/systematic-debugging

name: dev-systematic-debugging
description: "4-phase root cause debugging: understand bugs before fixing."
version: 1.1.0
author: Hermes Agent (adapted from obra/superpowers)
license: MIT
metadata:
  hermes:
    tags: [debugging, troubleshooting, problem-solving, root-cause, investigation]
    related_skills: [test-driven-development, writing-plans, subagent-driven-development]
origin: adapted
source_repo: obra/superpowers
source_url: https://github.com/obra/superpowers
source_license: MIT
language: en

Systematic Debugging

Overview

Random fixes waste time and create new bugs. Quick patches mask underlying issues. This skill enforces a 4-phase approach: understand the bug before fixing it.

Common Pitfalls

Bash Installer Scripts

**Invalid Commands:** Check command exists before using (e.g., `hermes config get` doesn't exist, use `hermes config show` or grep config file directly)

**Missing Auth Checks:** After installing CLI tools (gh, glab), check authentication before using (e.g., `gh auth status`)

**Wrong Installation Paths:** When cloning repos with nested structure, copy inner content not outer directory

**Hardcoded Values:** Detect username/repo dynamically, don't hardcode specific user

**Variable Expansion:** Use `<< EOF` (no quotes) for variable expansion in heredocs, `<< 'EOF'` to prevent

**Error Messages:** Include troubleshooting hints (possible causes, manual steps), not just "failed"

The 4-Phase Approach

**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)
• Someone 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

**Action:** Use `read_file` on the relevant source files. Use `search_files` to find the error string in the codebase.

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

**Action:** Use the `terminal` tool to run the failing test or trigger the bug:

# Run specific failing test
pytest tests/test_module.py::test_name -v

# Run with verbose output
pytest tests/test_module.py -v --tb=long

3. Check Recent Changes

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

**Action:**

# Recent commits
git log --oneline -10

# Uncommitted changes
git diff

# Changes in specific file
git log -p --follow src/problematic_file.py | head -100

4. Gather Evidence in Multi-Component Systems

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

**BEFORE proposing fixes, add diagnostic instrumentation:**

For EACH component boundary:

• Log what data enters the component
• Log what data exits the component
• Verify environment/config propagation
• Check state at each layer

Run once to gather evidence showing WHERE it breaks. THEN analyze evidence to identify the failing component. THEN investigate that specific component.

5. Trace Data Flow

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

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

**Action:** Use `search_files` to trace references:

# Find where the function is called
search_files("function_name(", path="src/", file_glob="*.py")

# Find where the variable is set
search_files("variable_name\\s*=", path="src/", file_glob="*.py")

Phase 1 Completion Checklist

• [ ] Error messages fully read and understood
• [ ] Issue reproduced consistently
• [ ] Recent changes identified and reviewed
• [ ] Evidence gathered (logs, state, data flow)
• [ ] Problem isolated to specific component/code
• [ ] Root cause hypothesis formed

**STOP:** Do not proceed to Phase 2 until you understand WHY it's happening.

---

Phase 2: Pattern Analysis

**Find the pattern before fixing:**

1. Find Working Examples

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

**Action:** Use `search_files` to find comparable patterns:

search_files("similar_pattern", path="src/", file_glob="*.py")

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

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 a 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 the hypothesis
• One variable at a time