root-cause-debugging

name	root-cause-debugging
description	6-phase root cause debugging: feedback loop, reproduce, pattern, hypothesise + instrument, fix, cleanup. Understand bugs before fixing.
version	2.0.0
author	Hermes Cortex
license	MIT
platforms	["linux","macos","windows"]
metadata	{"hermes":{"tags":["debugging","troubleshooting","problem-solving","root-cause","investigation"],"related_skills":["change-test-loop","plan","subagent-driven-development","codebase-design"]}}

Root Cause Debugging

Overview

Random fixes waste time and create new bugs. Quick patches mask underlying issues.

Core principle: ALWAYS build a tight feedback loop before theorizing. 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 FEEDBACK LOOP FIRST

If you haven't completed Phase 0 (built a tight pass/fail signal for the bug), 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 Six Phases

You MUST complete each phase before proceeding to the next.

---

Phase 0: Build a Feedback Loop

This is the skill. Everything else is mechanical.

If you have a tight pass/fail signal for the bug — one that goes red on this bug — you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.

Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.

Ways to construct one — try in roughly this order

1. Failing test at whatever seam reaches the bug — unit, integration, e2e.
2. Curl / HTTP script against a running dev server.
3. CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
4. Headless browser script (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
5. Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
6. Throwaway harness. Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
7. Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
8. Bisection harness. If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can git bisect run it.
9. Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
10. HITL bash script. Last resort. If a human must click, drive them with a structured loop script so the loop is still repeatable. Captured output feeds back to you.

Build the right feedback loop, and the bug is 90% fixed.

Tighten the loop

Treat the loop as a product. Once you have a loop, tighten it:

• Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
• Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
• Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)

A 30-second flaky loop is barely better than no loop; a 2-second deterministic one is a debugging superpower.

Non-deterministic bugs

The goal is not a clean repro but a higher reproduction rate. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not — keep raising the rate until it's debuggable.

When you genuinely cannot build a loop

Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do not proceed to hypothesise without a loop.

Phase 0 Completion Checklist

• A tight, deterministic pass/fail signal exists
• The signal goes red on this specific bug (proven by running it)
• The loop is fast enough to iterate on (seconds, not minutes)
• It can be run unattended (or HITL script exists for manual steps)

STOP: Do not proceed to Phase 1 without a tight feedback loop.

---

Phase 1: Reproduce + Minimise

Before anything else: confirm the bug.

Run the loop from Phase 0. Watch it go red.

Confirm:

• The loop produces the failure mode the user described — not a different failure that happens to be nearby. Wrong bug = wrong fix.
• The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
• You have captured the exact symptom (error message, wrong output, timing) so later phases can verify the fix actually addresses it.

Minimise

Once it's 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's load-bearing for the failure.

Why bother: a minimal repro shrinks the hypothesis space (fewer moving parts left to suspect) and becomes the clean regression test in Phase 4.

Done when every remaining element is load-bearing — removing any one of them makes the loop go green.

Evidence Gathering

Now that you have a minimised repro, gather evidence systematically:

1. Read Error Messages Carefully — stack traces, line numbers, error codes
2. Check Recent Changes — git log --oneline -10, git diff
3. Trace Data Flow — where does the bad value originate? Trace upstream to find the source

Action: Use read_file on relevant source files. 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(r"variable_name\s*=", path="src/", file_glob="*.py")

4. Multi-Component Systems — for each component boundary, log what enters and exits. Find WHERE it breaks before investigating the specific component.

Phase 1 Completion Checklist

• Bug reproduced and confirmed as the correct bug
• Repro minimised to smallest load-bearing scenario
• Error messages fully read and understood
• Recent changes identified and reviewed
• Evidence gathered (logs, state, data flow)
• Problem isolated to specific component/code

---

Phase 2: Pattern Analysis

Find the pattern before hypothesising:

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 2 Completion

• Working examples found and compared
• Differences identified between working and broken

---

Phase 3: Hypothesise + Instrument

Generate 3-5 Ranked Hypotheses

Generate 3-5 ranked hypotheses before testing any of them. Single-hypothesis generation anchors on the first plausible idea.

Each hypothesis must be falsifiable: state the prediction it makes.

Format: "If is the cause, then will make the bug disappear / will make it worse."

If you cannot state the prediction, the hypothesis is a vibe — discard or sharpen it.

Show the ranked list to the user before testing. They often have domain knowledge that re-ranks instantly ("we just deployed a change to #3"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it — proceed with your ranking if the user is AFK.

Instrument — One Variable at a Time

Each probe must map to a specific prediction from Phase 3. Change one variable at a time.

