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.
Borrowing it
Nothing to install: this file belongs to vudovn/ag-kit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vudovn/ag-kit/main/.agents/skills/code-review-graph/SKILL.mdgit 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-graph)<a href="https://agentmods.dev/skills/vudovn/ag-kit/code-review-graph"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/code-review-graph/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/vudovn/ag-kit/code-review-graph"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/code-review-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 34 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium MCP Rug Pull · line 94 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
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 | $0.00049 | $0.02567 |
| Opus 5.5 | $0.00020 | $0.01027 |
| Sonnet 5.5 | $0.00010 | $0.00513 |
| Haiku 4.5 | $0.00005 | $0.00257 |
Grade A, and why
code-review-graph 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.
How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Graph — Token-Efficient Codebase Context via MCP
Cut AI token usage substantially on large codebases by giving the AI a structural map instead of letting it read everything. Savings scale with codebase size — marginal on small projects, large on monorepos.
Overview
code-review-graph is an MCP server that uses Tree-sitter to parse your codebase into an AST graph stored in SQLite. When your AI assistant needs context for a task, it queries the graph first — getting only the files in the blast radius of your change — instead of reading every file in the directory.
Token Impact (illustrative — varies by codebase):
| Codebase Type | Pattern |
|---|---|
| Large monorepo (10K+ files) | Biggest savings — graph reads a small fraction of files |
| Mid-size app (1-5K files) | Meaningful reduction on multi-file changes |
| Small project (<200 files) | Little benefit — graph overhead can exceed savings |
Quality angle: scoping the AI to the blast radius reduces noise, which tends to improve review focus. Measure on your own repo rather than relying on a fixed multiplier.
Bootstrap Protocol (opt-in)
When invoked during /plan or standard usage on a mid-to-large project, check whether graph analysis is available before relying on it:
- Step 1: Check if the tool is installed:
Get-Command code-review-graph(Windows) orwhich code-review-graph(macOS/Linux). - Step 2: Check if a
.code-review-graph/directory exists in the workspace. - Step 3: If installed but the index is missing, ask the user before running
code-review-graph build(it scans the whole project). - Step 4: If not installed and the project is large, ask the user: "Would you like to
pip install code-review-graphand build a local map to cut token usage for this project?" Never install or run build without confirmation.
When to Use vs When to Skip
✅ Install it if:
- Codebase is 500+ files
- You make multi-file changes with cross-module dependencies
- You spend $20+/month on AI assistant tokens
- You work with monorepos, microservices, or cross-package TypeScript
- You want better review quality in addition to cost savings
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 · 307 lines · 49 tokens per session scan A 9e59161bbd8b
code-review-graph is a skill published in the GitHub repository vudovn/ag-kit (8,199 stars, last pushed 3d ago), licensed MIT. It adds 49 tokens to every session and 2,567 once invoked, about $0.0002 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.
Other skills, from other repositories
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…