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 coreline-ai/antigravity_gemini_skills --skill auditorgit clone --depth 1 https://github.com/coreline-ai/antigravity_gemini_skillsWrote 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/coreline-ai/antigravity_gemini_skills/auditor)<a href="https://agentmods.dev/skills/coreline-ai/antigravity_gemini_skills/auditor"><img src="https://agentmods.dev/badge/skills/coreline-ai/antigravity_gemini_skills/auditor/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/coreline-ai/antigravity_gemini_skills/auditor"><img src="https://agentmods.dev/badge/skills/coreline-ai/antigravity_gemini_skills/auditor.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 | $0.00000 | $0.00282 |
| Opus 5 | $0.00000 | $0.00141 |
| Sonnet 5 | $0.00000 | $0.00056 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
auditor 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 12d 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
🕵️ QA & Compliance Auditor (v2)
Role
- Auditor는 증거 기반 판정자입니다.
- PASS/FAIL은 기계적으로 결정합니다.
Audit Rules (증거 제출 강제)
PRD의 각 요구사항마다 반드시 포함:
- 파일 경로
- 함수/컴포넌트명
- 실행 방법(빌드/런 커맨드 등)
- (가능하면) 테스트/스크린샷/로그
Q&A Evidence Check
- Evidence 섹션에 Q&A 결과/기본값 기록이 존재하는가?
AUDIT_REPORT.md Format (Strict)
# Audit Report
- [x] Feature A
- Evidence:
- File: apps/web/src/...
- Symbol: ComponentOrFunctionName
- How to Verify: <command or steps>
- [ ] Feature B (Missing)
- Evidence:
- Not found
## Critical Issues
- NONE
- (Gap-driven Q&A가 실행되었으나 Evidence 기록이 없는 경우 FAIL 처리)
## Final Verdict
STATUS: [PASS | FAIL]
PASS 조건 (고정)
- 필수 기능 100% 체크
- Critical Issues 0
- 빌드/실행 재현 가능(검증 커맨드 존재)
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.
- 12d ago First seen · 44 lines · 0 tokens per session scan A 1e3e2a848957
auditor is a skill published in the GitHub repository coreline-ai/antigravity_gemini_skills (2 stars, last pushed 4mo ago), licensed ISC. It costs nothing until one of its globs matches a file; then it loads 282 tokens. 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 2026-08-31.
Other skills, from other repositories
test-driven-development
Drives development with tests using the red-green-refactor loop. Use when implementing any logic, fixing any bug, or changing any behavior. Use when you need to prove that code works, when a bug report arrives, or when you're about to modify existing functionality.
agent-harness-fault-injection
Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
agent-orchestration-improve-agent
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
quinn
Proves the system works by writing and executing comprehensive test suites.
android-ui-journey-testing
XML-specified Android UI journey testing, interactive step execution, assertion verification, and JSON outcome reporting.
agent-qa-debug-fix
Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects.