Borrowing it
Nothing to install: this file belongs to jakubsuplicki/codument. 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/jakubsuplicki/codument/main/.agents/skills/review-codebase/SKILL.mdgit clone --depth 1 https://github.com/jakubsuplicki/codumentWrote 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/jakubsuplicki/codument/review-codebase)<a href="https://agentmods.dev/skills/jakubsuplicki/codument/review-codebase"><img src="https://agentmods.dev/badge/skills/jakubsuplicki/codument/review-codebase/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/jakubsuplicki/codument/review-codebase"><img src="https://agentmods.dev/badge/skills/jakubsuplicki/codument/review-codebase.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 | $0.00093 | $0.01078 |
| Opus 5.5 | $0.00037 | $0.00431 |
| Sonnet 5.5 | $0.00019 | $0.00216 |
| Haiku 4.5 | $0.00009 | $0.00108 |
Grade A, and why
review-codebase 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review-codebase — 86% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Review
You are the orchestrator for a full codebase review. Your job is to divide the codebase into reviewable chunks (using the documentation registry's feature boundaries), delegate each chunk to a code-reviewer agent, and compile the results into a unified report.
How It Works
Step 1: Read the registry
Read docs/.registry.json to get the feature map. Each registry entry defines a feature with its source files — these are your review units.
If no registry exists, tell the user to run codument scan first.
Step 2: Determine review scope
By default, review all features. The user may narrow scope:
- Specific features: "review auth and payments"
- By status: "review stale features only"
- By directory: "review everything under src/api/"
- By severity filter: "only critical and high issues"
If the user doesn't specify, review everything.
Step 3: Spawn code-reviewer agents
For each feature in scope, spawn a code-reviewer agent with specific instructions:
Review the "{feature-name}" feature.
Doc: {doc path from registry}
Source files to review:
{list each file from the registry entry's sources array}
Focus areas: correctness, security, performance, error handling, type safety.
Report findings by severity: Critical, High, Medium, Low.
Include a Positives section for what's done well.
{any user-specified focus or constraints}
Batching:
- Small projects (< 6 features): spawn all agents in parallel
- Larger projects: batch 3-5 agents at a time to avoid overwhelming the system
Important: Be specific in agent instructions. Include the exact file paths. Vague instructions produce vague reviews.
Step 4: Compile the report
After all agents complete, compile findings into a unified report:
# Codebase Review Report
**Date**: YYYY-MM-DD
**Features reviewed**: N
**Total findings**: N (X critical, Y high, Z medium, W low)
## Critical Issues
[all critical findings across features, grouped by feature]
## High Priority
[all high findings across features, grouped by feature]
## Medium Priority
[all medium findings, grouped by feature]
## Low Priority
[summary count only — detail available per-feature]
## Highlights
[notable positives across the codebase]
## Recommended Priorities
[ordered list: what to fix first based on severity and blast radius]
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 · 126 lines · 93 tokens per session scan A eb23604440b8
review-codebase is a skill published in the GitHub repository jakubsuplicki/codument (48 stars, last pushed 23d ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,078 once invoked, about $0.0004 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
review
Adversarial review of an increment before it ships, by a fresh context or another model family; every finding cites path:line and is re-verified. Use for "review", "qa", "critique this".
sw-review
Adversarial review of an increment before it ships, by a fresh context or another model family; every finding cites path:line and is re-verified. Use for "review", "qa", "critique this".
kn-review
Use when reviewing implemented code before committing — multi-perspective review with severity-based findings.
kn-doc
Use when working with Knowns documentation - viewing, searching, creating, or updating docs.
receiving-feedback
Use when receiving code review feedback or corrections - before implementing suggestions. Requires technical verification and reasoned evaluation, not blind agreement or performative responses.
atlas-review
Review code changes and capture learnings for CLAUDE.md by following the canonical review command doc.