Buckets:
| 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, writing-plans, 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. | |
| ## 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: | |
| ```bash | |
| # 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:** | |
| ```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 | |
| - [ ] 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: | |
| ```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 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 | |
| - Don't fix multiple things at once | |
| ### 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.** | |
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