debug-mastery

A step-by-step method for investigating software problems and finding their underlying cause before changing code.

In plain words
What is it for?
Use it to investigate test failures, production bugs, unexpected behavior, slow performance, build failures, and integration problems.
Why use it?
It helps avoid quick patches that only hide symptoms or introduce new bugs.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/xenitv1/claude-code-maestro/debug-mastery
Any agent
npx skills add xenitV1/claude-code-maestro --skill debug-mastery
Clone the repo
git clone --depth 1 https://github.com/xenitV1/claude-code-maestro

Made for: Claude Code, Codex.

Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,351 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00029 $0.02351
Opus 5 $0.00015 $0.01175
Sonnet 5 $0.00006 $0.00470
Haiku 4.5 $0.00003 $0.00235

Measured yesterday against content hash b2cd4cb5dce1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

debug-mastery scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/debug-mastery/SKILL.md · 226 lines

How it starts

The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<domain_overview>

🐛 DEBUG MASTERY: SYSTEMATIC DEBUGGING

Philosophy: Random fixes waste time and create new bugs. Quick patches mask underlying issues. ALWAYS find root cause before attempting fixes. FORENSIC ANALYSIS MANDATE (CRITICAL): Never apply a fix without a confirmed root cause. AI-generated fixes often address symptoms rather than underlying architectural logic. You MUST perform a 'Forensic Investigation' that identifies the specific assumption or boundary condition that failed. For every fix, you must provide a brief analysis note explaining WHY the original architecture allowed the bug to exist, transforming every error into a systemic engineering lesson.


🚨 THE IRON LAW

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you haven't completed Phase 1, you cannot propose fixes. Violating the letter of this process is violating the spirit of debugging.

📋 WHEN TO USE

Use for ANY technical issue:

  • Test failures
  • Bugs in production
  • Unexpected behavior
  • Performance problems
  • Build failures
  • Integration issues Use 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 (systematic is faster than thrashing)
  • Manager wants it fixed NOW (systematic is faster than guess-and-check) </domain_overview> <debugging_phases>

🔄 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
    
  5. Trace Data Flow WHEN error is deep in call stack: See @root-cause-tracing.md for the complete backward tracing technique. 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

Read the full file on GitHub · 226 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 226 lines · 29 tokens per session scan A b2cd4cb5dce1

Subscribe to this mod's changes

debug-mastery is a skill published in the GitHub repository xenitV1/claude-code-maestro (230 stars, last pushed 7mo ago), licensed MIT. It adds 29 tokens to every session and 2,351 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens