systematic-debugging

name	systematic-debugging
description	4-phase root cause debugging: understand bugs before fixing.
version	1.2.0
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 0: Pre-Implementation Audit

BEFORE any changes, run automated checks to validate the current state. This prevents introducing new bugs and identifies real issues vs. perceived ones.

1. Syntax Validation

Check all modified files for syntax errors before making changes:

# Node.js
node -c server.js

# Python
python3 -m py_compile module.py

# JS/HTML (basic brace check)
python3 -c "c=open('dashboard.html').read(); print('OK' if c.count('{')==c.count('}') else 'MISMATCH')"

2. Variable Scope Analysis

For JavaScript/TypeScript files, check for:

• Undefined variables used in expressions (e.g., perfil && perfil.altura_cm when perfil is never declared in scope)
• const charts vs var charts = window.charts = {} (must use var for Chart.js instances)
• Mismatched function parameter names (e.g., function takes objHidr but caller passes objetivoHidr)
• Missing variable declarations in closures/lambdas
• JavaScript class constructor crash — missing method call: If a constructor calls this.something() but that method is NOT defined in the class, the constructor throws TypeError: this.something is not a function and the entire class instantiation fails silently (no method after the failing call executes). Symptom: app loads HTML but nothing works — no render loop, no event handlers, overlays stuck. Diagnosis: check that every this.method() called in the constructor is actually defined as a class method.
• Global function vs class method mismatch: HTML onclick="foo()" calls a global function foo(), NOT app.foo(). If foo only exists as a class method, the call fails with ReferenceError: foo is not defined. Symptom: button clicks do nothing. Fix: either add a global wrapper (function foo() { if (app) app.foo(); }) or change the onclick to app.foo().

# Check for undefined variable usage in a function scope
import re
with open('file.js', 'r') as f:
    content = f.read()

# Find function scope and check for used-but-undefined vars
# Look for patterns like `var && var.` where `var` is not declared with `var` in scope

3. Field Name Consistency

Check that frontend field names match backend expectations:

# Find all references to a field name
grep -rn "objetivo_peso_kg" dashboard.html server.js
grep -rn "peso_objetivo_kg" dashboard.html server.js
# If frontend uses X and backend expects Y → BUG

4. API Endpoint Verification

Verify all API endpoints called from frontend exist in backend:

# Extract all API calls from frontend
grep -oP "api\(['\"](/api/\S+)" dashboard.html | sort -u
# Compare with backend routes
grep -oP "app\.(get|post|put|delete)\(['\"](/api/\S+)" server.js | sort -u

5. Chart.js Memory Management

Check that charts are properly destroyed before recreation:

# Count chart destroy calls vs. new Chart calls
grep -c "\.destroy()" dashboard.html
grep -c "new Chart(" dashboard.html
# Should be equal or destroy > new (for safety)

6. XSS Risk Assessment

For files using innerHTML, check that user input is escaped:

# Count innerHTML assignments
grep -c "innerHTML =" dashboard.html
# Verify escape functions exist and are used
grep -c "escapeHtml" dashboard.html
grep -c "formatMarkdown" dashboard.html

7. CDN MIME Type Verification

WHEN frontend loads third-party libraries via CDN and they don't work:

Check the actual MIME type served by the CDN — this is the #1 silent failure in vanilla JS projects:

curl -sI "https://cdn.jsdelivr.net/npm/docx@9.7.1/dist/index.umd.cjs" | grep -i content-type
# application/node → BLOCKED by browser

curl -sI "https://unpkg.com/docx@9.7.1/dist/index.umd.cjs" | grep -i content-type
# text/javascript → OK

Common pitfall: jsdelivr serves .cjs files as application/node which modern browsers reject silently. Switch to unpkg which serves text/javascript. The symptom: network shows HTTP 200, but window.libName is undefined with NO console error.

Check list:

• Verify CDN URL returns the expected file (not a 404/redirect)
• Verify Content-Type is text/javascript or application/javascript
• Add defensive check: if (typeof window.libName === 'undefined') { alert('No cargada'); return; }
• Check that SPA fallback (serving index.html for everything) isn't intercepting CDN paths

8. DOM Element Existence Against JS References

WHEN UI elements were recently removed from HTML:

Check that ALL JavaScript references to removed DOM elements are also removed:

# Find all JS references to a removed element
grep -rn "shpModosContainer" js/
grep -rn "btnDescargarSHP" js/
grep -rn "getElementById.*shp" js/

Pattern: Remove <div id="shpModosContainer"> from HTML but JS still references document.getElementById('shpModosContainer') or adds event listeners to it → JS silently fails.

Fix:

1. Remove the dead JS code entirely (preferred)
2. Guard with if (document.getElementById('elID')) { ... } as fallback

Phase 0 Completion Checklist

• Syntax validation passed for all files
• Variable scope checked (no undefined vars in scope)
• Field names match between frontend and backend
• All API endpoints verified
• Chart.js memory management checked
• XSS risk assessed (user input escaped)
• CDN MIME types verified (if third-party libraries used)
• DOM references match HTML elements
• Known bugs documented before fixing

STOP: Do not proceed to Phase 1 until Phase 0 is complete. This phase takes 2-5 minutes but prevents 80% of "fixes that introduce new bugs."

---

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
• 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

# 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, not fix again.

Async-Specific Pitfalls

Async infinite recursion ≠ stack overflow (harder to catch)

Async recursion doesn't stack overflow the same way sync does — the event loop unwinds between awaits. This makes it INVISIBLE in local testing yet deadly in production:

• Symptom: Container restarts silently (OOM kill), "no available server", not reproducible locally
• Root cause: Function A calls A internally with a shifted parameter (e.g., buildSummary(today) → buildSummary(yesterday) → buildSummary(tomorrow)...) without a base case. Each "recursive" call leaks memory (pending HTTP requests, unresolved promises, response buffers) until the process hits the RAM limit.
• Why local testing misses it: Local dev has abundant RAM + open file handles. The recursion eventually hits a date with no API data (returns null → error → terminates). On production with cached data, every date succeeds → infinite chain.
• Fix: Never let function A call function A (directly or indirectly) unless you have a hard depth limit. Replace with targeted single-call fetches.

Detection trick: If you see const data = await buildSummary(ayer, token) inside function buildSummary(...) — that's the bug. The call chain is buildSummary(d) → buildSummary(d-1) → buildSummary(d-2) → ... with no termination.

Event loop starvation por concurrencia HTTP en servidores de 1 vCPU

Cuando un servidor tiene 1 vCPU (NaN free, contenedores pequeños, VPS baratos), múltiples llamadas HTTP simultáneas pueden colapsar el event loop de Node.js: