roborew/debug-fix

Debug-fix (diagnose discipline)

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

When exploring the codebase, use the project's domain glossary (CONTEXT.md / CONTEXT-MAP.md) and check docs/adr/ 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 — 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.

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; 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 many 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 loop. Last resort. If a human must click, drive them with a short checklist script so the loop is still structured.

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

Iterate on the loop itself

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

• 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.)

Non-deterministic bugs

The goal is not a clean repro but a higher reproduction rate. Loop the trigger many times, parallelise, add stress, narrow timing windows, inject sleeps. A high-flake bug is debuggable; 1% is not — keep raising the rate until it is.

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, log dump, core dump, 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 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, 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.

Do not proceed until you reproduce the bug.

Phase 3 — Trace (git history)

When the bug is a regression, use git log --oneline -- path, git log -S'symbol' -- path, and blame. Use git bisect only if the user approves the time cost.

Phase 4 — 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 <cause> is the cause, then <probe> 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. Don't block on it — proceed with your ranking if the user is AFK.

Phase 5 — Instrument

Each probe must map to a specific prediction from Phase 4. 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 6 — Root cause + fix + regression test

Root cause — one paragraph; cite files/lines.

Fix — smallest change.

Regression test — write before the fix when 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. Note it. Flag that 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 7 — Verify

Run targeted tests / the Phase 1 loop. Confirm the user's symptom is gone.

Phase 8 — 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 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), recommend a follow-up to the parent / refactor path with specifics. Make the recommendation after the fix is in, not before — you have more information now than when you started.

GitHub issue path (architect front door)

When the parent is architect on a debug/targeted-change path, do not write .plan/debug.*. Instead:

1. Complete diagnosis through Phase 6 (root cause + proposed fix + regression test plan).
2. Return structured slice content to architect for to-issues — one or more GitHub issues with acceptance criteria and test commands.
3. After issues are published and labelled state:ready-for-agent, parent architect emits the execution handoff (feature or queue variant).

Execution happens from the GitHub queue, not a local plan file.