cm-debugging

name	cm-debugging
description	Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
token_budget	1500
compressed	true
deprecated	false

Systematic Debugging

TL;DR

• Use when any bug, test failure, or unexpected behavior surfaces
• Process: reproduce → isolate → diagnose root cause → fix
• Defense in depth: also add a test that locks the bug
• Next: cm-tdd → cm-quality-gate

Overview

Random fixes waste time and create new bugs. Quick patches mask underlying issues.

Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.

Violating the letter of this process is violating the spirit of debugging.

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you haven't completed Phase 1, you cannot propose fixes.

When to Use

Use for ANY technical issue:

• Test failures
• Bugs in production
• Unexpected behavior
• Performance problems
• Build failures
• Integration issues

Use this ESPECIALLY when:

• Under time pressure (emergencies make guessing tempting)
• "Just one quick fix" seems obvious
• You've already tried multiple fixes
• Previous fix didn't work
• You don't fully understand the issue

Don't skip when:

• Issue seems simple (simple bugs have root causes too)
• You're in a hurry (rushing guarantees rework)
• Manager wants it fixed NOW (systematic is faster than thrashing)

The Four Phases

You MUST complete each phase before proceeding to the next.

Phase 0.5: Memory Integrity Check (BEFORE blaming code)

BEFORE blaming code, ASK: "Could memory be causing this bug?"

1. SUSPECT — Identify relevant memories:

   • What module/file is the bug in?
   • Read .cm/learnings.json filtered by that scope
   • List all active learnings + decisions for this area

2. INVESTIGATE — Did AI follow a memory when writing buggy code?

   • Check: Does the buggy code match a prevention pattern from any learning?
   • Check: Does the buggy code follow a decision that may be outdated?
   • If YES → that memory is a suspect

3. VERIFY — Is the suspect memory still correct?

   • Compare learning with current codebase (not when it was recorded)
   • Has the dependency/pattern/architecture changed since learning was recorded?
   • If memory is WRONG → proceed to HEAL

4. HEAL (only if memory confirmed as cause):

   • Invalidate: Set status = "invalidated" — learning is proven wrong
   • Correct: Update prevention with correct info, set status = "corrected"
   • Scope-reduce: Learning is right for smaller scope → narrow the scope
   • Record meta-learning in .cm/meta-learnings.json

IF memory caused the bug:
  → HEAL memory FIRST
  → THEN proceed to Phase 1 to fix code
  → The code fix will be correct because memory is now correct

IF memory did NOT cause the bug:
  → Proceed to Phase 1 normally

WHY PHASE 0.5? Fix memory first → code fix will be correct. Without fixing memory → bug will return next session (bug loop).

Phase 1: Root Cause Investigation

BEFORE attempting ANY fix:

1. Read Error Messages Carefully

   • Don't skip past errors or warnings
   • They often contain the exact solution
   • Read stack traces completely
   • Note line numbers, file paths, error codes

2. Reproduce Consistently

   • Can you trigger it reliably?
   • What are the exact steps?
   • Does it happen every time?
   • If not reproducible → gather more data, don't guess

3. Check Recent Changes

   • What changed that could cause this?
   • Git diff, recent commits
   • New dependencies, config changes
   • Environmental differences

4. Gather Evidence in Multi-Component Systems

   WHEN system has multiple components (CI → build → signing, API → service → database):

   BEFORE proposing fixes, add diagnostic instrumentation:

   For EACH component boundary:
     - Log what data enters component
     - Log what data exits component
     - Verify environment/config propagation
     - Check state at each layer
   
   Run once to gather evidence showing WHERE it breaks
   THEN analyze evidence to identify failing component
   THEN investigate that specific component
   

5. Trace Data Flow

   WHEN error is deep in call stack:

   • Where does bad value originate?
   • What called this with bad value?
   • Keep tracing up until you find the source
   • Fix at source, not at symptom

Phase 2: Pattern Analysis

Find the pattern before fixing:

1. Find Working Examples

   • Locate similar working code in same codebase
   • What works that's similar to what's broken?

2. Compare Against References

   • If implementing pattern, read reference implementation COMPLETELY
   • Don't skim - read every line
   • Understand the pattern fully before applying

3. Identify Differences

   • What's different between working and broken?
   • List every difference, however small
   • Don't assume "that can't matter"

4. Understand Dependencies

   • What other components does this need?
   • What settings, config, environment?
   • What assumptions does it make?

