897,
  "net_income": 14876002,
  "eps": 9.04,
  "metrics": {
    "profit_margin": 9.81,
    "roe": 23.34
  }
}

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<financial_report>
<company>Mcintyre, French and Sherman</company>
<ticker>GRIC</ticker>
<quarter>Q4 2021</quarter>
<revenue>78125638</revenue>
<expenses>13448897</expenses>
<net_income>14876002</net_income>
<eps>9.04</eps>
<metrics>
<profit_margin>9.81</profit_margin>
<roe>23.34</roe>
</metrics>
</financial_report>


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

[User]
Reformat the data below from TOML to YAML:
funding_source = "Kramer Ltd"
title = "Configurable bandwidth-monitored strategy"
[[authors]]
name = "James Brown"
affiliation = "Ward-Davidson"
email = "wilsonkristin@example.org"

[[authors]]
name = "Luke Liu"
affiliation = "Obrien-Huynh"
email = "jason08@example.org"

[[authors]]
name = "Paula Gonzalez"
affiliation = "Jones-Howell"
email = "brent41@example.org"

[publication]
venue = "Cell"
year = 2021
doi = "10.3653/hXNF"

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

Output:
publication:
  venue: Cell
  year: 2021
  doi: 10.3653/hXNF
funding_source: Kramer Ltd
title: Configurable bandwidth-monitored strategy
authors:
- name: James Brown
  affiliation: Ward-Davidson
  email: wilsonkristin@example.org
- name: Luke Liu
  affiliation: Obrien-Huynh
  email: jason08@example.org
- name: Paula Gonzalez
  affiliation: Jones-Howell
  email: brent41@example.org


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

[User]
Convert the following JSON to XML:
{
  "vitals": {
    "bp": "97/107",
    "hr": 107,
    "temp_f": 99.6
  },
  "patient": {
    "mrn": "MRN-705297",
    "age": 67,
    "gender": "F"
  },
  "chief_complaint": "chest pain",
  "assessment": [
    {
      "problem": "hypertension",
      "icd10": "K41"
    },
    {
      "problem": "hypertension",
      "icd10": "J91"
    },
    {
      "problem": "hypertension",
      "icd10": "J64"
    }
  ],
  "note_id": "CN-6154695"
}

[Assistant]
Approach:
1. Task: Convert clinical note 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"?>
<clinical_note>
<vitals>
<bp>97/107</bp>

107

<temp_f>99.6</temp_f>
</vitals>
<patient>
<mrn>MRN-705297</mrn>
<age>67</age>
<gender>F</gender>
</patient>
<chief_complaint>chest pain</chief_complaint>
<assessment>
<problem>hypertension</problem>
<icd10>K41</icd10>
</assessment>
<assessment>
<problem>hypertension</problem>
<icd10>J91</icd10>
</assessment>
<assessment>
<problem>hypertension</problem>
<icd10>J64</icd10>
</assessment>
<note_id>CN-6154695</note_id>
</clinical_note>


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

[User] Output news article as JSON.

[Assistant]
Approach:
1. Task: Create news article 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:
{
  "section": "Politics",
  "tags": [
    "machine",
    "fund",
    "station",
    "our"
  ],
  "published": "2026-01-05T17:58:55.100855",
  "views": 86295
}


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

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

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


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

[User]
Convert the following TOML to XML:
contract_id = "CTR-926700"
effective_date = "2026-01-04"
term_months = 36
value = 178791
status = "pending"
[[parties]]
name = "Kelly-Jackson"
role = "party_a"

[[parties]]
name = "Ramos, Hampton and Hartman"
role = "party_b"

[Assistant]
Approach:
1. Task: Convert contract from TOML 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"?>
<contract>
<contract_id>CTR-926700</contract_id>
<parties>
<name>Kelly-Jackson</name>
<role>party_a</role>
</parties>
<parties>
<name>Ramos, Hampton and Hartman</name>
<role>party_b</role>
</parties>
<effective_date>2026-01-04</effective_date>
<term_months>36</term_months>
<value>178791</value>
<status>pending</status>
</contract>


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

[User] Create a CSV representation of student assessment.

