diagnose

name

diagnose

description

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → hypothesise → instrument → fix → regression-test, with Phase 1 (build a feedback loop) load-bearing. Use when the user says "diagnose this", "debug this", "reproduce this bug", "this is throwing/failing/broken in prod", "why is this slow", or describes a performance regression. Distinct from `/triage` (which decides whether a bug has enough information to be tractable at all). Run this AFTER triage has confirmed the issue is real, BEFORE letting the issue advance to `Todo` for runway — the regression test you write seeds the issue's acceptance criteria and the loop you build proves the fix actually closes the bug.

/diagnose

A discipline for hard bugs. Six phases; Phase 1 is the skill — everything else is mechanical once you have a deterministic, agent-runnable pass/fail signal for the bug. Skip phases only when you can articulate why.

This skill is advisory. It writes test files, scripts, harnesses, and fixtures — never tracker labels or status changes. Its outputs feed the next skill in the chain — see § Outputs.

When to run

After /triage has classified the issue as a Bug with enough detail to attempt reproduction, but before the issue advances to Todo for runway. An issue that lands in runway without a concrete reproducer almost always produces a "fix" that passes review without proving anything — the regression test you build here pins the behavior down.

Also valid: standalone debugging during HITL work, where the bug never makes it to runway. The phases still apply.

When NOT to run

• The reporter's body lacks reproduction steps and the code path is unclear → run /triage and post needs-info with specific questions instead.
• The bug is already reproducing in CI — Phase 1 is mostly free; jump to Phase 3.
• The "bug" is actually a missing feature — this is Feature/Improvement work, not diagnosis. Run /grill-with-docs → /to-prd → /to-issues instead.

Pre-flight — read project context

Before Phase 1, read CONTEXT.md for the project's domain glossary, and skim docs/adr/ for any architectural decision in the area you're touching. Use the glossary vocabulary in everything you write — test names, log prefixes, hypothesis text — so downstream readers (the reporter, the human PR reviewer, the Claude Code instance running inside runway) inherit consistent language.

Phase 1 — Build a feedback loop

This is the skill. If you have a fast, deterministic, agent-runnable pass/fail signal for the bug, you will find the cause — bisection, hypothesis-testing, and instrumentation all just consume that signal. 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.

Ten patterns, in roughly this order

Try them in order; stop at the first one that works for this bug. Deep detail on each lives in /feedback-loop — that sibling skill is the reference for constructing loops; this skill is the reference for using them inside a diagnosis.

1. Failing test at whatever seam reaches the bug — unit, integration, e2e.
2. Curl / HTTP script against a running dev server.
3. CLI fixture diff — invoke the CLI with a known input, diff stdout against a recorded snapshot.
4. Headless browser script (Playwright / Puppeteer) — 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 — minimal subset of the system (one service, mocked deps) exercising the bug code path with a single function call.
7. Property / fuzz loop — for "sometimes wrong output" bugs, run 1000 random inputs and look for the failure mode.
8. Bisection harness — git bisect run over a script that boots state X, checks, repeats.
9. Differential loop — same input through old-version vs new-version (or two configs); diff outputs.
10. HITL bash — last resort. Drive the human via scripts/hitl-loop.template.sh so the loop is still structured. Captured output feeds back to the agent.

Iterate on the loop itself

The loop is a product. Once you have a loop, ask:

• Faster? Cache setup, skip unrelated init, narrow scope.
• Sharper? Assert on the specific symptom, not "didn't crash".
• 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 loop 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. Do not proceed to hypothesise. Output:

Cannot construct a feedback loop. Tried: . Need one of: (a) access to the environment that reproduces it, (b) a captured artifact (HAR, log dump, core dump, screen recording with timestamps), (c) permission to add temporary production instrumentation.

Recommend handing back to /triage to apply needs-info with these as the specific blockers.

This is the right answer. Hypothesising without a loop produces plausible fixes that don't prove anything. Take the slower path.

Phase 2 — Reproduce

Run the loop. Watch the bug appear. 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).
• The exact symptom (error message, wrong output, slow timing) is captured so later phases can verify the fix actually addresses it.

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:

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"), 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 away.

Use the CONTEXT.md glossary in hypothesis names so the user reads them in the project's language, not generic terms.

Phase 4 — Instrument

Each probe maps 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 becomes a single grep. Untagged logs survive; tagged logs die.

Perf branch. For performance regressions, logs are usually wrong. Establish a baseline measurement (timing harness, performance.now(), profiler, EXPLAIN ANALYZE for queries), 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 exercises the real bug pattern as it occurs at the call site. If the only available seam is too shallow (a single-caller test when the bug needs multiple callers, a 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 and for /improve-codebase-architecture.

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 like scripts/debug/).
• The hypothesis that turned out correct 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, hidden coupling), hand off to /improve-codebase-architecture with the specifics. Make the recommendation after the fix is in, not before — you have more information now than when you started.

If the bug surfaced an architectural decision worth recording (a non-obvious constraint that future debuggers should know), offer an ADR via /grill-with-docs. Same three-condition rule: hard to reverse, surprising without context, result of a real trade-off.

Outputs that flow downstream

The whole point of doing this before letting an issue go to runway is that the artefacts have downstream consumers. After Phase 6, hand the user a structured summary they can paste into the tracker comment or edit into the issue body itself.

Artefact from this run	Where it goes next
The Phase 5 regression test	Becomes one of the issue body's testable acceptance criteria. Phrase it as "When <scenario>, the system produces <observation>" so the spec round-trips.
The Phase 1 feedback loop (script / test / harness)	Stays in the repo (or scripts/debug/) and serves as a deterministic check Claude Code can run while implementing the fix. Reference it in the issue body so runway picks it up alongside the spec.
The hypothesis that turned out correct	Goes in the tracker comment / PR description as the post-mortem one-liner.
Architectural finding (no good seam, etc.)	Open a separate tracker issue tagged Improvement and route through /to-issues → /triage. Do not bundle architectural work into the bug-fix issue.
New domain term encountered	Update CONTEXT.md right there — same discipline as /grill-with-docs. Lazy-create the file if it doesn't exist.

After this skill finishes, the next step is /triage to advance the issue to Todo (or needs-human if the diagnosis surfaced something that runway shouldn't handle).

AI disclaimer

When this skill posts a comment to the active tracker (e.g. summarising the diagnosis for a Bug issue before handing back to triage), the comment must start with this disclaimer on its own line, before any other content:

> *This was generated by AI during diagnosis.*

When the skill writes only to local files (the regression test, the feedback loop script, debug logs), no disclaimer is needed — those carry their own provenance via git history.

Non-goals

• No tracker label transitions. Status / label moves on the active tracker are /triage's job. This skill may recommend a transition (e.g. "looks ready for Todo") but applies nothing itself.
• No production instrumentation without explicit user approval. Phase 4 logs are local; production probes need the user to say yes.
• No architectural refactoring inline. Phase 5 may note that the architecture blocks a regression test, but the actual restructuring is a separate skill invocation, not an extension of this one.

References

• ../triage/SKILL.md — upstream (decides whether a bug is even tractable).
• ../feedback-loop/SKILL.md — sibling reference on constructing loops; pattern detail for the ten approaches in Phase 1.
• Matt Pocock's diagnose — upstream inspiration for the six-phase loop. The phases here are the same; the tracker integration is what's added.