hermes-agent/skills/software-development/systematic-debugging/SKILL.md

---
name: 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
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [debugging, troubleshooting, problem-solving, root-cause, investigation]
    related_skills: [test-driven-development, plan, subagent-driven-development]
---

# 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.

## The Feedback Loop Rule

The feedback loop is the debugging work. Before reading code to build a theory, create or identify a **tight** command that can go red on the user's exact symptom and green when the bug is fixed. A tight loop is fast, deterministic, agent-runnable, and specific enough to catch this bug — not merely "doesn't crash".

When a clean repro is hard, spend disproportionate effort building the loop. Guessing without a red-capable loop is the failure mode this skill exists to prevent.

## 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. Build a Tight Feedback Loop

- Can you trigger the user's exact symptom with one command?
- Does the command fail for this bug and only pass once the bug is fixed?
- Is it fast enough to run repeatedly?
- Is it deterministic? For flaky bugs, can you raise the reproduction rate high enough to debug?
- If not reproducible → gather more data, don't guess.

**Ways to construct a loop — try in roughly this order:**

1. **Failing test** at the seam that reaches the bug: unit, integration, or end-to-end.
2. **HTTP script / curl** against a running dev server.
3. **CLI invocation** with fixture input, diffing stdout/stderr against expected output.
4. **Headless browser script** (Playwright/Puppeteer) asserting on DOM, console, or network.
5. **Replay a captured trace**: HAR, request payload, event log, queue message, or webhook body.
6. **Throwaway harness** that boots the smallest useful slice of the system and calls the failing path.
7. **Property / fuzz loop** when the bug is intermittent wrong output over a broad input space.
8. **Bisection harness** suitable for `git bisect run` when the bug appeared between two known states.
9. **Differential loop** comparing old vs new version, two configs, two providers, or two datasets.
10. **Human-in-the-loop script** only as a last resort: script the human steps and capture their result so the loop stays structured.

**Tighten the loop once it exists:**

- Make it faster: cache setup, narrow scope, skip unrelated initialization.
- Make the signal sharper: assert the exact symptom, not generic success.
- Make it more deterministic: pin time, seed randomness, isolate filesystem, freeze network.

For non-deterministic bugs, the immediate goal is a higher reproduction rate, not perfection. Run the trigger 100x, parallelize, add stress, narrow timing windows, or inject sleeps. A 50% flake is debuggable; a 1% flake usually is not.

**Action:** Use the `terminal` tool to run the tight loop:

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

# Or run a scripted repro
python scripts/repro_bug.py

# Or run a high-repetition flaky repro
for i in {1..100}; do pytest tests/test_flake.py::test_name -q || break; done
```

### 3. Check Recent Changes

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

**Action:**

```bash
# 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:

```python
# 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
- [ ] A tight loop command exists and has been run at least once
- [ ] Loop is red-capable: it asserts the user's exact symptom, not a nearby failure
- [ ] Loop is deterministic, or a flaky bug has a high enough reproduction rate to debug
- [ ] Recent changes identified and reviewed
- [ ] Evidence gathered (logs, state, data flow)
- [ ] Problem isolated to specific component/code
- [ ] Root cause hypotheses can be stated and tested

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

---

## Phase 2: Pattern Analysis

**Find the pattern before fixing:**

### 0. Minimize the Reproduction

Once the loop is red, shrink the repro to the smallest scenario that still goes red. Cut inputs, callers, config, data, and steps **one at a time**, re-running the loop after each cut. Keep only what is load-bearing for the failure.

Done when removing any remaining element makes the loop go green. A minimal repro narrows the hypothesis space and often becomes the cleanest regression test.

### 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:

```python
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 Ranked Falsifiable Hypotheses

- Generate 3–5 plausible hypotheses before testing any single one.
- Rank them by likelihood and cheapness to falsify.
- State the prediction each hypothesis makes: "If X is the cause, then changing or observing Y should make Z happen."
- Discard or sharpen any hypothesis that does not make a testable prediction.

If the user is present, show the ranked list before testing. They may have domain knowledge that instantly re-ranks it. If the user is AFK, proceed with your ranking.

### 2. Test Minimally

- Test the highest-ranked hypothesis with the smallest possible probe.
- Change one variable at a time.
- Don't fix multiple things at once.
- Prefer debugger/REPL inspection when available; one breakpoint beats ten logs.
- If you add logs, tag every temporary line with a unique prefix such as `[DEBUG-a4f2]` so cleanup is a single search.

### 3. Verify Before Continuing

- Did it work? → 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 the user 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 `test-driven-development` skill

### 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

```bash
# Run the specific regression test
pytest tests/test_module.py::test_regression -v

# Run full suite — no regressions
pytest tests/ -q
```

### 4. If Fix Doesn't Work — The Rule of Three

- **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 an architectural problem:**
- Each fix reveals new shared state/coupling in a 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 the architecture vs. continue fixing symptoms?

**Discuss with the user 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 a new problem in a different place**

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

**If 3+ fixes failed:** Question the architecture (Phase 4 step 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 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 the pattern, don't fix again. |

## Quick Reference

| Phase | Key Activities | Success Criteria |
|-------|---------------|------------------|
| **1. Root Cause** | Read errors, reproduce, check changes, gather evidence, trace data flow | Understand WHAT and WHY |
| **2. Pattern** | Find working examples, compare, identify differences | Know what's different |
| **3. Hypothesis** | Form theory, test minimally, one variable at a time | Confirmed or new hypothesis |
| **4. Implementation** | Create regression test, fix root cause, verify | Bug resolved, all tests pass |

## Hermes Agent Integration

### Investigation Tools

Use these Hermes tools during Phase 1:

- **`search_files`** — Find error strings, trace function calls, locate patterns
- **`read_file`** — Read source code with line numbers for precise analysis
- **`terminal`** — Run tests, check git history, reproduce bugs
- **`web_search`/`web_extract`** — Research error messages, library docs

### With delegate_task

For complex multi-component debugging, dispatch investigation subagents:

```python
delegate_task(
    goal="Investigate why [specific test/behavior] fails",
    context="""
    Follow systematic-debugging skill:
    1. Read the error message carefully
    2. Reproduce the issue
    3. Trace the data flow to find root cause
    4. Report findings — do NOT fix yet

    Error: [paste full error]
    File: [path to failing code]
    Test command: [exact command]
    """,
    toolsets=['terminal', 'file']
)
```

### With test-driven-development

When fixing bugs:
1. Write a test that reproduces the bug (RED)
2. Debug systematically to find root cause
3. Fix the root cause (GREEN)
4. The test proves the fix and prevents regression

## 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

**No shortcuts. No guessing. Systematic always wins.**