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systematic-debugging

debuggingbug fixingtroubleshootingroot cause analysiscode qualitysoftware developmentproblem solvingai agent
229.6k📄 MIT🕒 2026-06-16Source ↗

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npx skills add obra/superpowers

Works across Claude Code, Cursor, Codex, Copilot & Antigravity

Systematic debugging enforces a disciplined, scientific methodology to resolve technical failures, prioritizing structural root cause analysis over reactive patching. Instead of guessing or applying broad code changes, this skill requires agents to verify assumptions at every component boundary. It mandates that any proposed fix be preceded by a clear, evidence-based hypothesis. By treating debugging as an experimental process—where log inspection, reproducible test cases, and diff analysis precede any modification—this approach prevents the common trap of layering new bugs on top of existing issues. This strategy is essential for high-stakes environments where code complexity masks the origin of errors, ensuring that the final repair is precise, minimal, and verified against a controlled test case that accurately replicates the initial failure state.

When to Use This Skill

  • Diagnosing intermittent failures in multi-layered microservice architectures
  • Resolving production errors that persist despite multiple attempted patches
  • Identifying environmental drift between development and deployment pipelines
  • Debugging complex stack traces where the initial error message is misleading

How to Invoke This Skill

Example prompts that trigger this skill in Claude Code, Cursor, or Antigravity:

  • Find the root cause of this error
  • Stop, let's debug this systematically
  • Identify why this test is failing
  • Trace the data flow to find the source of the bug
  • Help me form a hypothesis for this failure

Pro Tips

  • 💡Always verify your assumptions: The most obvious 'fix' is often a distraction from the true root cause. Use logging and breakpoint debugging extensively.
  • 💡Isolate the problem: Systematically narrow down the scope by eliminating variables or components until the issue's origin becomes clear.
  • 💡Document your investigation: Keep a log of what you tried, what you observed, and your hypotheses. This prevents repetition and helps if you need to hand off the issue.

What this skill does

  • Isolation of failure points through layered instrumentation
  • Evidence-based root cause identification via data tracing
  • Pattern matching against known working implementation examples
  • Scientific hypothesis generation and verification
  • Automated reproduction of bugs using minimal test cases

When not to use it

  • When a quick temporary hotfix is legally or contractually mandated to restore service
  • During early-stage prototyping where the focus is on exploration rather than stability

Example workflow

  1. Analyze the error message and stack trace to identify the failing module
  2. Create a minimal, reproducible test case to isolate the issue
  3. Instrument component boundaries with diagnostic logging to trace data flow
  4. Compare the failing code against a known-working reference implementation
  5. Formulate a specific hypothesis and apply a single, targeted code fix
  6. Verify the fix with the reproduction test and confirm no regression occurs

Prerequisites

  • A codebase with existing test infrastructure
  • Access to environment logs and build configurations

Pitfalls & limitations

  • !Attempting to fix symptoms rather than tracing back to the source
  • !Making multiple simultaneous code changes that obscure the actual solution
  • !Skipping the reproduction phase in favor of immediate code editing

FAQ

Why can't I just fix the error immediately?
Quick fixes often address the symptom while ignoring the underlying trigger, which leads to recurring bugs and technical debt.
What if I can't reproduce the bug?
If you cannot trigger the failure reliably, you must gather more diagnostic data or environment details before proposing any code changes.
How do I handle bugs in complex multi-layer systems?
Log the data entering and exiting each component boundary to identify specifically where the transformation or state handling fails.
Is this approach slower than just debugging normally?
While it requires more front-loaded effort, it is faster in the long run because it prevents the 'trial and error' thrashing that commonly extends debugging sessions.

How it compares

Unlike generic debugging which often relies on intuition or trial-and-error, this skill forces a strict, phase-gated adherence to the scientific method to ensure permanent resolution.

Source & trust

230k stars📄 MIT🕒 Updated 2026-06-16
📄 Full skill instructions — original source: obra/superpowers
# 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
- 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 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


**Example (multi-layer system):**
# Layer 1: Workflow
echo "=== Secrets available in workflow: ==="
echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"

# Layer 2: Build script
echo "=== Env vars in build script: ==="
env | grep IDENTITY || echo "IDENTITY not in environment"

# Layer 3: Signing script
echo "=== Keychain state: ==="
security list-keychains
security find-identity -v

# Layer 4: Actual signing
codesign --sign "$IDENTITY" --verbose=4 "$APP"


**This reveals:** Which layer fails (secrets → workflow ✓, workflow → build ✗)

5. **Trace Data Flow**

**WHEN error is deep in call stack:**

See root-cause-tracing.md in this directory for the complete backward tracing technique.

**Quick version:**
- 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
- One-off test script if no framework
- MUST have before fixing
- Use the superpowers:test-driven-development 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

**STOP and question fundamentals:**
- Is this pattern fundamentally sound?
- Are we "sticking with it through sheer inertia"?
- Should we refactor architecture vs. continue fixing symptoms?

**Discuss with your human partner before attempting more fixes**

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

## 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.**

**If 3+ fixes failed:** Question the architecture (see Phase 4.5)

## your human partner's Signals You're Doing It Wrong

**Watch for these redirections:**
- "Is that not happening?" - You assumed without verifying
- "Will it show us...?" - You should have added evidence gathering
- "Stop guessing" - You're proposing fixes without understanding
- "Ultrathink this" - Question fundamentals, not just symptoms
- "We're stuck?" (frustrated) - Your approach isn't working

**When you see these:** STOP. Return to Phase 1.

## 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. |
| "Reference too long, I'll adapt the pattern" | Partial understanding guarantees bugs. Read it completely. |
| "I see the problem, let me fix it" | Seeing symptoms ≠ understanding root cause. |
| "One more fix attempt" (after 2+ failures) | 3+ failures = architectural problem. Question pattern, don't fix again. |

## 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.

## Supporting Techniques

These techniques are part of systematic debugging and available in this directory:

- **root-cause-tracing.md** - Trace bugs backward through call stack to find original trigger
- **defense-in-depth.md** - Add validation at multiple layers after finding root cause
- **condition-based-waiting.md** - Replace arbitrary timeouts with condition polling

**Related skills:**
- **superpowers:test-driven-development** - For creating failing test case (Phase 4, Step 1)
- **superpowers:verification-before-completion** - Verify fix worked before claiming success

## Real-World Impact

From debugging sessions:
- Systematic approach: 15-30 minutes to fix
- Random fixes approach: 2-3 hours of thrashing
- First-time fix rate: 95% vs 40%
- New bugs introduced: Near zero vs common

How to Use This Skill Unit

Option A: Project-Specific (Recommended)

  1. Click "Download" above
  2. In your project, create the directory: .agent/skills/systematic-debugging/
  3. Save the file as SKILL.md
  4. The agent will automatically discover the skill based on its description.

Option B: Global Installation (All Agents)

Save the file to these locations to make it available across all projects:

  • Claude Code: ~/.claude/skills/obra/superpowers/systematic-debugging/SKILL.md
  • Cursor: ~/.cursor/skills/obra/superpowers/systematic-debugging/SKILL.md
  • Antigravity: ~/.gemini/antigravity/skills/obra/superpowers/systematic-debugging/SKILL.md

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For Cursor & Windsurf

For Cursor or Windsurf, individual skills are best used in the "Rules for AI" section. This specific unit helps the agent avoid debugging & troubleshooting issues, leading to cleaner, more efficient code.

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Source & attribution

This skill is categorized under Debugging & Troubleshooting and is published by Jesse Vincent, maintained in obra/superpowers.

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