diagnosing-bugs：mattpocock/skills 方法论: engineering/diagnosing-bugs，适用于工程实践、代码质量与开发流程优化。
diagnosing-bugs：mattpocock/skills 方法论: engineering/diagnosing-bugs，适用于工程实践、代码质量与开发流程优化。
> 来源: mattpocock/skills (141k stars) — Total TypeScript 创始人 Matt Pocock
> 类目: 工程方法论
> 原文件: skills/engineering/diagnosing-bugs/SKILL.md
> 模型推荐: gpt-4.1 (代码工程)
Matt Pocock (Total TypeScript, 141k stars) 沉淀下来的"日常代码工程动作"skill 模板。
每一个对应一个具体动作(代码评审 / TDD / 重构 / 文档对齐 / 任务交接),不是工具,是"该怎么干这件事"的工作流模板。
michael 强调"skill 要有相应的指导功能,指导用户使用",所以这里加了下面两节让 Agent 和用户对接。
---
1. 接到任务后先按这个 skill 的检查清单走一遍
2. 清单里如果出现"先做 X 才能做 Y"的红线 → 必须先完成 X
3. 跑完清单后开始动手(改代码 / 写文档 / 评审)
4. 完工前用 `verification-before-completion`(superpowers) 或 `verification` 类 step 自检
5. 任务量大时考虑用 `dispatching-parallel-agents`(superpowers) 或 `wayfinder`(mattpocock) 拆解
1. 告诉 Agent 你要做什么(一句话即可)
2. Agent 跑 skill 清单时不要打断 — 它可能在收集上下文
3. 如果 Agent 主动问你"代码评审要不要安排一个 reviewer"等决策点 → 直接回答
4. 完工后让 Agent 跑一遍自检再交回
5. 整个过程 Agent 自动化,不需要手工介入
---
---
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.
---
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.
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.**
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) — drives the UI, 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.
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 — 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:
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 — 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 — 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 <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.
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.
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.
3. Apply the fix.
4. Watch it pass.
5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.
Required before declaring done:
本 skill 专注于diagnosing-bug，mattpocock/skills 方法论: engineering/diagnosing-bugs。它将相关流程标准化，帮助用户更快拿到可靠结果，减少重复手工操作。
当你需要在diagnosing-bugs相关工作中获得稳定、可复用的产出时最适合——无论是单次任务还是纳入日常工作流，都能直接调用。
需要一个具体的项目或任务上下文，最好带有代码仓库或需求文档。
1. 告诉 Agent 你要做什么(一句话即可)
2. Agent 跑 skill 清单时不要打断 — 它可能在收集上下文
3. 如果 Agent 主动问你"代码评审要不要安排一个 reviewer"等决策点 → 直接回答
4. 完工后让 Agent 跑一遍自检再交回
5. 整个过程 Agent 自动化,不需要手工介入
---
本 skill 专注于diagnosing-bug，mattpocock/skills 方法论: engineering/diagnosing-bugs。它将相关流程标准化，帮助用户更快拿到可靠结果，减少重复手工操作。
当你需要在diagnosing-bugs相关工作中获得稳定、可复用的产出时最适合——无论是单次任务还是纳入日常工作流，都能直接调用。
需要一个具体的项目或任务上下文，最好带有代码仓库或需求文档。