Antigravity Skill Vault is a collection of reusable Agent Skills for Google Antigravity, covering software development, operations, security, and business work. It is for people who want Antigravity agents to follow specialized expertise, personas, and structured workflows. The catalogue skills are entries from this collection.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/rmyndharis/antigravity-skillsnpx agentmods add skills/rmyndharis/antigravity-skills/code-review-ai-ai-reviewWrote 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/rmyndharis/antigravity-skills/code-review-ai-ai-review)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/code-review-ai-ai-review"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/code-review-ai-ai-review/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/rmyndharis/antigravity-skills/code-review-ai-ai-review"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/code-review-ai-ai-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00048 | $0.03685 |
| Opus 5 | $0.00024 | $0.01843 |
| Sonnet 5 | $0.00010 | $0.00737 |
| Haiku 4.5 | $0.00005 | $0.00368 |
Grade A, and why
code-review-ai-ai-review scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(['sonar-scanner', f'-Dsonar.projectKey={self.repo}'], check=True) Copies of this mod
5 near-identical copies found in the catalogue:
- performance-testing-review-ai-review — 98% identical, 6 lines differ
- code-review-ai-ai-review — 97% identical, 3 lines differ
- code-review-ai-ai-review — 94% identical, 6 lines differ
- code-review-ai-ai-review — 94% identical, 11 lines differ
- code-review-ai-ai-review — 83% identical, 49 lines differ
How it starts
The opening of the file, as written. The whole thing — 450 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Powered Code Review Specialist
You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.
Use this skill when
- Working on ai-powered code review specialist tasks or workflows
- Needing guidance, best practices, or checklists for ai-powered code review specialist
Do not use this skill when
- The task is unrelated to ai-powered code review specialist
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
Context
Multi-layered code review workflows integrating with CI/CD pipelines, providing instant feedback on pull requests with human oversight for architectural decisions. Reviews across 30+ languages combine rule-based analysis with AI-assisted contextual understanding.
Requirements
Review: $ARGUMENTS
Perform comprehensive analysis: security, performance, architecture, maintainability, testing, and AI/ML-specific concerns. Generate review comments with line references, code examples, and actionable recommendations.
Automated Code Review Workflow
Initial Triage
- Parse diff to determine modified files and affected components
- Match file types to optimal static analysis tools
- Scale analysis based on PR size (superficial >1000 lines, deep <200 lines)
- Classify change type: feature, bug fix, refactoring, or breaking change
Multi-Tool Static Analysis
Execute in parallel:
- CodeQL: Deep vulnerability analysis (SQL injection, XSS, auth bypasses)
- SonarQube: Code smells, complexity, duplication, maintainability
- Semgrep: Organization-specific rules and security policies
- Snyk/Dependabot: Supply chain security
- GitGuardian/TruffleHog: Secret detection
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 · 450 lines · 48 tokens per session scan A b4f7e8b32353
code-review-ai-ai-review is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,529 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 3,685 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
github
Manages all git operations (commit, push, branch management, PR creation) in a standardized, safe, and consistent way. Automatically runs the code-review skill before any commit. Enforces Conventional Commits standard. Handles the full lifecycle: branch → review → commit → push → PR.
architecture-review
Before committing to an implementation plan, run this skill to stress-test the proposed architecture. Catches over-engineering, circular dependencies, missing failure modes, security gaps, and scalability cliffs — before any code is written. Acts as a "second eye" on the plan.
code-review
Systematic code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.
007
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
address-github-comments
Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
code-reviewer
Professional Code Reviewer Expert skill. Orchestrate technical collaboration, design reviews, sprint planning, and system diagnostics.