Diagnosing Bugs

Diagnosing Bugs

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

When exploring the codebase, check for an ADR.md at the repo root (or docs/adr/); when present, read the decisions in the area you're touching. Skim the narrowest relevant style-guide/ file – so your mental model matches the intended design, not just the current code.

Redact evidence

Before showing commands, outputs, or captured artifacts, replace secrets with <REDACTED>. Read credentials from environment variables without printing their values. Quote only the lines needed to diagnose the failure, excluding authentication headers from captured requests. For human-in-the-loop scripts, capture observations; leave signing in to the user as a step rather than capturing credentials. If the redacted evidence is insufficient, say what information is missing.

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.

The concrete loops for this stack

Reach for these first, in roughly this order:

1. Failing vitest unit test – the sharpest seam for logic bugs. Run one file with pnpm exec ng test --watch=false --include=<path> (pnpm test --watch=false runs the whole suite). See ng-testing for the how.
2. Failing Playwright e2e spec – for bugs that only show in a real browser. Check for a playwright.config.ts first: if it exists, pnpm test:e2e is this loop; if not (the workshop's teaching branches gain Playwright later), scaffold via create-e2e-tests if you need it.
3. curl against the already-running dev server at http://localhost:4200. Per AGENTS.md, never start ng serve yourself – use the server the user has running, and only ask for approval if nothing answers on the port.
4. git bisect run when the bug appeared between two known-good/bad commits – wrap any of the above loops as the bisect predicate.

The full catalogue of loop shapes (headless harness, trace replay, property/fuzz, differential, HITL) lives in references/feedback-loops.md. Read it when the four above don't reach the bug.

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? (Narrow the test scope, skip unrelated init, cache setup.)
• 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 / flaky 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, screen recording with timestamps), 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, a curl, a script path – 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 goes 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 – 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 flaky bugs, 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 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 – a cheap checkpoint, big time saver. If you want to be pushed on the ranking, run the grill-me skill. 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, DevTools trace), 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. Follow the red-green discipline in test-driven-development: use ng-testing for a unit seam, create-e2e-tests for a browser seam.

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 single-caller test when the bug needs multiple callers, a unit test that can't replicate the triggering chain), 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 it for Phase 6.

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 – keep it local per AGENTS.md; no opportunistic refactors.
4. Watch it pass.
5. Re-run the Phase 1 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/harnesses deleted (or moved to a clearly-marked debug location)
• Lint and the relevant tests pass (AGENTS.md)

Then surface the diff and the root cause to the user – they commit, never you (repo hard rule: no staging or committing on the agent's own initiative). State the hypothesis that turned out correct so the next debugger learns; if you're handing the thread to a fresh session, use the handover skill.

Finally, ask: what would have prevented this bug? If the answer is architectural (no good test seam, tangled callers, hidden coupling), hand off to ng-review-architecture with the specifics. Make that recommendation after the fix is in, not before – you have more information now than when you started.