[Assistant]
Approach:
1. Task: Create student assessment 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:
assessment_id,code,course_id,credits,grading_final,grading_homework,grading_midterm,instructor,percentage,schedule,scores,term,title
ASMT-453812,,CRS-9368,,,,,,68.59,,"[{""question"": 1, ""points_earned"": 2, ""points_possible"": 10}, {""question"": 2, ""points_earned"": 3, ""po",,
,FVM153,CRS-6488,4,40,30,30,Amanda Klein,,"[{""week"": 1, ""topic"": ""past""}, {""week"": 2, ""topic"": ""bar""}, {""week"": 3, ""topic"": ""reveal""}, {""week"":",,Fall 2023,Open-architected grid-enabled monitoring 101
,WZR295,CRS-3107,3,40,30,30,Miss Kimberly Lynch,,"[{""week"": 1, ""topic"": ""growth""}, {""week"": 2, ""topic"": ""research""}, {""week"": 3, ""topic"": ""relationshi",,Fall 2024,Intuitive zero administration focus group 101
,YIL391,CRS-1193,3,40,30,30,Evan Li,,"[{""week"": 1, ""topic"": ""education""}, {""week"": 2, ""topic"": ""staff""}, {""week"": 3, ""topic"": ""tree""}, {""w",,Spring 2020,Synergized incremental forecast 101
,HXV292,CRS-6548,3,40,30,30,Richard Suarez,,"[{""week"": 1, ""topic"": ""bad""}, {""week"": 2, ""topic"": ""marriage""}, {""week"": 3, ""topic"": ""worker""}, {""we",,Fall 2021,Multi-tiered motivating throughput 101


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

[User]
Transform this CSV data into XML format:
article_id,author,content,headline,published,section,shares,tags,views
ART-9801838,Jeffrey Gordon,"Partner energy whose simply half because long. Pattern include thousand care four against.
Scene cen",Multi-lateral didactic encryption,2026-01-04T05:36:27.140251,Business,4423,"[""word"", ""public"", ""near""]",26731
ART-2768968,Teresa Mueller,"Up with fast fish. Appear enter financial. Environmental that near sign special.
Us ball huge citize",Stand-alone value-added standardization,2026-01-06T23:36:49.876750,Business,3661,"[""risk"", ""prepare"", ""bar"", ""teacher"", ""guess""]",85348
ART-2157250,David Scott,Because material what wall owner. Popular blood only pay reflect east behavior. So involve foot resu,Centralized analyzing matrix,2026-01-01T12:21:52.193791,Politics,2869,"[""new"", ""type"", ""increase""]",16269

[Assistant]
Approach:
1. Task: Convert news article from CSV 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"?>
<news_article>
<article_id>ART-9801838</article_id>
<headline>Multi-lateral didactic encryption</headline>
<author>Jeffrey Gordon</author>
<published>2026-01-04T05:36:27.140251</published>

Business

<content>Partner energy whose simply half because long. Pattern include thousand care four against.
Scene center certainly letter already rate among. Do amount account court term same.
Step usually ground onto study myself.</content>
<tags>word</tags>
<tags>public</tags>
<tags>near</tags>
<views>26731</views>
<shares>4423</shares>
</news_article>


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

[User] Generate financial report data in TOML format.

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

Output:
company = "Rodriguez, Sanchez and French"
ticker = "TOPI"
quarter = "Q3 2022"
revenue = 30596216
expenses = 79425858
net_income = 10739155
eps = 9.27

[metrics]
profit_margin = 21.79
roe = 22.32


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

[User] Output news article as CSV.

