Agent skill

Systematic Debugging

by heyitsnoah in heyitsnoah/claudesidian

ALWAYS use before attempting any fix. An agent skill from heyitsnoah/claudesidian.

MITAuto-check passedDevelopment

Install Systematic Debugging

skills CLI
$ npx skills add heyitsnoah/claudesidian --skill systematic-debugging -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install heyitsnoah/claudesidian systematic-debugging --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/heyitsnoah/claudesidian.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/systematic-debugging .claude/skills/systematic-debugging && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
systematic-debugging
GitHub stars
2.6k
Token cost
~3.3k tokens
SKILL.md length
1,286 words
Files
1
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

ALWAYS use before attempting any fix. An agent skill from heyitsnoah/claudesidian.

  • Works in 4 steps: Root Cause Investigation → Pattern Analysis → Hypothesis and Testing → …
  • Encountering any technical issue
  • SKILL.md covers Overview, The Iron Law, When to Use and The Four Phases, plus 6 more sections
  • Calls npm

What it does

Systematic Debugging is an agent skill from heyitsnoah/claudesidian. ALWAYS use before attempting any fix. Never jump to solutions - investigate root cause first. Use when encountering any technical issue, bug, test failure, or unexpected behavior.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Debugging, Root cause analysis and Failing and flaky tests. The licence is MIT.

When your agent uses it

  • Encountering any technical issue
  • Unexpected behavior

Example prompts

  • “Use the systematic-debugging skill to alway use before attempting any fix. An agent skill from heyitsnoah/claudesidian”
  • “/systematic-debugging”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Root Cause Investigation
  2. Pattern Analysis
  3. Hypothesis and Testing
  4. Implementation

What it can do on your machine

Read from SKILL.md and the folder at commit 6c56f35. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Systematic Debugging loads about 3.3k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 1,286 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from heyitsnoah/claudesidian at commit 6c56f35, republished under its MIT licence (© heyitsnoah). 1,286 words, ~3,291 tokens.

Download SKILL.mdSave it as .claude/skills/systematic-debugging/SKILL.md (or your agent's skills folder).
name
systematic-debugging
description
ALWAYS use before attempting any fix. Never jump to solutions - investigate root cause first. Use when encountering any technical issue, bug, test failure, or unexpected behavior.

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)
  • Manager 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
  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
  3. Check Recent Changes

    • What changed that could cause this?
    • Git diff, recent commits
    • New dependencies, config changes
    • Environmental differences
  4. Gather Evidence in Multi-Component Systems

    WHEN system has multiple components (CI → build → signing, API → service → database):

    BEFORE proposing fixes, add diagnostic instrumentation:

    For EACH component boundary:
      - Log what data enters component
      - Log what data exits component
      - Verify environment/config propagation
      - Check state at each layer
    
    Run once to gather evidence showing WHERE it breaks
    THEN analyze evidence to identify failing component
    THEN investigate that specific component

    Example (multi-layer system):

    bash
    # Layer 1: Workflow
    echo "=== Secrets available in workflow: ==="
    echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"
    
    # Layer 2: Build script
    echo "=== Env vars in build script: ==="
    env | grep IDENTITY || echo "IDENTITY not in environment"
    
    # Layer 3: Signing script
    echo "=== Keychain state: ==="
    security list-keychains
    security find-identity -v
    
    # Layer 4: Actual signing
    codesign --sign "$IDENTITY" --verbose=4 "$APP"

    This reveals: Which layer fails (secrets → workflow ✓, workflow → build ✗)

  5. Trace Data Flow

    WHEN error is deep in call stack:

    Quick version:

    • Where does bad value originate?
    • What called this with bad value?
    • Keep tracing up until you find the source
    • Fix at source, not at symptom
Phase 2: Pattern Analysis

Find the pattern before fixing:

  1. Find Working Examples

    • Locate similar working code in same codebase
    • What works that's similar to what's broken?
  2. Compare Against References

    • If implementing pattern, read 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 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 hypothesis
    • One variable at a time
    • Don't fix multiple things at once
  3. Verify Before Continuing

