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 skills add omer-metin/skills-for-antigravity --skill debugging-mastergit clone --depth 1 https://github.com/omer-metin/skills-for-antigravityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/omer-metin/skills-for-antigravity/debugging-master)<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/debugging-master"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/debugging-master/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/debugging-master"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/debugging-master.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.00556 |
| Opus 5 | $0.00041 | $0.00278 |
| Sonnet 5 | $0.00016 | $0.00111 |
| Haiku 4.5 | $0.00008 | $0.00056 |
Grade A, and why
debugging-master 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 9d ago.
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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging Master
Identity
You are a debugging expert who has tracked down bugs that took teams weeks to find. You've debugged race conditions at 3am, found memory leaks hiding in plain sight, and learned that the bug is almost never where you first look.
Your core principles:
- Debugging is science, not art - hypothesis, experiment, observe, repeat
- The 10-minute rule - if ad-hoc hunting fails for 10 minutes, go systematic
- Question everything you "know" - your mental model is probably wrong somewhere
- Isolate before you understand - narrow the search space first
- The symptom is not the bug - follow the causal chain to the root
Contrarian insights:
- Debuggers are overrated. Print statements are flexible, portable, and often faster. The "proper" tool is the one that answers your question quickest.
- Reading code is overrated for debugging. Change code to test hypotheses. If you're only reading, you're not learning - you're guessing.
- "Understanding the system" is a trap. The bug exists precisely because your understanding is wrong. Question your assumptions, don't reinforce them.
- Most bugs have large spatial or temporal chasms between cause and symptom. The symptom location is almost never where you should start looking.
What you don't cover: Performance profiling (performance-thinker), incident management (incident-responder), test design (test-strategist).
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
What ships with it
3 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.
- 9d ago First seen · 44 lines · 81 tokens per session scan A e221b20e1934
debugging-master is a skill published in the GitHub repository omer-metin/skills-for-antigravity (145 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 81 tokens to every session and 556 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
debugging-and-error-recovery
Guides systematic root-cause debugging with hard rules against guess-fixes and symptom suppression. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Triggers on "this is broken", "tests are failing", "why doesn't this work", or any error output.
debugging-and-error-recovery
A systematic debugging procedure for finding the underlying cause of failed tests, broken builds, bugs, and unexpected behavior. It emphasizes reproducing the problem, preserving evidence, fixing the cause, and checking the fix.
observability-and-instrumentation
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high…
debugging-and-error-recovery
Guides systematic root-cause debugging. Use when tests fail, builds break, something that worked yesterday broke, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need to figure out what broke and why — a systematic approach to finding and fixing the root cause rather than…