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debugging-methodology
Structured debugging workflow — hypothesis-driven, evidence-based, reproducible. Produces auditable findings, not just a fix.
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Structured debugging workflow — hypothesis-driven, evidence-based, reproducible. Produces auditable findings, not just a fix.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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| name | debugging-methodology |
| version | 1.0.0 |
| description | Structured debugging workflow — hypothesis-driven, evidence-based, reproducible. Produces auditable findings, not just a fix. |
| tags | ["debugging","troubleshooting","methodology","engineering","incident"] |
| tool_agnostic | true |
A structured, hypothesis-driven approach to debugging. The goal is not just to fix the immediate issue — it is to understand what actually happened and why, so the fix is correct and the knowledge is retained.
Never guess twice. Form a hypothesis, test it with the minimum action needed to confirm or deny it, then update your understanding. A bug fixed by trial and error is a bug that will return.
State the symptom precisely. What is the observed behavior? What is the expected behavior? Where and when does it occur?
Make it reproducible. If you cannot reproduce the bug reliably, you cannot confirm you have fixed it. Invest in a minimal reproduction case.
Gather evidence before touching anything. Logs, stack traces, error messages, network traffic, database state, environment configuration. Capture the state before any changes.
Define the blast radius. What is affected? What is confirmed not affected? This scopes the investigation.
List 2-3 candidate hypotheses. Each should be a falsifiable claim about the cause. "The database is slow" is not a hypothesis. "The N+1 query in OrderService.getItems() is causing timeouts under load" is.
Order by likelihood and testability. Test the most likely and easiest-to-test hypothesis first.
State what evidence would confirm or deny each hypothesis. Know what you are looking for before you look.
Change one thing at a time. Multiple simultaneous changes make it impossible to know what fixed the problem.
Test the hypothesis, not the fix. Confirm the cause before applying the solution.
Document what you tried and what it showed. If you hand off the investigation or need to revisit it, this log is essential.
Apply the fix once the cause is confirmed. The fix should follow directly from the confirmed hypothesis.
Verify the fix against the original reproduction case. Confirm the symptom is gone.
Check for regressions. Run the relevant test suite. Check adjacent behavior.
Confirm the fix under conditions close to production. A fix that works locally but not under load or in a specific environment is not a fix.
Write a brief root cause summary. What was the actual cause? What masked it? Why did it occur?
Note any systemic issues the bug exposed. Missing test coverage, unclear ownership, configuration drift, absent monitoring.
State what would catch this class of bug earlier. A test, a log line, an alert, a linting rule.
For anything non-trivial, produce a brief debugging record: