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debugging

Structured root-cause investigation for bugs and failures.

Skill by renathoaz

IndexedMIT★ 2
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Structured root-cause investigation for bugs and failures.

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Secrets
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Install scripts
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Runs as
Node.js package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/debugging

What's inside

Skill name
debugging
Description
Structured root-cause investigation for bugs and failures.
License
MIT
Instructions
173 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (10)

  • SKILL.md
  • find-polluter.sh
  • condition-based-waiting-example.ts · references/
  • condition-based-waiting.md · references/
  • defense-in-depth.md · references/
  • root-cause-tracing.md · references/
  • test-academic.md
  • test-pressure-1.md
  • test-pressure-2.md
  • test-pressure-3.md

SKILL.md (first lines)

# Debugging

## Constraint

```
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION
```

If Phase 1 is incomplete, no fix proposals allowed. Period.

## When to Activate

Any technical malfunction: test failures, runtime errors, unexpected behavior, performance regressions, build breaks, integration mismatches.

Activate **especially** when:
- Time pressure makes guessing tempting
- An "obvious quick fix" presents itself
- Previous attempts have not resolved the issue
- The failure mechanism is not fully understood

## Investigation Protocol

Complete each phase sequentially. Do not skip ahead.

### Phase 1: Root Cause Investigation

**1. Read error output thoroughly**
- Full stack traces, not just the top frame
- Note line numbers, file paths, error codes
- Warnings preceding the error often contain the answer

**2. Reproduce consistently**
- Determine exact trigger steps
- Confirm it repeats reliably
- If non-reproducible: gather more data — do not guess

**3. Check recent changes**
- `git diff`, `git log --oneline -10`
- New dependencies, config modifications, environment drift

**4. Instrument multi-component boundaries**

When the system spans multiple layers (CI pipeline, API chain, build tooling):

```
For EACH component boundary:
  - Log inputs entering the component
  - Log outputs leaving the component
  - Verify config/env propagation across the boundary
  - Run once, collect evidence, identify the failing seam
```

**5. Trace data flow backward**

See `references/root-cause-tracing.md` for the full technique.

Short version: start at the crash site, ask "where did this bad value come from?", trace upstream until you reach the originating mutation. Fix there.

### Phase 2: Pattern Comparison

1. Locate analogous working code in the same codebase
2. If implementing a known pattern, read the reference implementation completely — no skimming
3. Diff working vs. broken — list every difference, however trivial
4. Map dependencies: config, environment, shared state, ordering assumptions

### Phase 3: Hypothesis Testing

1. State one hypothesis explicitly: "X causes the failure because Y"
2. Make the smallest possible change to test it — one variable only
3. Evaluate:
   - Confirmed → proceed to Phase 4
   - Refuted → form a new hypothesis from the new evidence
   - Do not layer fixes on top of each other

### Phase 4: Fix and Verify

1. **Write a failing test** that captures the bug (use `arsyn:tdd` if available)
2. **Apply a single fix** targeting the root cause — no bundled refactoring
3. **Run the full relevant test suite** — not just the new test
4. **If the fix fails:**
   - Count total attempts
   - Under 3: return to Phase 1 with new data
   - **3 or more: stop fixing. The problem is likely architectural.**

**On 3+ failed attempts:**
Indicators of structural mismatch:
- Each fix surfaces new coupling in a different location
- Fixes demand large-scale refactoring to implement
- Each patch spawns new symptoms elsewhere

Stop and reconsider fundamentals: Is the current approach viable, or is inertia the only reason to continue? Escalate for discussion before further changes.

## File-System-as-State Pattern

For investigations spanning many files or long debugging sessions, offload findings to disk instead of relying on context memory:

```bash
# Initialize a debug log at the start of investigation
echo "# Debug Log — $(date -Iminutes)" > debug-log.md

# Append findings as you go
echo "## Phase 1 — Error Analysis" >> debug-log.md
echo "- Stack trace points to auth.ts:142" >> debug-log.md
echo "- Token is null at middleware boundary" >> debug-log.md

# Append hypotheses and results
echo "## Hypothesis 1: Token not forwarded by proxy" >> debug-log.md
echo "- Result: REFUTED — proxy logs show token present" >> debug-log.md

# Later, grep across your own findings instead of scrolling context
grep "REFUTED\|CONFIRMED" debug-log.md
```

Benefits:
- Survives context window limits in long sessions
- Grep-searchable — faster than re-reading conversation history
- Shareable artifact for post-incident review
- Prevents re-testing already-refuted hypotheses

From renathoaz/arsyn, MIT.

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Last commit (this folder)
Mar 24, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

debugging skill. Agent Mart. https://agentmart.dev/skills/renathoaz-debugging (last updated 2026-10-24)