skill
Diagnosing Bugs
نبذة
# Diagnosing Bugs
A discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, read `CONTEXT.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 `<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 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 ph
التثبيت
شغل هذا الأمر
npx skills add mattpocock/skillsيعمل مع
خطوات التثبيت
Install with `npx skills add mattpocock/skills`, or clone the repository and copy the `skills/engineering/diagnosing-bugs` folder into your Claude skills directory.
أسئلة شائعة
كيف أثبت Diagnosing Bugs؟
شغل هذا الأمر في الطرفية:
npx skills add mattpocock/skillsمع أي أدوات ذكاء اصطناعي تعمل Diagnosing Bugs؟
تعمل مع claude_app، claude_code، claude_api، cursor، codex، windsurf، cline، zed.
من طور Diagnosing Bugs؟
طورها mattpocock.
هل Diagnosing Bugs مجانية؟
نعم، يمكنك استخدامها مجانا.
git clone https://github.com/anthropics/skills && cp -r skills/skills/claude-api ~/.claude/skills/
npx skills add mattpocock/skills
npx skills add mattpocock/skills
npx skills add mattpocock/skills
npx skills add microsoft/azure-skills
npx skills add microsoft/azure-skills
افحص قبل التثبيت
شغل أي مصدر عبر فحوصاتنا - الظهور في الذكاء الاصطناعي والأمان والأداء واكتشاف التقنيات.
فحص أمني تلقائي للموقع
الأمان
محلل سرعة الصفحة
الأداء
اختبار جودة المحتوى العربي بالذكاء الاصطناعي
جودة المحتوى
مختبر وكلاء الذكاء الاصطناعي
اختبار الذكاء الاصطناعي
كاشف منصة الموقع
الترحيل
تدقيق الظهور في محركات الذكاء الاصطناعي
الظهور في الذكاء الاصطناعي
مولد ملف llms.txt
الظهور في الذكاء الاصطناعي
مقياس سهولة القراءة بالعربية
جودة المحتوى
منشئ البيانات المنظمة
الظهور في الذكاء الاصطناعي
حاسبة تكاليف الذكاء الاصطناعي
اختبار الذكاء الاصطناعي
محلل العناوين العربية
جودة المحتوى