debug

name	debug
description	Systematic debugging with hypothesis-driven investigation. Use when something is broken, tests are failing, unexpected behavior occurs, or errors need investigation. Triggers on: 'this is broken', 'debug', 'why is this failing', 'unexpected error', 'not working', 'bug', 'fix this issue', 'investigate', 'tests failing', 'trace the error', 'use debug mode'. Full access mode - can run commands, add logging, and fix issues.
allowed-tools	["Read","Edit","Write","Bash","Grep","Glob","LSP"]

Debug Mode

Systematic bug investigation and resolution.

Core Approach

"Don't guess. Form hypotheses. Test them."

The 4-Phase Process

Phase 1: Assessment 🔍

Goal: Understand and reproduce

• What is the expected behavior?
• What is the actual behavior?
• Can you reliably reproduce?
• What changed recently?

Key Questions:

• When did this start happening?
• Does it happen consistently or intermittently?
• What are the exact inputs that trigger it?
• What error messages or symptoms appear?

Building a Feedback Loop

This is the most important step. If you have a fast, deterministic, agent-runnable pass/fail signal, you will find the cause. If you don't, no amount of code-staring will save you. Spend disproportionate effort here.

Techniques — try in roughly this order:

1. Failing test at whatever seam reaches the bug (unit, integration, e2e)
2. Curl / HTTP script against a running dev server
3. CLI invocation with fixture input, diffing stdout against known-good output
4. Headless browser script (Playwright/Puppeteer) — drives UI, asserts on DOM/console
5. Replay captured trace — save a real request/payload to disk, replay through the code path
6. Throwaway harness — minimal subset of system that exercises the bug path
7. Property/fuzz loop — if "sometimes wrong output", run 1000 random inputs
8. Bisection harness — automate git bisect run between known-good and known-bad
9. Differential loop — run same input through old vs new version, diff outputs

Iterate on the loop: Can you make it faster? Sharper signal? More deterministic?

If you cannot build a loop: Stop and say so. List what you tried. Ask for: captured artifacts (logs, HAR file), environment access, or permission to add temporary instrumentation.

Phase 2: Investigation 🔬

Goal: Isolate and trace

• Trace execution from entry point
• Identify where expected diverges from actual
• Form hypotheses about root cause
• Test hypotheses systematically

Techniques:

• Add strategic logging/prints
• Use debugger breakpoints
• Simplify inputs to minimal reproduction
• Check boundary conditions

Phase 3: Resolution 🔧

Goal: Fix minimally and verify

• Implement the smallest fix that addresses root cause
• Don't fix symptoms, fix the disease
• Add regression test
• Verify fix doesn't break other things

If fix doesn't work:

• Count: How many fixes attempted?
• If < 3: Return to Phase 1, re-analyze with new information
• If ≥ 3: STOP. Question your understanding of the system.

Phase 4: Quality ✅

Goal: Prevent recurrence

• Add test covering the bug
• Document if the cause was non-obvious
• Consider if similar bugs exist elsewhere
• Clean up debug code

Debugging Checklist

- [ ] **Reproduced**: Can trigger bug consistently
- [ ] **Isolated**: Know which component is failing
- [ ] **Root Cause**: Understand WHY it fails
- [ ] **Fixed**: Minimal change addresses cause
- [ ] **Tested**: Regression test added
- [ ] **Clean**: Debug code removed

Hypothesis Template

For each hypothesis, record: Hypothesis (what's wrong) → Test (how to verify) → Result → Conclusion (confirmed/rejected/needs more info).

Common Root Causes

Symptom	Often Caused By
Works locally, fails in CI	Environment differences, missing deps
Intermittent failure	Race condition, timing, external dependency
Wrong output	Logic error, wrong variable, off-by-one
Crash/exception	Null/None access, type mismatch, missing data
Performance issue	N+1 queries, missing index, memory leak

Rationalization Prevention

Excuse	Reality	Required Action
"The fix is obvious"	Obvious fixes mask root causes	Form a hypothesis and verify before changing code
"It's probably X"	"Probably" isn't evidence	Test the hypothesis — name it, design a test, run it
"This is too simple to debug formally"	Simple bugs waste the most time undiagnosed	Follow Phase 1 — reproduce, isolate, then fix
"Logs look clean"	You didn't add targeted logging	Add debug logging at the suspected point
"I've tried 3 things, might as well try a 4th"	Stacking guesses compounds confusion	STOP. Return to Phase 1. Re-analyze with new info
"It works now"	If you don't know why, it will break again	Explain WHY it works and what changed

Red Flags - STOP and Re-Assess

If you catch yourself skipping reproduction ("I know what's wrong") or testing multiple hypotheses at once — STOP. Return to Phase 1.

Debug Report Format

## Debug Report

### Bug Summary

- **Expected**: [what should happen]
- **Actual**: [what happens instead]
- **Severity**: [critical/high/medium/low]

### Reproduction

1. [Step to reproduce]
2. [Step to reproduce]
3. [Observe bug]

**Minimal reproduction**: [simplest case that triggers bug]

### Investigation

| Hypothesis | Test           | Result                     |
| ---------- | -------------- | -------------------------- |
| [theory]   | [what I tried] | ✅ Confirmed / ❌ Rejected |

### Root Cause

[What's actually wrong and why]

### Fix Applied

- **File**: `path/to/file.py`
- **Change**: [what was modified]
- **Why**: [how this fixes the root cause]

### Verification

- [ ] Bug no longer reproduces
- [ ] Existing tests pass
- [ ] Regression test added: `test_name`
- [ ] No debug code left behind

### Prevention

[How to prevent similar bugs in the future]