mastermind-debug

name	mastermind-debug
description	Systematic root-cause debugging protocol. Use before ANY fix attempt — for test failures, bugs, unexpected behavior, build failures, performance regressions.

mastermind:debug — Systematic Debugging

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 or staging
• Unexpected behavior
• Performance regressions
• 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 that didn't work
• Previous fix didn't resolve the issue
• You don't fully understand what's happening

Don't skip when:

• Issue seems simple (simple bugs have root causes too)
• You're in a hurry (systematic is faster than thrashing)
• The user wants it fixed immediately (systematic approach finds the real fix faster)

The Four Phases

You MUST complete each phase before proceeding to the next.

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Phase 1: Root Cause Investigation

BEFORE attempting ANY fix:

1. Read Error Messages Carefully

   • Don't skim 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 the issue 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 between working and broken states

4. Gather Evidence in Multi-Component Systems

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

   BEFORE proposing fixes, add diagnostic instrumentation:

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

   Example:

   # Layer 1: entry point
   echo "=== Input received: ==="
   echo "VAR: ${VAR:+SET}${VAR:-UNSET}"
   
   # Layer 2: processing
   echo "=== State after processing: ==="
   env | grep VAR || echo "VAR not in environment"
   
   # Layer 3: output/result
   echo "=== Final state: ==="
   

5. Trace Data Flow

   WHEN the error is deep in a call stack:

   • Where does the bad value originate?
   • What called this with the bad value?
   • Keep tracing up until you find the source
   • Fix at the source, not at the symptom — a null crashing in layer 4 usually entered in layer 1

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Phase 2: Pattern Analysis

Once you understand WHERE the break is, find existing working examples:

1. Find working code that solves the same problem

   • Search the codebase for similar patterns
   • Check git history for when this worked
   • Find the canonical implementation to compare against

2. Compare against references

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

3. Compare broken vs. working

   • What's different?
   • List every difference, not just the obvious one — don't assume "that can't matter"
   • Environment, config, data, timing

4. Understand dependencies

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

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Phase 3: Form and Test a Hypothesis

1. State your hypothesis explicitly:

   "I believe the root cause is [X] because [evidence Y] and [evidence Z]."

2. Test the hypothesis minimally

   • Change one thing and observe the result
   • If it doesn't confirm or deny, gather more evidence
   • A confirmed hypothesis means you understand the root cause

3. If hypothesis is wrong: Do NOT add more fixes. Return to Phase 1 with the new information.

4. When you don't know: Say "I don't understand X". Don't pretend to know. Research more or ask the user — a stated unknown beats a confident guess.

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Phase 4: Implementation

1. Write a failing test first (before touching production code)

   • Automated test where possible; a one-off test script if no framework
   • Use Skill("mastermind-tdd") for writing proper failing tests
   • The test MUST fail before the fix proves it

2. Implement a single fix

   • Address the root cause identified in Phase 3
   • ONE change at a time
   • No "while I'm here" improvements
   • No bundled refactoring

3. Verify the fix

   • Test passes now?
   • No other tests broken?
   • Issue actually resolved?
   • Use Skill("mastermind-review") to verify before declaring done

4. If the fix doesn't work:

   • STOP
   • Count: How many fixes have you tried?
   • If < 3: Return to Phase 1, re-analyze with the new information
   • If ≥ 3: STOP — this is likely an architectural problem (see Phase 4.5 below)
   • Do NOT attempt a 4th fix without architectural discussion

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Phase 4.5 — If 3+ Fixes Have Failed: Question the Architecture

Pattern indicating an architectural problem:

• Each fix reveals new coupling or shared state issues in a different place
• Each fix creates new symptoms elsewhere
• Fixes require "massive refactoring" to implement cleanly

STOP and question fundamentals:

• Is this design pattern fundamentally sound?
• Are we continuing out of inertia?
• Should we refactor the architecture rather than continue patching?

Discuss with the user before attempting more fixes. This is not a failed hypothesis — this is the wrong architecture.

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Red Flags — STOP and Return to Phase 1

If you catch yourself thinking or doing any of these:

Thought / Action	What it means
"Quick fix for now, investigate later"	You're skipping root cause. Return to Phase 1.
"Just try changing X and see if it works"	Guessing. Return to Phase 1.
"Add multiple changes, run tests"	Can't isolate what worked. Return to Phase 1.
"Skip the test, I'll manually verify"	Untested fixes don't stick. Write the failing test.
"Pattern says X but I'll adapt it differently"	Partial understanding guarantees bugs. Read the reference completely.
"Here are the main problems: …" (listing fixes without investigation)	Solutions before evidence. Return to Phase 1.
"It's probably X, let me fix that"	Assumed root cause. Verify it first. Return to Phase 1.
"I don't fully understand but this might work"	You don't have a hypothesis. Return to Phase 1.
Proposing solutions before tracing data flow	No root cause. Return to Phase 1.
"One more fix attempt" (already tried 2+)	3+ failures = architectural problem. Phase 4.5.
Each fix reveals a new problem in a different place	Architectural problem. Phase 4.5.

User Signals You're Doing It Wrong

Watch for these redirections from the user:

Signal	What it means
"Is that actually happening?"	You assumed without verifying. Add evidence gathering.
"What does the output show?"	You should have checked before proposing a fix.
"Stop guessing"	You're proposing fixes without root cause. Phase 1.
"We keep going in circles"	3+ fix attempts = architectural problem. Phase 4.5.

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 the test after confirming the fix works"	Untested fixes don't stick. Test first proves it.
"Multiple fixes at once saves time"	Can't isolate what worked. Creates new bugs.
"Reference too long, I'll adapt the pattern"	Partial understanding guarantees bugs. Read it completely.
"I can see the problem, let me fix it"	Seeing symptoms ≠ understanding root cause.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question the pattern.

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	Identified differences
3. Hypothesis	Form theory, test minimally	Confirmed or new hypothesis
4. Implementation	Write failing test, fix, verify	Bug resolved, tests pass

When the Process Reveals "No Root Cause"

If systematic investigation reveals the 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, clear error message)
4. Add monitoring/logging for future investigation

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

Supporting Techniques

• Root-cause tracing: when a bad value crashes deep in the stack, trace it backward caller by caller until you find where it was created; fix there. Adding a guard at the crash site only hides the origin.
• Defense in depth: AFTER fixing the root cause, add validation at the layers the bad value passed through unchecked — so the next bad value fails loudly at its first boundary, not silently at its fourth.
• Condition-based waiting: replace arbitrary sleeps/timeouts in flaky tests and scripts with polling for the actual condition ("wait until the file exists / the port answers"), with a hard cap. Timing guesses are bugs waiting for a slower machine.

Related skills:

• Skill("mastermind-tdd") — for creating the failing test case (Phase 4, Step 1)
• Skill("mastermind-verify") — verify the fix worked before claiming success

Impact

Systematic approach vs random fixing:

• Time to fix: 15-30 min vs 2-3 hours of thrashing
• First-time fix rate: ~95% vs ~40%
• New bugs introduced by the fix: near zero vs common