Diagnose

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when something is broken, throwing errors, failing tests, or performing poorly.

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

When exploring the codebase, use the project's domain glossary to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.

Phase 1 — Build a Feedback Loop

This is the skill. Everything else is mechanical. If you have a fast, deterministic, agent-runnable pass/fail signal for the bug, you will find the cause. If you don't, no amount of staring at code will help.

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

Ways to construct one — try in roughly 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 a fixture input, diffing stdout against a known-good snapshot.
4. Browser snapshot via Playwright / Puppeteer — drives the UI, asserts on DOM/console/network.
5. Replay a captured trace. Save a real request/payload/event log; replay it through the code path in isolation.
6. Throwaway harness. Minimal subset of the system that exercises the bug code path with a single call.
7. Property / fuzz loop. If the bug is "sometimes wrong output," run 1000 random inputs and look for the failure mode.
8. Bisection harness. Automate "boot at state X, check, repeat" so you can git bisect run it.
9. Differential loop. Run same input through old vs new version, diff outputs.

Iterate on the loop itself

Once you have a loop, ask:

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

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

Non-deterministic bugs

The goal is not a clean repro but a higher reproduction rate. Loop 100×, parallelise, add stress, narrow timing windows. A 50%-flake bug is debuggable; 1% is not — keep raising the rate.

When you genuinely cannot build a loop

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

Phase 2 — Reproduce

Run the loop. Watch the bug appear.

Confirm:

• The loop produces the failure mode the user described — not a different nearby failure
• The failure is reproducible across multiple runs (or, for non-deterministic bugs, at a high enough rate)
• You have captured the exact symptom so later phases can verify the fix

Do not proceed until you reproduce the bug.

Phase 3 — Hypothesise

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

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

Format: "If <X> is the cause, then <changing Y> will make the bug disappear / <changing Z> will make it worse."

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

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"). Don't block on it — proceed if the user is AFK.

Phase 4 — Instrument

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

Tool preference:

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

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

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

Phase 5 — Fix + Regression Test

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

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, a regression test there gives false confidence.

If no correct seam exists, that itself is the finding. Flag it — the architecture is preventing the bug from being locked down.

If a correct seam exists:

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

Phase 6 — Cleanup + Post-Mortem

Required before declaring done:

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

Then ask: what would have prevented this bug? If the answer involves architectural change (no good test seam, tangled callers), consider running the /improve-codebase-architecture skill with the specifics.