ai-output-validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and…
Systematic root cause analysis for production and development bugs. Hypothesis-driven debugging — never guess-and-check.
$ npx -y skills add DevelopersGlobal/ai-agent-skills --skill debugging-methodology --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/debugging-methodologyContext preview
The summary Claude sees to decide when to auto-load this skill.
Systematic root cause analysis for production and development bugs. Hypothesis-driven debugging — never guess-and-check.
name: debugging-methodology description: Systematic root cause analysis for production and development bugs. Hypothesis-driven debugging — never guess-and-check. category: test applies-to: [claude, gemini, cursor, copilot, any] version: 1.0.0
Random code changes in response to errors are not debugging — they're noise generation. This skill enforces a systematic, hypothesis-driven approach: understand the problem, form a hypothesis, test it, confirm the root cause, then fix.
AI agents often cycle through random fixes until something "works." This skill prevents that.
1. Before doing anything else: **reproduce the bug reliably**. If you can't reproduce it, you can't fix it. 2. Write a failing test that captures the bug — this becomes your regression test. 3. Note the exact conditions that trigger the bug: inputs, environment, sequence of actions.
**Verify:** You can trigger the bug on demand.
4. Read the full error message — not just the first line. 5. Read the stack trace from bottom to top — the root cause is usually near the bottom. 6. Identify: *What was the program trying to do? What happened instead?*
**Verify:** You can explain the bug in one sentence without using the word "error."
7. Based on what you know, form a specific hypothesis: *"I think the bug is X because Y."* 8. The hypothesis must be **falsifiable** — you can design a test that proves or disproves it. 9. Do not start making code changes until you have a hypothesis.
**Verify:** Your hypothesis is specific enough to design a test for.
10. Add targeted logging or a targeted test that confirms or refutes the hypothesis. 11. Run it. Read the output carefully. 12. If the hypothesis is **wrong**: update your understanding, form a new hypothesis, repeat. 13. If the hypothesis is **right**: you've found the root cause.
**Verify:** Root cause is confirmed by evidence, not assumed.
14. Fix the root cause — not the symptom. Suppressing an error message is not a fix. 15. Make the minimum change that fixes the root cause. 16. Run the failing test you wrote in Step 1 — it should now pass. 17. Run the full test suite — no regressions.
**Verify:** The specific failing test now passes. Full suite still passes.
18. If the bug wasn't caught by existing tests: add a test that would have caught it. 19. If the bug was caused by a bad assumption: document the assumption or add a guard. 20. Consider: does this class of bug exist elsewhere in the codebase?
| Excuse | Rebuttal | |--------|----------| | "Let me just try this and see" | Random changes produce random results. Form a hypothesis first. | | "It must be a framework bug" | It's almost never the framework. Prove it before blaming it. | | "Works on my machine" | Environment differences are root causes. Find them. Don't dismiss them. | | "I'll add a try/catch" | That hides the bug. Find and fix the root cause. |
AI agent skills for production grade applications
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and…
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