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

Output:
article_id,author,content,headline,published,section,shares,tags,views
ART-2188860,Michael Logan,"Include start build no effort continue. Mention sister mention threat.
Choose herself case writer qu",Advanced well-modulated service-desk,2026-01-03T08:09:52.608144,Politics,1092,"[""hear"", ""civil"", ""might"", ""way"", ""win""]",35184
ART-5279021,Kyle Gross,"Century seven pattern source third direction.
Decide term play arm society book middle civil. Read c",Triple-buffered national framework,2026-01-01T19:32:05.732344,Politics,3800,"[""memory"", ""own"", ""practice"", ""might""]",21505
ART-8637940,Lindsey Anderson,Away third sit understand provide. Perform thus management science eye arrive. Cut door stay letter.,Phased empowering flexibility,2026-01-04T05:38:14.171487,Sports,2145,"[""she"", ""animal"", ""history""]",53299
ART-9980906,Matthew Avila MD,"Each hand kitchen require any tax right. Dream themselves seven. Thank process defense.
Knowledge ca",Switchable attitude-oriented model,2026-01-02T12:21:09.133361,Politics,212,"[""source"", ""west"", ""prevent""]",20058
ART-4267125,Dustin Carr,Need well lose others. Truth main wait according vote enter system. Against common conference skill ,Diverse neutral parallelism,2026-01-04T05:56:49.453140,Tech,4281,"[""available"", ""certainly"", ""require"", ""task"", ""section""]",74972


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

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

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

Output:
company = "Eaton, Mccarty and Lopez"
ticker = "OWKB"
quarter = "Q3 2021"
revenue = 46227283
expenses = 75330506
net_income = 13309525
eps = 4.82

[metrics]
profit_margin = 29.61
roe = 8.93


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

[User] Generate student assessment data in YAML format.

[Assistant]
Approach:
1. Task: Create student assessment in YAML
2. Complexity: 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:
assessment_id: ASMT-718533
student_id: STU-781568
course_id: CRS-7994
type: assignment
date: '2026-01-03'
scores:
- question: 1
  points_earned: 8
  points_possible: 10
- question: 2
  points_earned: 4
  points_possible: 10
- question: 3
  points_earned: 5
  points_possible: 10
- question: 4
  points_earned: 8
  points_possible: 10
- question: 5
  points_earned: 0
  points_possible: 10
- question: 6
  points_earned: 5
  points_possible: 10
- question: 7
  points_earned: 2
  points_possible: 10
- question: 8
  points_earned: 2
  points_possible: 10
- question: 9
  points_earned: 6
  points_possible: 10
- question: 10
  points_earned: 4
  points_possible: 10
- question: 11
  points_earned: 8
  points_possible: 10
- question: 12
  points_earned: 4
  points_possible: 10
- question: 13
  points_earned: 7
  points_possible: 10
- question: 14
  points_earned: 3
  points_possible: 10
total_score: 62
percentage: 88.34
grade: B


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

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

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

Output:
post_id = "POST-15490463"
platform = "instagram"
user_id = "dcf0c28f-6ad6-4153-873b-0cd39fa73a46"
username = "allenjames"
content = "Eight television free focus window leave. Education Democrat past music mother able. Fund season represent when analysis movement.\nEverything over rich serious forget. Fire particular western."
timestamp = "2026-01-07T13:51:27.338072"
hashtags = [ "#left", "#long", "#instead", "#budget",]

[engagement]
likes = 85
shares = 112
comments = 292


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

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


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

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


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

[User]
Convert the following CSV to YAML:
assessment,chief_complaint,encounter_date,note_id,patient_age,patient_gender,patient_mrn,vitals_bp,vitals_hr,vitals_temp_f
"[{""problem"": ""diabetes"", ""icd10"": ""I12""}, {""problem"": ""diabetes"", ""icd10"": ""K62""}]",chest pain,2026-01-02,CN-4315709,89,M,MRN-534874,165/61,112,99.4
"[{""problem"": ""hypertension"", ""icd10"": ""J39""}, {""problem"": ""diabetes"", ""icd10"": ""K19""}]",headache,2026-01-02,CN-9175166,77,F,MRN-849686,118/64,86,101.3
"[{""problem"": ""diabetes"", ""icd10"": ""K77""}, {""problem"": ""diabetes"", ""icd10"": ""J61""}]",fever,2026-01-03,CN-4266501,18,M,MRN-721513,124/95,119,101.3

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

Output:
note_id: CN-4315709
patient:
  mrn: MRN-534874
  age: 89
  gender: M
encounter_date: '2026-01-02'
chief_complaint: chest pain
vitals:
  bp: 165/61
  hr: 112
  temp_f: 99.4
assessment:
- problem: diabetes
  icd10: I12
- problem: diabetes
  icd10: K62


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

[User] Output sales analytics as JSON.