Phase 3: Hypothesis and Testing

Scientific method:

1. Form Single Hypothesis

   • State clearly: "I think X is the root cause because Y"
   • Write it down
   • Be specific, not vague

2. Test Minimally

   • Make the SMALLEST possible change to test hypothesis
   • One variable at a time
   • Don't fix multiple things at once

3. Verify Before Continuing

   • Did it work? Yes → Phase 4
   • Didn't work? Form NEW hypothesis
   • DON'T add more fixes on top

4. When You Don't Know

   • Say "I don't understand X"
   • Don't pretend to know
   • Ask for help
   • Research more

Phase 4: Implementation

Fix the root cause, not the symptom:

1. Create Failing Test Case

   • Simplest possible reproduction
   • Automated test if possible
   • MUST have before fixing
   • Use the cm-tdd skill for writing proper failing tests

2. Implement Single Fix

   • Address the root cause identified
   • ONE change at a time
   • No "while I'm here" improvements
   • No bundled refactoring

3. Verify Fix

   • Test passes now?
   • No other tests broken?
   • Issue actually resolved?

4. If Fix Doesn't Work

   • STOP
   • Count: How many fixes have you tried?
   • If < 3: Return to Phase 1, re-analyze with new information
   • If ≥ 3: STOP and question the architecture (step 5 below)
   • DON'T attempt Fix #4 without architectural discussion

5. If 3+ Fixes Failed: Question Architecture

   Pattern indicating architectural problem:

   • Each fix reveals new shared state/coupling/problem in different place
   • Fixes require "massive refactoring" to implement
   • Each fix creates new symptoms elsewhere

   Discuss with your human partner before attempting more fixes

   This is NOT a failed hypothesis - this is a wrong architecture.

Step 5: Record Learning (MANDATORY)

After fixing any bug, ALWAYS write to .cm/CONTINUITY.md → "Mistakes & Learnings":

- What Failed:      [exact error message or behavior]
- Why It Failed:    [root cause from Phase 1]
- How to Prevent:   [concrete pattern to avoid]
- Scope:            [global | module:{name} | file:{path}]

Scope rules: Choose the SMALLEST scope that applies.

• Bug in one file → file:src/api/routes.ts
• Bug in module pattern → module:auth
• Bug in project-wide practice → global

Anti-duplicate: If a similar learning already exists in .cm/learnings.json, reinforce it (reinforceCount++) instead of creating a new entry.

Token savings: Next time same error pattern appears, AI reads the learning (~50 tokens) instead of repeating full 4-phase debug cycle (~3,000 tokens). This is the #1 token saver in the entire kit.

---

Red Flags - STOP and Follow Process

If you catch yourself thinking:

• "Quick fix for now, investigate later"
• "Just try changing X and see if it works"
• "Add multiple changes, run tests"
• "Skip the test, I'll manually verify"
• "It's probably X, let me fix that"
• "I don't fully understand but this might work"
• "Pattern says X but I'll adapt it differently"
• "Here are the main problems: [lists fixes without investigation]"
• Proposing solutions before tracing data flow
• "One more fix attempt" (when already tried 2+)
• Each fix reveals new problem in different place

ALL of these mean: STOP. Return to Phase 1.

When you see these user signals: STOP. Return to Phase 1.

• "Stop guessing"
• "Ultrathink this"

Common Rationalizations

Excuse	Reality
"Issue is simple, don't need process"	Simple issues have root causes too. Process is fast for simple bugs.
"Emergency, no time for process"	Systematic debugging is FASTER than guess-and-check thrashing.
"Just try this first, then investigate"	First fix sets the pattern. Do it right from the start.
"I'll write test after confirming fix works"	Untested fixes don't stick. Test first proves it.
"Multiple fixes at once saves time"	Can't isolate what worked. Causes new bugs.

Quick Reference

Phase	Key Activities	Success Criteria
1. Root Cause	Read errors, reproduce, check changes, gather evidence	Understand WHAT and WHY
2. Pattern	Find working examples, compare	Identify differences
3. Hypothesis	Form theory, test minimally	Confirmed or new hypothesis
4. Implementation	Create test, fix, verify	Bug resolved, tests pass

When Process Reveals "No Root Cause"

If systematic investigation reveals issue is truly environmental, timing-dependent, or external:

1. You've completed the process
2. Document what you investigated
3. Implement appropriate handling (retry, timeout, error message)
4. Add monitoring/logging for future investigation

But: 95% of "no root cause" cases are incomplete investigation.

Integration

Related skills:

• cm-tdd - For creating failing test case (Phase 4, Step 1)
• cm-quality-gate - Verify fix worked before claiming success