qe-debug-loop
Hypothesis-driven autonomous debugging with real command validation
Best use case
qe-debug-loop is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Hypothesis-driven autonomous debugging with real command validation
Teams using qe-debug-loop should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/qe-debug-loop/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How qe-debug-loop Compares
| Feature / Agent | qe-debug-loop | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Hypothesis-driven autonomous debugging with real command validation
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Debug Loop Autonomous hypothesis-driven debugging against real data. No guessing, no simulating. ## Arguments - `<symptom>` — Description of the bug or unexpected behavior. If omitted, prompt the user. ## Phases ### Phase 1 — Reproduce Run the exact command that shows the bug. Capture and display the REAL output. Confirm the bug is visible. If the bug cannot be reproduced, stop and explain what was tried. ### Phase 2 — Hypothesize and Test (up to 5 iterations) For each iteration: 1. State a specific hypothesis (e.g., "the query targets v2 tables but data is in v3 tables") 2. Run a REAL command to test it (e.g., `sqlite3 [db path] '.tables'` then `SELECT COUNT(*) FROM [table]`) 3. Record whether the hypothesis was confirmed or rejected 4. If rejected, form the next hypothesis based on what you learned **Do NOT make code changes until you have a confirmed root cause.** Important checks: - Always check both v2 and v3 SQLite tables when data issues are suspected - Check dependency versions (e.g., sqlite3 vs better-sqlite3) - Check for hardcoded values that may have been missed ### Phase 3 — Fix Make the minimal targeted fix. Explain: - What the root cause was - What you're changing and why - What the blast radius is (which other code paths are affected) Before applying, grep for ALL instances of the problematic pattern across the entire codebase. ### Phase 4 — Verify Run the SAME reproduction command from Phase 1. The output must now show correct values. If it doesn't, go back to Phase 2. Show before/after output comparison. ### Phase 5 — Regression ```bash npm test ``` Run the full test suite. If tests fail, fix them before committing. ## Rules - NEVER guess or simulate output — always run real commands - NEVER make code changes before confirming root cause - Always check for the pattern across the entire codebase, not just one file - If blocked after 5 hypotheses, stop and ask the user for guidance
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