Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
php artisan boost:add-skill shipwithian/shipkit-coreSave this content to: .claude/skills/diagnosing-bugs/SKILL.md
# Diagnosing Bugs
A discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, read the context rule file in `.ai/rules/contexts/` for the area you're touching to get its vocabulary and invariants, and check `docs/adr/` if it exists.
## Redact
This skill has you show commands, outputs and captured artifacts. **Redact every secret first**: write `<REDACTED>` 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 Pest test** at whatever seam reaches the bug: a feature test through the route (`$this->actingAs(...)->post(route(...))`), or the Action or model directly. Run it with `php artisan test --compact --filter='<name>'`.
2. **Curl / HTTP script** against a running dev server (`composer run dev`; get the base URL from the Boost `get-absolute-url` tool).
3. **CLI invocation** with a fixture input, diffing stdout against a known-good snapshot.
4. **Browser** via Claude in Chrome against the dev server, reading console errors with the Boost `browser-logs` tool.
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 <X> is the cause, then <changing Y> will make the bug disappear / <changing 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 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. **REPL inspection** with `php artisan tinker --execute '...'`, plus the Boost tools: `last-error` for the latest exception, `read-log-entries` for the log, `database-query` for read-only state. One targeted look beats ten logs.
2. **Targeted logs** (`logger()->debug(...)`) 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, `DB::listen` query counts, `EXPLAIN` via `database-query`, profiler), 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 learnsA discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, read the context rule file in .ai/rules/contexts/ for the area you're touching to get its vocabulary and invariants, and check docs/adr/ if it exists.
This skill has you show commands, outputs and captured artifacts. Redact every secret first: write <REDACTED> 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.
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.
$this->actingAs(...)->post(route(...))), or the Action or model directly. Run it with php artisan test --compact --filter='<name>'.composer run dev; get the base URL from the Boost get-absolute-url tool).browser-logs tool.git bisect run it.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.
Treat the loop as a product. Once you have a loop, tighten it:
A 30-second flaky loop is barely better than no loop; a 2-second deterministic one is tight, a debugging superpower.
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.
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.
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:
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.
Run the loop. Watch it go red as the bug appears.
Confirm:
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.
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.
Each probe must map to a specific prediction from Phase 3. Change one variable at a time.
Tool preference:
php artisan tinker --execute '...', plus the Boost tools: last-error for the latest exception, read-log-entries for the log, database-query for read-only state. One targeted look beats ten logs.logger()->debug(...)) at the boundaries that distinguish hypotheses.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, DB::listen query counts, EXPLAIN via database-query, profiler), then bisect. Measure first, fix second.
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:
diff against a pristine copy to prove the mutation landed before you trust it.Required before declaring done:
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