{"slug":"diagnosing-bugs-3","title":"diagnosing-bugs","summary":"Diagnosis loop for hard bugs and performance regressions. Use when the user says \"diagnose\"/\"debug this\", or reports something broken/throwing/failing/slow.","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-10-05T21:53:05.70526Z","repo":{"url":"https://github.com/VoDaiLocz/kilo-kit-mcp","stars":27,"forks":3,"license":"Apache-2.0","updatedAt":"2026-09-13T09:11:19Z"},"bodyHtml":"
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name: diagnosing-bugs\ndescription: Diagnosis loop for hard bugs and performance regressions. Use when the user says \"diagnose\"/\"debug this\", or reports something broken/throwing/failing/slow.

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Diagnosing Bugs

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A discipline for hard bugs. Skip phases only when explicitly justified.

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When exploring the codebase, read CONTEXT.md (if it exists) to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.

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Redact

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This skill has you show commands, outputs and captured artifacts. Redact every secret first: write <REDACTED> in its place. Build loops against env vars, so the credential stays in the environment rather than in what you show. Captured artifacts carry auth headers: quote only the lines that carry the signal.

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If the redacted output is not enough to diagnose the bug, say so and ask the user.

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Phase 1: Build a feedback loop

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This is the skill. Everything else is mechanical. If you have a tight pass/fail signal for the bug (one that goes red on this bug), you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.

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Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.

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Ways to construct one, in roughly this order

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  1. Failing test at whatever seam reaches the bug: unit, integration, e2e.
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  2. Curl / HTTP script against a running dev server.
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  3. CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
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  4. Headless browser script (Playwright / Puppeteer) that drives the UI and asserts on DOM/console/network.
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  5. Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
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  6. Throwaway harness. Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
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  7. Property / fuzz loop. If the bug is \"sometimes wrong output\", run 1000 random inputs and look for the failure mode.
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  8. Bisection harness. If the bug appeared between two known states (commit, dataset, version), automate \"boot at state X, check, repeat\" so you can git bisect run it.
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  9. Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
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  10. HITL bash script. Last resort. If a human must click, drive them with scripts/hitl-loop.template.sh so the loop is still structured. Captured output feeds back to you.
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Build the right feedback loop, and the bug is 90% fixed.

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Tighten the loop

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Treat the loop as a product. Once you have a loop, tighten it:

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A 30-second flaky loop is barely better than no loop; a 2-second deterministic one is tight, a debugging superpower.

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Non-deterministic bugs

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The goal is not a clean repro but a higher reproduction rate. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not, so keep raising the rate until it's debuggable.

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When you genuinely cannot build a loop

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Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a redacted captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do not proceed to hypothesise without a loop.

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Completion criterion: a tight loop that goes red

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Phase 1 is done when the loop is tight and red-capable: you can name one command (a script path, a test invocation, a curl) that you have already run at least once (show the invocation and its output, redacted), and that is:

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If you catch yourself reading code to build a theory before this command exists, stop: jumping straight to a hypothesis is the exact failure this skill prevents. No red-capable command, no Phase 2.

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Phase 2: Reproduce + minimise

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Run the loop. Watch it go red as the bug appears.

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Confirm:

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Minimise

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Once it's red, shrink the repro to the smallest scenario that still goes red. Cut inputs, callers, config, data, and steps one at a time, re-running the loop after each cut, and keep only what's load-bearing for the failure.

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Why bother: a minimal repro shrinks the hypothesis space in Phase 3 (fewer moving parts left to suspect) and becomes the clean regression test in Phase 5.

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Done when every remaining element is load-bearing: removing any one of them makes the loop go green.

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Do not proceed until you have reproduced and minimised.

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Phase 3: Hypothesise

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Generate 3–5 ranked hypotheses before testing any of them. Single-hypothesis generation anchors on the first plausible idea.

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Each hypothesis must be falsifiable: state the prediction it makes.

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Format: \"If

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If you cannot state the prediction, the hypothesis is a vibe: discard or sharpen it.

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Show the ranked list to the user before testing. They often have domain knowledge that re-ranks instantly (\"we just deployed a change to #3\"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it; proceed with your ranking if the user is AFK.

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Phase 4: Instrument

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Each probe must map to a specific prediction from Phase 3. Change one variable at a time.

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Tool preference:

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  1. Debugger / REPL inspection if the env supports it. One breakpoint beats ten logs.
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  2. Targeted logs at the boundaries that distinguish hypotheses.
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  3. Never \"log everything and grep\".
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Tag every debug log with a unique prefix, e.g. [DEBUG-a4f2]. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.

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Perf branch. For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, performance.now(), profiler, query plan), then bisect. Measure first, fix second.

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Phase 5: Fix + regression test

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Write the regression test before the fix, but only if there is a correct seam for it.

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A correct seam is one where the test exercises the real bug pattern as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.

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If no correct seam exists, that itself is the finding. Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.

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If a correct seam exists:

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  1. Turn the minimised repro into a failing test at that seam.
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  2. Watch it fail.
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  3. Apply the fix.
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  4. Watch it pass.
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  5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.
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Phase 6: Cleanup

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Required before declaring done:

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