    • Did it work? Yes → 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 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
    • One-off test script if no framework
    • MUST have before fixing
  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

    • Test passes now?
    • No other tests broken?
    • Issue actually resolved?
  4. If Fix Doesn't Work

    • 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 architectural problem:

    • Each fix reveals new shared state/coupling/problem in 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 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 new problem in different place

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

If 3+ fixes failed: Question the architecture (see Phase 4.5)

Show full SKILL.md (548 more words)Show less

Common Rationalizations

ExcuseReality
"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 pattern, don't fix again.

Quick Reference

PhaseKey ActivitiesSuccess Criteria
1. Root CauseRead errors, reproduce, check changes, gather evidenceUnderstand WHAT and WHY
2. PatternFind working examples, compareIdentify differences
3. HypothesisForm theory, test minimallyConfirmed or new hypothesis
4. ImplementationCreate test, fix, verifyBug resolved, tests pass

Technique: Root Cause Tracing

When bugs manifest deep in the call stack, trace backward to find the original trigger.

The Tracing Process
  1. Observe the Symptom

    Error: git init failed in /Users/jesse/project/packages/core
  2. Find Immediate Cause - What code directly causes this?

    typescript
    await execFileAsync('git', ['init'], { cwd: projectDir })
  3. Ask: What Called This?

    typescript
    WorktreeManager.createSessionWorktree(projectDir, sessionId)
      → called by Session.initializeWorkspace()
      → called by Session.create()
      → called by test at Project.create()
  4. Keep Tracing Up - What value was passed?

    • projectDir = '' (empty string!)
    • Empty string as cwd resolves to process.cwd()
  5. Find Original Trigger - Where did empty string come from?

    typescript
    const context = setupCoreTest() // Returns { tempDir: '' }
    Project.create('name', context.tempDir) // Accessed before beforeEach!
Adding Stack Traces

When you can't trace manually, add instrumentation:

typescript
async function gitInit(directory: string) {
  const stack = new Error().stack
  console.error('DEBUG git init:', {
    directory,
    cwd: process.cwd(),
    nodeEnv: process.env.NODE_ENV,
    stack,
  })
  await execFileAsync('git', ['init'], { cwd: directory })
}

Tips:

  • Use console.error() in tests (logger may be suppressed)
  • Log before the dangerous operation, not after it fails
  • Include context: directory, cwd, environment variables
  • new Error().stack shows complete call chain
Finding Which Test Causes Pollution

If something appears during tests but you don't know which test, use bisection:

bash
# Run tests one-by-one, stop at first polluter
for f in src/**/*.test.ts; do
  npm test "$f" && [ -d .git ] && echo "POLLUTER: $f" && break
done

NEVER fix just where the error appears. Trace back to find the original trigger.

Technique: Defense-in-Depth Validation

After finding root cause, validate at EVERY layer data passes through. Make the bug structurally impossible.

Why Multiple Layers
  • Single validation: "We fixed the bug"
  • Multiple layers: "We made the bug impossible"

Different layers catch different cases:

  • Entry validation catches most bugs
  • Business logic catches edge cases
  • Environment guards prevent context-specific dangers
  • Debug logging helps when other layers fail
The Four Layers

Layer 1: Entry Point Validation - Reject invalid input at API boundary

typescript
function createProject(name: string, workingDirectory: string) {
  if (!workingDirectory || workingDirectory.trim() === '') {
    throw new Error('workingDirectory cannot be empty')
  }
  if (!existsSync(workingDirectory)) {
    throw new Error(`workingDirectory does not exist: ${workingDirectory}`)
  }
}

Layer 2: Business Logic Validation - Ensure data makes sense for operation

typescript
function initializeWorkspace(projectDir: string, sessionId: string) {
  if (!projectDir) {
    throw new Error('projectDir required for workspace initialization')
  }
}

