diagnose

name	diagnose
description	Diagnosis loop for hard bugs and performance regressions — feedback loop first, hypotheses second, fix behind a regression test. Use when the user says "diagnose"/"debug this", or reports something broken, throwing, failing, or slow.
argument-hint	Describe the bug or paste the failing symptom

Diagnose

A discipline for hard bugs. Skip phases only when explicitly justified. The through-line: state the root cause before resolving anything — never circumvent or bypass an error to make a symptom disappear. A fix without a named cause is a bypass wearing a fix's clothes.

Before exploring, check the injected meme recall and run ~/.claude/skills/meme/bin/meme search "<module or symptom>" — a prior gotcha about the failing area often is the diagnosis.

Phase 1 — Build a feedback loop

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.

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

Ways to construct one — try them 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. Headless browser script (Playwright MCP) — drives the UI, asserts on DOM/console/network.
5. Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
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. Write it in the session scratchpad, never the repo.
7. Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
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.
9. Differential loop. Run the same input through the working path and the broken path (old vs new version, two configs, a sibling feature that works) and diff outputs. Reading the working implementation next to the broken one is the cheapest differential of all.
10. Human-in-the-loop script. Last resort. If a human must click, write a script that tells them exactly what to do, captures the observable output (log slice, screenshot, HTTP dump) to a file, and hands it back to you — so even the manual loop is structured and repeatable.

Build the right feedback loop, and the bug is 90% fixed.

Tighten the loop

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

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

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

Non-deterministic bugs

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 — keep raising the rate until it's debuggable.

When you genuinely cannot build a loop

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

Completion criterion — a tight loop that goes red

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 (paste the invocation and its output), and that is:

• Red-capable — it drives the actual bug code path and asserts the user's exact symptom, so it can go red on this bug and green once fixed. Not "runs without erroring" — it must be able to catch this specific bug.
• Deterministic — same verdict every run (flaky bugs: a pinned, high reproduction rate, per above).
• Fast — seconds, not minutes.
• Agent-runnable — you can run it unattended; a human in the loop only via a structured capture script (ladder rung 10).

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.

Phase 2 — Reproduce + minimise

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

Confirm:

• The loop produces the failure mode the user described — not a different failure that happens to be nearby. Wrong bug = wrong fix.
• The failure is reproducible across multiple runs (or, for non-deterministic bugs, at a high enough rate to debug against).
• You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.

Minimise

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 — keep only what's load-bearing for the failure.

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.

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

Do not proceed until you have reproduced and minimised.

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 is the cause, then will make the bug disappear / 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"), 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.

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. Untagged logs survive; tagged logs die.

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.

Phase 5 — Fix + regression test

State the root cause first — the confirmed hypothesis, in one sentence, before any code changes. If you cannot state it, you are not in Phase 5 yet.

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 (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.

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 Phase 6.

If a correct seam exists:

1. Turn the minimised repro into a failing test at that seam, following the project's test conventions.
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 + capture

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 harnesses deleted from the repo (scratchpad files can stay — they die with the session)
• The confirmed root-cause hypothesis is stated in the proposed commit message — so the next debugger learns

Then ask: what would have prevented this bug? Route each answer to its owner:

• A trap the next session would hit again (surprising config, misleading API, hidden coupling) → draft it with /meme:draft as a gotcha; the confirmed root cause of a subtle bug is usually draft-worthy on its own.
• Hours sunk on a wrong-path hypothesis, lost work, or a production incident → offer /postmortem.
• An architectural cause (no good test seam, tangled callers, a shallow module the deletion test would flag) → recommend the deepening scan in /codebase-drag-audit, naming the specific modules. Make this recommendation after the fix is in, not before — you have more information now than when you started.

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