[Assistant]
Approach:
1. Task: Create sales analytics 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:
{
  "units_sold": 11543,
  "period": "June 2021",
  "channels": [
    {
      "name": "online",
      "revenue": 1417680,
      "growth": 41.94
    },
    {
      "name": "wholesale",
      "revenue": 3176166,
      "growth": 26.78
    }
  ],
  "total_revenue": 8223229,
  "top_products": [
    {
      "sku": "SKU-9002",
      "sales": 412560
    },
    {
      "sku": "SKU-3918",
      "sales": 426996
    },
    {
      "sku": "SKU-1618",
      "sales": 496165
    }
  ]
}


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

[User] Output product listing as XML.

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<product_listing>
<product_id>PROD-831490</product_id>
<name>Seamless optimal task-force</name>
<category>electronics</category>
<price>975.8</price>
<currency>USD</currency>
<stock>924</stock>
<is_available>True</is_available>
<rating>2.71</rating>
<reviews>121</reviews>
<specs>
<weight_kg>17.77</weight_kg>
<dimensions>52x27x46cm</dimensions>
</specs>
</product_listing>


[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:
{
  "course_id": "CRS-6496",
  "code": "TMG245",
  "instructor": "Maria Bowers",
  "schedule": [
    {
      "week": 1,
      "topic": "situation"
    },
    {
      "week": 2,
      "topic": "firm"
    },
    {
      "week": 3,
      "topic": "involve"
    },
    {
      "week": 4,
      "topic": "change"
    },
    {
      "week": 5,
      "topic": "yard"
    },
    {
      "week": 6,
      "topic": "morning"
    },
    {
      "week": 7,
      "topic": "other"
    },
    {
      "week": 8,
      "topic": "left"
    }
  ]
}

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<course_syllabus>
<course_id>CRS-6496</course_id>
TMG245
<instructor>Maria Bowers</instructor>
<schedule>
<week>1</week>
<topic>situation</topic>
</schedule>
<schedule>
<week>2</week>
<topic>firm</topic>
</schedule>
<schedule>
<week>3</week>
<topic>involve</topic>
</schedule>
<schedule>
<week>4</week>
<topic>change</topic>
</schedule>
<schedule>
<week>5</week>
<topic>yard</topic>
</schedule>
<schedule>
<week>6</week>
<topic>morning</topic>
</schedule>
<schedule>
<week>7</week>
<topic>other</topic>
</schedule>
<schedule>
<week>8</week>
<topic>left</topic>
</schedule>
</course_syllabus>


[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: 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:
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 YAML format. Think step-by-step, then generate syntactically perfect YAML.

[User]
Convert the following TOML to YAML:
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"

[[results]]
test = "Glucose"
value = 97.8
unit = "mg/dL"
flag = "normal"

[[results]]
test = "Glucose"
value = 88.1
unit = "g/dL"
flag = "high"

[[results]]
test = "Glucose"
value = 65.1
unit = "mg/dL"
flag = "high"

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

[[results]]
test = "Glucose"
value = 86.1
unit = "mg/dL"
flag = "low"

[[results]]
test = "Hemoglobin"
value = 152.8
unit = "mg/dL"
flag = "normal"

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

Output:
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] Output student assessment as JSON.

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

Output:
{
  "assessment_id": "ASMT-871992",
  "student_id": "STU-916232",
  "course_id": "CRS-5865",
  "type": "quiz",
  "date": "2026-01-05",
  "scores": [
    {
      "question": 1,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 6,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 10,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 2,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 8,
      "points_earned": 7,
      "points_possible": 10
    },
    {
      "question": 9,
      "points_earned": 2,
      "points_possible": 10
    },
    {
      "question": 10,
      "points_earned": 4,
      "points_possible": 10
    },
    {
      "question": 11,
      "points_earned": 5,
      "points_possible": 10
    }
  ],
  "total_score": 83,
  "percentage": 70.57,
  "grade": "B"
}


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

[User] Create a TOML representation of student assessment.

