Diagnosing Bugs

--- name: diagnosing-bugs description: Diagnosis loop for hard bugs and performance regressions. 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, read `GLOSSARY.md` (if it exists) to get a clear mental model of the relevant modules, and check ADRs in the area you're touching. ## Redact This skill has you show commands, outputs and captured artifacts. **Redact every secret first**: write `` in its place. Build loops against env vars, so the credential stays in the environment rather than in what you show. Captured artifacts carry auth headers: quote only the lines that carry the signal. If the redacted output is not enough to diagnose the bug, say so and ask the user. ## 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, 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) that drives the UI and 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 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, drive _them_ with `scripts/hitl-loop.template.sh` so the loop is still structured. Captured output feeds back to you. 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, so 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 redacted 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** (show the invocation and its output, redacted), 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 `scripts/hitl-loop.template.sh`. 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 as 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, and 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 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 the next phase. If a correct seam exists: 1. Turn the minimised repro into a failing test at that seam. 2. Watch it fail. If you forced the red by mutating code or a fixture, `diff` against a pristine copy to prove the mutation landed before you trust it. 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 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