diagnose

name	diagnose
description	Use when debugging hard bugs or perf regressions. Disciplined loop — reproduce, minimise, hypothesise, instrument, fix, regression-test. Triggers: debug this, broken, throwing, failing.
metadata	{"upstream":"mattpocock/skills/engineering/diagnose","upstream-sha":"49cec7be019bc408e87b77670f3d442f536da254","adapted-date":"2026-04-28"}

Diagnose

Discipline for hard bugs. Skip phases only when explicitly justified.

Domain context runs automatically when the skill loads — output replaces each line below:

• CONTEXT.md: !cat CONTEXT.md 2>/dev/null || true
• ADR list: !ls docs/adr/ 2>/dev/null || true

If CONTEXT.md content is present above, use that vocabulary. If ADR list showed files, read the relevant ones before exploring the codebase. If both were empty, proceed silently.

Phase 1 — Build a feedback loop

This is the skill. Everything else is mechanical. Fast, deterministic, agent-runnable pass/fail signal for the bug → you will find the cause. Without one, no amount of staring at code will save you.

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

Ways to construct one — try roughly in this order

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

Build the right feedback loop → bug is 90% fixed.

Iterate on the loop itself

Treat loop as product. Once you have a loop, ask:

• Can I make it faster? (Cache setup, skip unrelated init, narrow test scope.)
• Can I make signal sharper? (Assert on specific symptom, not "didn't crash".)
• Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)

30-second flaky loop ≈ no loop. 2-second deterministic loop = debugging superpower.

Non-deterministic bugs

Goal is not clean repro but higher reproduction rate. Loop trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. 50%-flake bug is debuggable; 1% is not — keep raising rate until debuggable.

When you genuinely cannot build a loop

Stop and say so explicitly. List what you tried. Ask user for: (a) access to whatever environment reproduces it, (b) 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.

Do not proceed to Phase 2 until you have a loop you believe in.

Phase 2 — Reproduce

Run the loop. Watch bug appear.

Confirm:

• Loop produces failure mode user described — not a different failure nearby. Wrong bug = wrong fix.
• Failure reproducible across multiple runs (or, for non-deterministic bugs, at high enough rate to debug against).
• Exact symptom captured (error message, wrong output, slow timing) so later phases can verify fix addresses it.

Do not proceed until you reproduce the bug.

Phase 3 — Hypothesise

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

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

Format: "If is the cause, then will make bug disappear / will make it worse."

Can't state prediction → hypothesis is a vibe — discard or sharpen.

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

Phase 4 — Instrument

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

Tool preference:

1. Debugger / REPL inspection if env supports it. One breakpoint beats ten logs.
2. Targeted logs at boundaries that distinguish hypotheses.
3. Never "log everything and grep".

Tag every debug log with unique prefix, e.g. [DEBUG-a4f2]. Cleanup at end becomes single grep. Untagged logs survive; tagged logs die.

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

Phase 5 — Fix + regression test

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

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

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

If correct seam exists:

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

Phase 6 — Cleanup + post-mortem

Required before declaring done:

• Original repro no longer reproduces (re-run Phase 1 loop)
• Regression test passes (or absence of seam documented)
• All [DEBUG-...] instrumentation removed (grep the prefix)
• Throwaway prototypes deleted (or moved to clearly-marked debug location)
• Hypothesis that turned out correct stated in commit / PR message — so next debugger learns

Then ask: what would have prevented this bug? If answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the codebase-improve skill with specifics. Make recommendation after fix is in, not before — you have more information now than when you started.