Tool preference:

1. Debugger / REPL inspection if the env supports it. One breakpoint beats ten logs.
2. Targeted logs at the boundaries that distinguish hypotheses.
3. Never "log everything and grep".

Tag every debug log with a unique prefix, e.g. [DEBUG-a4f2]. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.

Perf branch. For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, performance.now(), profiler, query plan), then bisect. Measure first, fix second.

When You Don't Know

• Say "I don't understand X"
• Don't pretend to know
• Ask the user for help
• Research more

Phase 3 Completion Checklist

• 3-5 ranked hypotheses generated (shown to user if present)
• Each hypothesis is falsifiable with a prediction
• Instrumentation probes one variable at a time
• Debug logs tagged with unique prefix
• Perf bugs: baseline measurement established before fix

---

Phase 4: Fix + Regression Test

Fix the root cause, not the symptom:

1. Check for a Correct Test Seam

Write the regression test before the fix — but only if there is a correct seam for it.

A correct seam is one where the test exercises the real bug pattern as it occurs at the call site. If the only available seam is too shallow (unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.

If no correct seam exists, that itself is the finding. Note it. The codebase architecture is preventing the bug from being locked down. This is a candidate for the codebase-design skill — the module needs deepening to create a testable seam.

2. If a Correct Seam Exists

1. Turn the minimised repro into a failing test at that seam (RED)
2. Watch it fail for the correct reason
3. Apply the fix — ONE change at a time
4. Watch it pass (GREEN)
5. Re-run the Phase 0 feedback loop against the original (un-minimised) scenario

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

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

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

• STOP.
• Count: How many fixes have you tried?
• If < 3: Return to Phase 1, re-analyse with new information
• If ≥ 3: STOP and question the architecture (step 4 below)
• DON'T attempt Fix #4 without architectural discussion

4. 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. Discuss with the user before attempting more fixes. This is NOT a failed hypothesis — this is a wrong architecture. Use the codebase-design skill to evaluate module depth, seam placement, and adapter strategy.

Phase 4 Completion Checklist

• Correct seam identified (or absence documented)
• Regression test passes at the seam
• Original Phase 0 loop goes green
• Full test suite passes
• No regressions introduced

---

Phase 5: Cleanup + Post-mortem

Required before declaring done:

• Original repro no longer reproduces (re-run the Phase 0 loop)
• Regression test passes (or absence of seam is documented)
• All [DEBUG-...] instrumentation removed (grep the prefix)
• Throwaway prototypes deleted (or moved to a clearly-marked debug location)
• The hypothesis that turned out correct is stated in the commit / PR message — so the next debugger learns

Then ask: what would have prevented this bug? If the answer involves architectural change (no good test seam, tangled callers, hidden coupling), hand off to the codebase-design skill with the specifics. Make the recommendation after the fix is in, not before — you have more information now than when you started.

---

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 a tight feedback loop exists
• "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 0.

If 3+ fixes failed: Question the architecture (Phase 4 step 4).

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"	Without a feedback loop, you can't tell if the fix worked. Build the loop first.
"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
0. Feedback Loop	Build tight pass/fail signal; 10 construction methods; tighten loop	One command that goes red on this bug
1. Reproduce + Minimise	Run the loop, confirm correct bug, minimise, gather evidence	Minimal repro; bug understood
2. Pattern Analysis	Find working examples, compare, identify differences	Know what's different
3. Hypothesise + Instrument	3-5 ranked hypotheses, tagged debug logs, one variable at a time	Confirmed hypothesis; root cause identified
4. Fix + Regression Test	Check seam, RED-GREEN, verify original loop goes green	Bug resolved, all tests pass
5. Cleanup + Post-mortem	Remove instrumentation, document hypothesis, ask "what would prevent this?"	Clean commit; architecture note if applicable

Hermes Agent Integration

Investigation Tools

Use these Hermes tools during debugging:

• 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

With delegate_task

For complex multi-component debugging, dispatch investigation subagents:

delegate_task(
    goal="Investigate why [specific test/behavior] fails",
    context="""
    Follow root-cause-debugging skill:
    0. Build a tight feedback loop first
    1. Reproduce and minimise
    2. Find pattern differences
    3. Generate ranked hypotheses
    4. Report findings — do NOT fix yet

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

With change-test-loop

When fixing bugs:

1. Phase 0 → build the feedback loop that proves the bug
2. Write a test that reproduces the bug (RED)
3. Debug systematically to find root cause
4. Fix the root cause (GREEN)
5. The test proves the fix and prevents regression

With codebase-design

When a bug resists fixing because no good test seam exists, use the codebase-design skill:

• Evaluate whether the module is deep or shallow
• Identify whether a seam can be introduced
• Design the deepened module before attempting the fix

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.