Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
GitHub Copilot
Gemini CLI+17 moreClaude CodeCursorCodex CLIGitHub CopilotGemini CLI
VS Code
Windsurf
Manus+14 moreSystematic Debugging: 4-Phase Root Cause Methodology
Stop guessing at bugs. A 4-phase method that makes you find root cause before touching the code. Takes debugging from hours to minutes. Works in any language, with any AI agent.
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See it in action
You say
I've been trying to fix this bug for an hour. The API returns 500 errors when I pass a user ID over 10000, but only in production. I've tried adding more logging but can't find the pattern. Load the debugging methodology and help me find root cause.
Your agent does
Following Systematic Debugging methodology.
Phase 1 — Investigation
- Reproduced: GET /api/users/10001 returns 500 in production, 200 in staging
- No error in application logs. 500 coming from upstream.
- Checked nginx access logs — upstream timed out after 30s
- Database query for user 10001 takes 45s, other IDs take <100ms
- EXPLAIN ANALYZE on the user query: full table scan, missing index on partition for high user IDs
Phase 2 — Pattern
- Database has 4 partitions. Partition for IDs 5000-10000 has an index. Partition for 10000+ does not.
- Likely cause: migration script created the partition without copying the index definition. Not reproduced in staging because staging uses a single unpartitioned table.
Phase 3 — Hypothesis
- Missing index on the 10000+ partition is the root cause. Not a code bug.
Phase 4 — Implementation
- Created migration to add the missing index
- Wrote regression test that queries user 15001 and asserts <200ms response time
- Applied to production during low traffic. 500s resolved. Query now 85ms.
- Re-examined migration scripts to prevent recurrence.
The skill produces structured investigation output, not code. It makes the agent show its work at each phase before touching anything.
What you get
About this skill
You're 2 hours into a bug. Tried 4 fixes. Each one "almost worked" but broke something else. Still guessing.
Most developers skip straight to trying things. Most AI agents do the same. No method means trial and error. Patch one thing, break two others.
This skill fixes that. 4 phases. Your agent loads it and follows it like a playbook. No fixing until you've found root cause.
Phase 1, find what's happening. Error messages. Reproduce it. Audit what changed. Gather evidence. Check logs. Trace the data. There are specific commands for each.
Phase 2, compare broken code against working code. Find every difference. Know what depends on what before touching anything.
Phase 3, one hypothesis. One minimal test. Verify before moving to the next thing. No bundle fixes.
Phase 4, write a failing regression test first. Apply the fix. Verify. If it still fails after three attempts, the architecture is wrong. Stop patching.
584 lines. 6 reference files covering Python debugging, Node debugging, stale bytecode detection, and pipeline filter tracing. Plus remote troubleshooting, multi-component diagnostics, and a red flags list that catches every rationalisation agents use to skip steps. "Quick fix for now." "Issue is simple." "Just try changing X." Your agent reads these and catches itself.
How to use it
Drop the zip in your agent's skills directory. Reference it in config. When your agent hits a bug, tell it to follow the skill.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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