Json output formatter
[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.
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Format enterprise data retrieval results as JSON with token tracking. Use this skill when you need to output query results to JSON files in the format {"q1": {"answer": [...], "tokens": N}, ...}. Handles answer list formatting, token counting, and file output validation.
SKILL.md
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JSON Output Format for Data Retrieval
Output Structure
Every answer must follow this exact format:
{
"q1": {"answer": ["item1", "item2"], "tokens": 123},
"q2": {"answer": ["item1"], "tokens": 456},
"q3": {"answer": ["url1", "url2"], "tokens": 789}
}
Rules
answer: Always a list, even if only 1 itemtokens: Positive numeric value (not a string)- Questions keys: Match question identifiers (q1, q2, q3, etc.)
Answer Formatting
Multiple Items (list with length > 1):
"answer": ["eid_abc123", "eid_def456", "eid_ghi789"]
Single Item (still in a list):
"answer": ["eid_abc123"]
Empty Results:
"answer": []
Token Counting
Track tokens consumed for each question:
- Note the token count from API usage or elapsed processing
- Store as positive integer in
tokensfield - Total tokens = sum of all question tokens
Python code to write output:
import json
results = {
"q1": {"answer": ["eid_1e9356f5", "eid_06cddbb3"], "tokens": 150},
"q2": {"answer": ["eid_99835861"], "tokens": 200},
"q3": {"answer": ["https://example.com/demo"], "tokens": 100}
}
with open('/root/answer.json', 'w') as f:
json.dump(results, f, indent=2)
Validation Checklist
- All questions answered (q1, q2, q3, ...)
- Each answer is a list
- Single items still in list format
- Token counts are positive integers
- File saved to
/root/answer.json - Valid JSON (test with
jqorpython -m json.tool)