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

Output:
assessment_id = "ASMT-744456"
student_id = "STU-954920"
course_id = "CRS-3021"
type = "exam"
date = "2026-01-03"
total_score = 97
percentage = 89.67
grade = "C"
[[scores]]
question = 1
points_earned = 7
points_possible = 10

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

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

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

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

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

[[scores]]
question = 7
points_earned = 5
points_possible = 10

[[scores]]
question = 8
points_earned = 1
points_possible = 10

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

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

[[scores]]
question = 11
points_earned = 9
points_possible = 10

[[scores]]
question = 12
points_earned = 9
points_possible = 10


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

[User]
Convert the following JSON to TOML:
{
  "review_id": "REV-7169946",
  "product_id": "PROD-908355",
  "user_id": "109a43ce-0a9d-47ca-b5cc-5940dcb3a5c0",
  "rating": 3,
  "title": "Suffer kid daughter race still baby.",
  "content": "Young lot card leave book. Thank agency tree shake. Manage organization reduce from sort soldier.\nRun rich marriage parent discover kitchen. Positive person baby list positive really range.",
  "date": "2026-01-05",
  "verified_purchase": true,
  "helpful_votes": 3
}

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

Output:
review_id = "REV-7169946"
product_id = "PROD-908355"
user_id = "109a43ce-0a9d-47ca-b5cc-5940dcb3a5c0"
rating = 3
title = "Suffer kid daughter race still baby."
content = "Young lot card leave book. Thank agency tree shake. Manage organization reduce from sort soldier.\nRun rich marriage parent discover kitchen. Positive person baby list positive really range."
date = "2026-01-05"
verified_purchase = true
helpful_votes = 3


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

[User]
Transform this YAML data into JSON format:
review_id: REV-1521978
product_id: PROD-894495
user_id: 0e93d4dc-0d39-4af2-8ec3-d8d1250011b9
rating: 4
title: Great how data open such.
content: City not either situation all along might. Under could expect build member.
  Fish ready onto white majority.
date: '2026-01-05'
verified_purchase: false
helpful_votes: 57

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


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

[User] Output sales analytics as YAML.

[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: 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 TOML data into JSON format:
openapi = "3.0.0"

[info]
title = "Reactive exuding frame API"
version = "1.0.0"

[paths."/second".get]
summary = "Life century work throughout however miss another."
[[paths."/second".get.parameters]]
name = "param0"
in = "query"
type = "string"

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

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

[Assistant]
Approach:
1. Task: Convert api specification from TOML 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:
{
  "openapi": "3.0.0",
  "info": {
    "title": "Reactive exuding frame API",
    "version": "1.0.0"
  },
  "paths": {
    "/second": {
      "get": {
        "summary": "Life century work throughout however miss another.",
        "parameters": [
          {
            "name": "param0",
            "in": "query",
            "type": "string"
          }
        ],
        "responses": {
          "200": {
            "description": "Success"
          },
          "400": {
            "description": "Bad Request"
          }
        }
      }
    }
  }
}


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

[User] Create a YAML representation of experiment result.

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

Output:
experiment_id: EXP-883806
title: Effect of there on wife
methodology:
  design: observational
  sample_size: 200
  duration_days: 32
results:
- metric: efficacy
  baseline: 15.64
  endpoint: 83.3
  p_value: 0.4248
- metric: efficacy
  baseline: 36.36
  endpoint: 63.05
  p_value: 0.0775
conclusion: significant


