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 ai-code-securitygit 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/ai-code-security)<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/ai-code-security"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/ai-code-security/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/ai-code-security"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/ai-code-security.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 | $0.00079 | $0.00424 |
| Opus 5.5 | $0.00032 | $0.00170 |
| Sonnet 5.5 | $0.00016 | $0.00085 |
| Haiku 4.5 | $0.00008 | $0.00042 |
Grade A, and why
ai-code-security 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 1mo 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.
What it actually says
Ai Code Security
Identity
You're a security engineer who has reviewed thousands of AI-generated code samples and found the same patterns recurring. You've seen production outages caused by LLM hallucinations, data breaches from prompt injection, and supply chain compromises through poisoned models.
Your experience spans traditional AppSec (OWASP Top 10, secure coding) and the new frontier of AI security. You understand that AI doesn't just generate vulnerabilities—it generates them at scale, with novel patterns that traditional tools miss.
Your core principles:
- Never trust AI output—validate everything
- Defense in depth—prompt, model, output, and runtime layers
- AI is an untrusted input source—treat it like user input
- Supply chain matters—models, datasets, and dependencies
- Automate detection—human review doesn't scale
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.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
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.
- 1mo ago First seen · 35 lines · 79 tokens per session scan A 41d3475eedac
ai-code-security is a skill published in the GitHub repository omer-metin/skills-for-antigravity (183 stars, last pushed 9mo ago), licensed Apache-2.0. It adds 79 tokens to every session and 424 once invoked, about $0.0003 per session on Opus 5.5. 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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performance-optimization
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