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-graphnpx skills add vudovn/ag-kit --skill code-review-graphgit clone --depth 1 https://github.com/vudovn/ag-kitWhat 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 | $0.00049 | $0.02567 |
| Opus 5 | $0.00024 | $0.01283 |
| Sonnet 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 3d 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.
- 3d 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 (8,162 stars, last pushed 2d ago), licensed MIT. It adds 49 tokens to every session and 2,567 once invoked, about $0.0002 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.
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