systematic-debugging

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","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: Failing test at the seam that reaches the bug: unit, integration, or end-to-end. HTTP script / curl against a running dev server. CLI invocation with fixture input, diffing stdout/stderr against expected output. Headless browser script (Playwright/Puppeteer) asserting on DOM, console, or network. Replay a captured trace : HAR, request payload, event log, queue message, or webhook body. Throwaway harness that boots the smallest useful slice of the system and calls the failing path. Property / fuzz loop when the bug is intermittent wrong output over a broad input space. Bisection harness suitable for git bisect run when the bug appeared between two known states. Differential loop comparing old vs new version, two configs, two providers, or two datasets. 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: # 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: # 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 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: 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 # 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: 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: Write a test that reproduces the bug (RED) Debug systematically to find root cause Fix the root cause (GREEN) 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.