AG Kit is a toolkit that gives Google Antigravity coding agents a structured workspace with rules, skills, specialist agents, workflows, persistent memory, orchestration, MCP guidance, and a safety hook. It is for building and operating agent workflows in Antigravity, and its catalogue entries provide parts of that workspace contract.
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/vudovn/ag-kit/code-review-checklistnpx skills add vudovn/ag-kit --skill code-review-checklistgit clone --depth 1 https://github.com/vudovn/ag-kitWrote 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/vudovn/ag-kit/code-review-checklist)<a href="https://agentmods.dev/skills/vudovn/ag-kit/code-review-checklist"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/code-review-checklist.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.00681 |
| Opus 5 | $0.00009 | $0.00341 |
| Sonnet 5 | $0.00004 | $0.00136 |
| Haiku 4.5 | $0.00002 | $0.00068 |
Grade A, and why
code-review-checklist 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 4d 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.
Copies of this mod
7 near-identical copies found in the catalogue:
- code-review-checklist — 89% identical, 4 lines differ
- code-review-checklist — 89% identical, 4 lines differ
- code-review-checklist — 89% identical, 4 lines differ
- code-review-checklist — 89% identical, 4 lines differ
- code-review-checklist — 89% identical, 5 lines differ
- code-review-checklist — 89% identical, 4 lines differ
- code-review-checklist — 89% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Checklist
Quick Review Checklist
Correctness
- Code does what it's supposed to do
- Edge cases handled
- Error handling in place
- No obvious bugs
Security
- Input validated and sanitized
- No SQL/NoSQL injection vulnerabilities
- No XSS or CSRF vulnerabilities
- No hardcoded secrets or sensitive credentials
- AI-Specific: Protection against Prompt Injection (if applicable)
- AI-Specific: Outputs are sanitized before being used in critical sinks
Performance
- No N+1 queries
- No unnecessary loops
- Appropriate caching
- Bundle size impact considered
Code Quality
- Clear naming
- DRY - no duplicate code
- SOLID principles followed
- Appropriate abstraction level
Testing
- Unit tests for new code
- Edge cases tested
- Tests readable and maintainable
Documentation
- Complex logic commented
- Public APIs documented
- README updated if needed
AI & LLM Review Patterns
Logic & Hallucinations
- Chain of Thought: Does the logic follow a verifiable path?
- Edge Cases: Did the AI account for empty states, timeouts, and partial failures?
- External State: Is the code making safe assumptions about file systems or networks?
Prompt Engineering Review
// ❌ Vague prompt in code
const response = await ai.generate(userInput);
// ✅ Structured & Safe prompt
const response = await ai.generate({
system: "You are a specialized parser...",
input: sanitize(userInput),
schema: ResponseSchema
});
Anti-Patterns to Flag
// ❌ Magic numbers
if (status === 3) { ... }
// ✅ Named constants
if (status === Status.ACTIVE) { ... }
// ❌ Deep nesting
if (a) { if (b) { if (c) { ... } } }
// ✅ Early returns
if (!a) return;
if (!b) return;
if (!c) return;
// do work
// ❌ Long functions (100+ lines)
// ✅ Small, focused functions
// ❌ any type
const data: any = ...
// ✅ Proper types
const data: UserData = ...
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.
- 4d ago First seen · 112 lines · 18 tokens per session scan A 07b20413aa0d
code-review-checklist is a skill published in the GitHub repository vudovn/ag-kit (8,168 stars, last pushed 4d ago), licensed MIT. It adds 18 tokens to every session and 681 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.
Other skills, from other repositories
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…