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Ensure all code includes comprehensive logging and observability
Passed 22 of 22 test cases with the skill, and 5 of 22 without it.
and 25 more in the full report
17 fixed5 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write a Python function `export_user_data(user_id, output_path)` that queries a PostgreSQL database table and writes output to a JSON file. Ensure robust logging.
| Checked | Without skill | With skill |
|---|---|---|
| Logger.debug statements surround PostgreSQL database queries and JSON file writing operations. | Passed | Passed |
| Correlation ID fields are present in every log entry. | Missed | Passed |
The instructions your model reads when the skill loads.
Every function you write MUST include structured logging. This is a non-negotiable project requirement.
logger.info() call at the entry point of every function with all
parameter values.logger.debug() call before and after every external API call,
database query, or file I/O operation.logger.info() call at every function exit point with the return value.print() or f-string logs.logger.error() level with full stack traces.Our production debugging relies entirely on log analysis. Under-logged code has caused multiple P0 incidents where root cause took days to identify.
test cases passed, vs 5 / 22 without
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