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

[User]
Reformat the data below from XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<student_assessment>
<assessment_id>ASMT-503362</assessment_id>
<student_id>STU-235214</student_id>
<course_id>CRS-9952</course_id>
<type>assignment</type>
<date>2026-01-01</date>
<scores>
<question>1</question>
<points_earned>1</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>2</question>
<points_earned>0</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>3</question>
<points_earned>0</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>4</question>
<points_earned>4</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>5</question>
<points_earned>1</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>6</question>
<points_earned>6</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>7</question>
<points_earned>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>4</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>10</question>
<points_earned>0</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>11</question>
<points_earned>1</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>12</question>
<points_earned>8</points_earned>
<points_possible>10</points_possible>
</scores>
<total_score>52</total_score>
<percentage>94.35</percentage>
<grade>C</grade>
</student_assessment>

[Assistant]
Approach:
1. Task: Convert student assessment 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:
{
  "assessment_id": "ASMT-503362",
  "student_id": "STU-235214",
  "course_id": "CRS-9952",
  "type": "assignment",
  "date": "2026-01-01",
  "scores": [
    {
      "question": 1,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 4,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 6,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 8,
      "points_possible": 10
    },
    {
      "question": 8,
      "points_earned": 5,
      "points_possible": 10
    },
    {
      "question": 9,
      "points_earned": 4,
      "points_possible": 10
    },
    {
      "question": 10,
      "points_earned": 0,
      "points_possible": 10
    },
    {
      "question": 11,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 12,
      "points_earned": 8,
      "points_possible": 10
    }
  ],
  "total_score": 52,
  "percentage": 94.35,
  "grade": "C"
}


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

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

[Assistant]
Approach:
1. Task: Convert prescription from JSON to 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"?>
<prescription>
<rx_id>RX-7309915</rx_id>
<patient_mrn>MRN-389849</patient_mrn>
<prescriber>Briana Dickerson</prescriber>
<date>2026-01-03</date>
<medications>
<name>Metformin</name>
<dosage>20mg</dosage>
<frequency>daily</frequency>
<quantity>30</quantity>
</medications>
</prescription>


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

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

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

Output:
{
  "assessment_id": "ASMT-613322",
  "student_id": "STU-814570",
  "course_id": "CRS-3548",
  "type": "assignment",
  "date": "2026-01-01",
  "scores": [
    {
      "question": 1,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 10,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 8,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 6,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 7,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 7,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 1,
      "points_possible": 10
    }
  ],
  "total_score": 64,
  "percentage": 69.35,
  "grade": "C"
}


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

[User] Generate prescription data in XML format.

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

Output:
<?xml version="1.0" encoding="UTF-8"?>
<prescription>
<rx_id>RX-3305496</rx_id>
<patient_mrn>MRN-551064</patient_mrn>
<prescriber>Eric Jackson</prescriber>
<date>2026-01-03</date>
<medications>
<name>Atorvastatin</name>
<dosage>20mg</dosage>
<frequency>daily</frequency>
<quantity>90</quantity>
</medications>
</prescription>


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

[User]
Convert the following TOML to YAML:
title = "Stand-alone empowering functionalities 101"
[[schedule]]
week = 1
topic = "class"

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

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

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

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

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

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

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

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

Output:
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 CSV format. Think step-by-step, then generate syntactically perfect CSV.

[User]
Reformat the data below from JSON to CSV:
{
  "experiment_id": "EXP-883806",
  "title": "Effect of there on wife",
  "methodology": {
    "design": "observational",
    "sample_size": 200,
    "duration_days": 32
  },
  "results": [
    {
      "metric": "efficacy",
      "baseline": 15.64,
      "endpoint": 83.3,
      "p_value": 0.4248
    },
    {
      "metric": "efficacy",
      "baseline": 36.36,
      "endpoint": 63.05,
      "p_value": 0.0775
    }
  ],
  "conclusion": "significant"
}

[Assistant]
Approach:
1. Task: Convert experiment result from JSON to CSV
2. Complexity: complex - 8-10 fields, 3-4 levels
3. Format rules: header row, comma-separated, quoted fields with commas
4. Structure: Organize data logically with appropriate nesting
5. Transform values to match the target format specifications.

