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Diagnose hard bugs and performance regressions with reproducible evidence, focused hypotheses, and proportional instrumentation.
Passed 10 of 17 test cases with the skill, and 9 of 17 without it.
and 2 more in the full report
5 fixed9 passed either way6 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are establishing standard operating procedures for debugging complex state synchronization errors in our React Native application. Which reference document in the workspace should be loaded for detailed feedback-loop options and debugging tactics?
| Checked | Without skill | With skill |
|---|---|---|
| Directs loading references/debugging-playbook.md for debugging tactics. | Missed | Passed |
The instructions your model reads when the skill loads.
Start from evidence and adapt the depth of the investigation to the problem. Read relevant repository context and architectural decisions when they exist.
Reproduce the reported symptom with the cheapest signal that distinguishes broken from fixed. A focused test or script is ideal when practical; logs, traces, snapshots, comparisons, or measured timings may be better for other failures. Tighten the loop by improving speed, specificity, and determinism.
If an exact reproduction is unavailable, continue with the strongest evidence available and state the limitation. Request an artifact, access, or temporary instrumentation only when it would materially improve the diagnosis.
For feedback-loop options and debugging tactics, load the
debugging playbook as needed. For a rare
manual reproduction, adapt
scripts/hitl-loop.template.sh.
Minimize the reproducing scenario when doing so will shrink the search space. Form a small ranked set of falsifiable hypotheses from the evidence, then choose probes that best distinguish them. Share hypotheses with the user when their domain knowledge could redirect the investigation; do not make routine progress depend on a checkpoint.
Use targeted instrumentation at boundaries that separate plausible causes. Change as little as practical per probe. For performance regressions, establish a baseline and use profiling, query plans, or bisection before optimizing.
Add a regression test when a stable seam can represent the real failure. If it cannot, explain the coverage gap rather than adding a misleading test. Apply the smallest justified fix, then rerun both the focused signal and relevant nearby checks.
test cases passed, vs 9 / 17 without
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