/diagnosing-bugs

name: diagnosing-bugs
description: "Diagnosis loop for hard bugs and performance regressions. Builds a red-capable feedback loop and runs it before hypothesising — complements static code review (which finds bugs by reading) by running actual repros. Use when the user says 'diagnose'/'debug this', or reports something broken/throwing/failing/slow."

Diagnosing Bugs

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

When exploring the codebase, consult `.claude-code-hermit/compiled/` (hermit knowledge base — architecture decisions and codebase health assessments) to build a mental model of relevant modules before reading source. Check any ADRs in the area you're touching.

Diagnostic byproducts (failing-test output, stack traces, repro snapshots, instrumentation logs) go in `.claude-code-hermit/raw/` — ephemeral inputs under analysis, not `.claude-code-hermit/compiled/`. Reusable lessons from the diagnosis go to auto-memory.

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 / Puppeteer) — drives the UI, asserts on DOM/console/network. 5. **Replay a captured trace.** Save a real network request / payload / event log to disk (in `.claude-code-hermit/raw/`); 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. 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 old-version vs new-version (or two configs) and diff outputs. 10. **HITL bash script.** Last resort. If a human must click, write a script that documents the exact steps and captures output so the loop is still structured.

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 tight — 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.

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, reproducible 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 o