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.
npx agentmods add skills/xenitv1/claude-code-maestro/debug-masterynpx skills add xenitV1/claude-code-maestro --skill debug-masterygit clone --depth 1 https://github.com/xenitV1/claude-code-maestroWhat 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.
| Model | Per session | Once 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 |
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.
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:
- 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
- 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
- Check Recent Changes
- What changed that could cause this?
- Git diff, recent commits
- New dependencies, config changes
- Environmental differences
- 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 - Trace Data Flow
WHEN error is deep in call stack:
See
@root-cause-tracing.mdfor 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
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.
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.
- yesterday First seen · 226 lines · 29 tokens per session scan A b2cd4cb5dce1
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.
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