Json data analysis
[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.
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Patterns for analyzing and outputting structured JSON answers from enterprise data queries.
SKILL.md
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JSON Data Analysis and Output
Output Format
When answering questions from enterprise data, use this standard format:
{
"q1": {"answer": ["item1", "item2"], "tokens": 123},
"q2": {"answer": ["item1"], "tokens": 456}
}
Key Rules
- Every answer is a list, even single items
- Token counts are numeric values (not strings)
- Employee IDs are strings like "eid_abc123"
- URLs are full strings including protocol
Python Verification
import json
with open('answer.json') as f:
data = json.load(f)
for k, v in data.items():
assert isinstance(v['answer'], list)
assert isinstance(v['tokens'], (int, float))