| name | diagnosing-bugs |
| description | Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression. |
Diagnosing bugs
A discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, query codemap (the structural SQLite index) before reaching for Grep or Read per the codemap rule — symbol-shaped questions ("where is X defined?", "what calls X?") have direct answers in the symbols / calls tables. Read the relevant section of docs/architecture.md to ground the mental model of layering, and check docs/glossary.md for canonical domain terms (file types, recipe ids, schema columns).
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
- Failing test at whatever seam reaches the bug — unit, integration, e2e. Codemap convention:
src/**/<name>.test.ts for unit + integration; fixtures/golden/ for query-shape regressions; bun test <file> runs them.
- CLI invocation with a fixture input, diffing stdout against a known-good snapshot. Examples:
bun src/index.ts query --json … against fixtures/minimal/, golden runner under scripts/query-golden.ts.
- Replay a captured trace. Save a real
.codemap/index.db / config / fixture file to disk; replay it through the code path in isolation.
- Throwaway harness. Spin up a minimal subset (one parser, one DB connection) that exercises the bug code path with a single function call.
- Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
- 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.
- Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs. The B.6 baseline machinery (
codemap query --save-baseline / --baseline) is built for exactly this — use it.
- HITL bash script. Last resort. If a human must click or copy a value out of the IDE, 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.
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.)
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 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, broken .codemap/index.db), or (c) permission to add temporary 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 test invocation (bun test <file>), bun src/index.ts query --json …, a throwaway harness, or scripts/hitl-loop.template.sh for the human-in-the-loop case — that you have already run at least once (paste the invocation and its output), and that is:
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
Run the loop. Watch the bug appear.
Confirm:
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 <X> is the cause, then <Y> will make the bug disappear / <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 changed #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.
Done when: 3–5 ranked, falsifiable hypotheses stated (each with a prediction); user has seen the list before any fix attempt.
Phase 4 — Instrument
Each probe must map to a specific prediction from Phase 3. Change one variable at a time.
Tool preference:
- Debugger / REPL inspection if the env supports it. One breakpoint beats ten logs.
- Targeted logs at the boundaries that distinguish hypotheses.
- 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, --performance flag for index runs), then bisect. Measure first, fix second.
Done when: one hypothesis confirmed or all falsified with evidence — one variable changed per probe, each probe mapped to a Phase 3 prediction.
Phase 5 — Fix + regression test
Write the regression test before the fix — but only if there is a correct seam for it (per the improve-codebase-architecture vocabulary).
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:
- Turn the minimised repro into a failing test at that seam.
- Watch it fail.
- Apply the fix.
- Watch it pass.
- Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.
Done when: loop is green against the original scenario; regression test added at a correct seam, or the seam-gap is documented as an architectural finding.
Phase 6 — Cleanup + post-mortem
Required before declaring done:
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.
Done when: no [DEBUG-…] sediment (grep clean); throwaway harnesses deleted; commit / PR message states the winning root-cause hypothesis; any durable insight lifted into .agents/rules/ or a skill (else .agents/lessons.md).