Layer 3: Environment Guards - Prevent dangerous operations in specific contexts

typescript
async function gitInit(directory: string) {
  if (process.env.NODE_ENV === 'test') {
    const normalized = normalize(resolve(directory))
    const tmpDir = normalize(resolve(tmpdir()))
    if (!normalized.startsWith(tmpDir)) {
      throw new Error(`Refusing git init outside temp dir during tests`)
    }
  }
}

Layer 4: Debug Instrumentation - Capture context for forensics

typescript
async function gitInit(directory: string) {
  logger.debug('About to git init', {
    directory,
    cwd: process.cwd(),
    stack: new Error().stack,
  })
}
Applying Defense-in-Depth

When you find a bug:

  1. Trace the data flow - Where does bad value originate? Where used?
  2. Map all checkpoints - List every point data passes through
  3. Add validation at each layer - Entry, business, environment, debug
  4. Test each layer - Try to bypass layer 1, verify layer 2 catches it

Don't stop at one validation point. Add checks at every layer.

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

© heyitsnoah, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/systematic-debugging of heyitsnoah/claudesidian.

Open the folder on GitHubat commit 6c56f35

Compare with similar skills

Systematic Debugging next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Systematic Debugging compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Systematic Debugging this skillheyitsnoah/claudesidian2.6k—~3.3kAutomated safety check: PassMIT
Systematic DebuggingChrisWiles/claude-code-showcase6.1k3 repos~1.2kAutomated safety check: PassNone
Debugging and Error Recoveryaddyosmani/agent-skills103k1 repos~2.6kAutomated safety check: PassMIT
Systematic Debugginged3dai/ed3d-plugins2503 repos~2.4kAutomated safety check: PassNone
Debugging And Error Recoveryabashev/vfs-s31066 repos~2.6kAutomated safety check: PassApache-2.0
Veomni DebugByteDance-Seed/VeOmni2.2k—~2.8kAutomated safety check: PassApache-2.0

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Categories

Questions about Systematic Debugging

What does Systematic Debugging do?

ALWAYS use before attempting any fix. An agent skill from heyitsnoah/claudesidian. Systematic Debugging is an agent skill from heyitsnoah/claudesidian. ALWAYS use before attempting any fix.

When should I use Systematic Debugging?

Systematic Debugging fits situations like: encountering any technical issue; unexpected behavior.

How do I install Systematic Debugging in Claude Code?

Run `npx skills add heyitsnoah/claudesidian --skill systematic-debugging -a claude-code`. Or copy the skill folder (.agents/skills/systematic-debugging in heyitsnoah/claudesidian) into .claude/skills/systematic-debugging in your project. Claude Code loads it when a task matches its description.

How do I install Systematic Debugging in Codex?

Run `npx skills add heyitsnoah/claudesidian --skill systematic-debugging -a codex`. Or copy the skill folder (.agents/skills/systematic-debugging in heyitsnoah/claudesidian) into .agents/skills/systematic-debugging in your project. Codex loads it when a task matches its description.

Can I use Systematic Debugging in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add heyitsnoah/claudesidian --skill systematic-debugging -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/systematic-debugging, .gemini/skills/systematic-debugging, .github/skills/systematic-debugging and .opencode/skills/systematic-debugging in your project.

What does Systematic Debugging need to run?

Going by SKILL.md and its folder, Systematic Debugging needs the command-line tools its instructions call (npm).

Does Systematic Debugging access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Systematic Debugging safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Systematic Debugging use?

Systematic Debugging is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Systematic Debugging use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Systematic Debugging?

Skills that share tags, products or a category with Systematic Debugging: Systematic Debugging (ChrisWiles/claude-code-showcase, 6.1k stars), Debugging and Error Recovery (addyosmani/agent-skills, 103k stars), Systematic Debugging (ed3dai/ed3d-plugins, 250 stars) and Debugging And Error Recovery (abashev/vfs-s3, 106 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Systematic Debugging?

heyitsnoah (a GitHub user) maintains it in heyitsnoah/claudesidian, which has 2,597 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on April 11, 2026.

Source: heyitsnoah/claudesidian on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.