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Systematic approach to diagnosing and fixing errors
Passed 13 of 23 test cases with the skill, and 13 of 23 without it.
2 fixed11 passed either way8 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to add temporary debug statements to my Python CLI tool to inspect variable state during execution. I usually just write `print(var)` statements. What is the standard debug logging format to avoid polluting standard output?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends formatting debug output as f"DEBUG: {var=}". | Passed | Passed |
| Directs sending Python debug print statements to sys.stderr. | Passed | Passed |
| Advises removing debug prints after fixing the bug. | Missed | Passed |
The instructions your model reads when the skill loads.
A structured approach to finding and fixing bugs.
1. REPRODUCE → 2. ISOLATE → 3. DIAGNOSE → 4. FIX → 5. VERIFY
Never skip steps. Never guess-and-check repeatedly.
| Error type | Where to look |
|---|---|
| Compile error | The FIRST error (later ones are often cascading) |
| Segfault | Last function in the stack trace, check array bounds and null pointers |
| Python traceback | The innermost frame (bottom), but also check the middle for context |
| Wrong output | Diff expected vs actual: diff <(expected) <(actual) |
| Timeout/hang | Is it an infinite loop? Deadlock? I/O bound? Add a timer or counter |
test cases passed, vs 13 / 23 without
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