Output:
conclusion,experiment_id,methodology_design,methodology_duration_days,methodology_sample_size,results,title
significant,EXP-883806,observational,32,200,"[{""metric"": ""efficacy"", ""baseline"": 15.64, ""endpoint"": 83.3, ""p_value"": 0.4248}, {""metric"": ""efficac",Effect of there on wife
non-significant,EXP-943693,RCT,131,357,"[{""metric"": ""safety"", ""baseline"": 10.27, ""endpoint"": 20.86, ""p_value"": 0.067}, {""metric"": ""efficacy""",Effect of including on east
non-significant,EXP-341513,RCT,151,439,"[{""metric"": ""efficacy"", ""baseline"": 90.71, ""endpoint"": 95.42, ""p_value"": 0.2598}, {""metric"": ""effica",Effect of responsibility on old
non-significant,EXP-524543,RCT,306,787,"[{""metric"": ""efficacy"", ""baseline"": 95.88, ""endpoint"": 61.64, ""p_value"": 0.2484}, {""metric"": ""effica",Effect of good on character


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


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

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

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

Output:
assessment_id = "ASMT-672485"
student_id = "STU-183204"
course_id = "CRS-8704"
type = "quiz"
date = "2026-01-01"
total_score = 82
percentage = 72.48
grade = "A"
[[scores]]
question = 1
points_earned = 0
points_possible = 10

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

[[scores]]
question = 3
points_earned = 1
points_possible = 10

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

[[scores]]
question = 5
points_earned = 1
points_possible = 10

[[scores]]
question = 6
points_earned = 8
points_possible = 10

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

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

[[scores]]
question = 9
points_earned = 0
points_possible = 10

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

[[scores]]
question = 11
points_earned = 5
points_possible = 10

[[scores]]
question = 12
points_earned = 0
points_possible = 10


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

[User] Create a XML representation of contract.

[Assistant]
Approach:
1. Task: Create contract 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"?>
<contract>
<contract_id>CTR-619223</contract_id>
<parties>
<name>Mcdaniel LLC</name>
<role>party_a</role>
</parties>
<parties>
<name>Hunt-Bowman</name>
<role>party_b</role>
</parties>
<effective_date>2026-01-03</effective_date>
<term_months>21</term_months>
<value>87478</value>
<status>pending</status>
</contract>


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

[User]
Reformat the data below from XML to JSON:
<?xml version="1.0" encoding="UTF-8"?>
<financial_report>
<company>Snow, Shelton and Parsons</company>
<ticker>LYEH</ticker>
<quarter>Q3 2022</quarter>
<revenue>49988929</revenue>
<expenses>3428077</expenses>
<net_income>8543670</net_income>
<eps>3.41</eps>
<metrics>
<profit_margin>11.21</profit_margin>
<roe>5.99</roe>
</metrics>
</financial_report>

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

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


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

[User] Generate financial report data in YAML format.

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

Output:
revenue: 81140617
metrics:
  profit_margin: 25.46
  roe: 20.6
quarter: Q1 2021


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

[User] Create a TOML representation of student assessment.

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

Output:
assessment_id = "ASMT-871992"
student_id = "STU-916232"
course_id = "CRS-5865"
type = "quiz"
date = "2026-01-05"
total_score = 83
percentage = 70.57
grade = "B"
[[scores]]
question = 1
points_earned = 1
points_possible = 10

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

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

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

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

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

[[scores]]
question = 7
points_earned = 0
points_possible = 10

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

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

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

[[scores]]
question = 11
points_earned = 5
points_possible = 10


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

[User] Create a YAML representation of financial report.

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

Output:
eps: 6.0
company: Richardson-Gonzalez
quarter: Q3 2020
net_income: 13953656


[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: 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"?>
<research_paper>
<paper_id>arXiv:2020.10600</paper_id>
<title>Reactive bi-directional middleware</title>
<authors>
<name>Sarah Rose</name>
<affiliation>Martin, Taylor and Mills</affiliation>
<email>adrianporter@example.com</email>
</authors>
<authors>
<name>Samantha Garcia</name>
<affiliation>Santiago LLC</affiliation>
<email>cbrewer@example.net</email>
</authors>
<abstract>Carry prove certain tell event break. Thus not actually teacher girl perhaps.</abstract>
<keywords>public</keywords>
<keywords>stock</keywords>
<keywords>student</keywords>
<keywords>hair</keywords>
<keywords>hard</keywords>
<publication>
<venue>Cell</venue>
<year>2022</year>
<doi>10.6112/KodW</doi>
</publication>
<citations>273</citations>
<funding_source>Salazar, Mullen and Johnson</funding_source>
</research_paper>


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

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

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

Output:
{
  "result_id": "LAB-4504067",
  "patient_mrn": "MRN-266543",
  "order_date": "2026-01-05",
  "panel": "Lipid",
  "results": [
    {
      "test": "Glucose",
      "value": 181.1,
      "unit": "g/dL",
      "flag": "normal"
    },
    {
      "test": "Hemoglobin",
      "value": 164.3,
      "unit": "mg/dL",
      "flag": "low"
    },
    {
      "test": "Glucose",
      "value": 140.0,
      "unit": "g/dL",
      "flag": "normal"
    },
    {
      "test": "Hemoglobin",
      "value": 113.0,
      "unit": "mg/dL",
      "flag": "high"
    }
  ]
}


[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:
{
  "assessment_id": "ASMT-613322",
  "student_id": "STU-814570",
  "course_id": "CRS-3548",
  "type": "assignment",
  "date": "2026-01-01",
  "scores": [
    {
      "question": 1,
      "points_earned": 1,
      "points_possible": 10
    },
    {
      "question": 2,
      "points_earned": 10,
      "points_possible": 10
    },
    {
      "question": 3,
      "points_earned": 8,
      "points_possible": 10
    },
    {
      "question": 4,
      "points_earned": 6,
      "points_possible": 10
    },
    {
      "question": 5,
      "points_earned": 7,
      "points_possible": 10
    },
    {
      "question": 6,
      "points_earned": 7,
      "points_possible": 10
    },
    {
      "question": 7,
      "points_earned": 1,
      "points_possible": 10
    }
  ],
  "total_score": 64,
  "percentage": 69.35,
  "grade": "C"
}

[Assistant]
Approach:
1. Task: Convert student assessment 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"?>
<student_assessment>
<assessment_id>ASMT-613322</assessment_id>
<student_id>STU-814570</student_id>
<course_id>CRS-3548</course_id>
<type>assignment</type>
<date>2026-01-01</date>
<scores>
<question>1</question>
<points_earned>1</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>2</question>
<points_earned>10</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>3</question>
<points_earned>8</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>4</question>
<points_earned>6</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>5</question>
<points_earned>7</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>6</question>
<points_earned>7</points_earned>
<points_possible>10</points_possible>
</scores>
<scores>
<question>7</question>
<points_earned>1</points_earned>
<points_possible>10</points_possible>
</scores>
<total_score>64</total_score>
<percentage>69.35</percentage>
<grade>C</grade>
</student_assessment>


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

[User]
Transform this XML data into JSON format:
<?xml version="1.0" encoding="UTF-8"?>
<financial_report>
<expenses>65628233</expenses>
<eps>5.75</eps>
<quarter>Q2 2023</quarter>
<revenue>73745517</revenue>
<net_income>6367407</net_income>
</financial_report>

[Assistant]
Approach:
1. Task: Convert financial report 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:
{
  "expenses": 65628233,
  "eps": 5.75,
  "quarter": "Q2 2023",
  "revenue": 73745517,
  "net_income": 6367407
}


[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:2020.59386"
title = "Self-enabling demand-driven application"
abstract = "Before today call network oil future. Admit itself catch song various rest. Personal worry quality director."
keywords = [ "song", "TV", "southern",]
citations = 274
[[authors]]
name = "Keith Wilcox"
affiliation = "Ruiz-Torres"
email = "martinstephanie@example.org"

[[authors]]
name = "Jennifer Mccoy"
affiliation = "Thompson-Marshall"
email = "brianvelasquez@example.org"

[publication]
venue = "Nature"
year = 2020
doi = "10.1369/MikD"