awesome-agv: Skill for Claude Code

.agents/skills/code-review/SKILL.md

code-review is a skill for Claude Code, Codex from irahardianto/awesome-agv. It costs 44 tokens per session (1,812 once invoked), scanned A, original, MIT.

A structured code-review process for checking code against project rules and common quality risks. It can review a feature, changed files in a pull request, or an entire codebase.

In plain words
What is it for?
Use it when reviewing new code, auditing a pull request, or checking a larger codebase for rule violations and design problems.
Why use it?
It catches problems that basic automatic checks may miss, including security issues, data loss risks, resource leaks, missing monitoring, and incorrect business logic.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; installed under .agents/ (shared by several agents).

This is irahardianto/awesome-agv's own configuration. It tells Claude Code and Codex how to work on awesome-agv itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything awesome-agv configures →

Reuse

Borrowing it

Nothing to install: this file belongs to irahardianto/awesome-agv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/irahardianto/awesome-agv/main/.agents/skills/code-review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/irahardianto/awesome-agv

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for code-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/irahardianto/awesome-agv/code-review/github.svg)](https://agentmods.dev/skills/irahardianto/awesome-agv/code-review)
Your own site
<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/code-review"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/code-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.

agentmods 80×15 button for code-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/code-review"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,812 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.01812
Opus 5 $0.00022 $0.00906
Sonnet 5 $0.00009 $0.00362
Haiku 4.5 $0.00004 $0.00181

Measured 9d ago against content hash 149cbe9486bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

code-review 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 9d 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.

.agents/skills/code-review/SKILL.md · 178 lines

How it starts

The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Review Skill

Purpose

Systematically review code against the full antigravity rule set. Catches issues that linters miss: architectural violations, missing observability, business logic errors, pattern inconsistencies.

When to Invoke

  • During the /audit workflow (as part of parallel subagent dispatch)
  • When user asks for a code review outside any workflow
  • Best practice: Invoke in a fresh conversation (not the same one that authored the code) to avoid confirmation bias

Review Process

1. Scope the Review

Identify the files/features to review. Determine the review scope:

  • Feature review — all files in a feature directory
  • PR review — only changed files
  • Full codebase audit — all features

2. Load the Rule Set

Read all applicable rules from .agents/rules/. Use rule-priority.md for severity classification.

3. Review Categories (Priority Order)

Review each file/feature against these categories, in order from rule-priority.md:

Critical (Must Fix)
  • Security — injection, hardcoded secrets, broken auth
  • Data loss — missing error handling on writes, no transaction boundaries
  • Resource leaks — unclosed connections, missing cleanup
Major (Should Fix)
  • Testability — I/O not behind interfaces, untested error paths
  • Observability — missing logging on operations, no correlation IDs
  • Error handling — empty catch blocks, swallowed errors
  • Architecture — circular dependencies, wrong layer access
Minor (Nice to Fix)
  • Pattern consistency — deviation from established codebase patterns
  • Naming — unclear variable/function names
  • Code organization — functions too long, mixed responsibilities
Enhancement (Backlog)
  • Style — formatting issues or non-critical refactorings
  • Documentation — missing comments on complex logic

4. Produce Findings

Output a structured findings document:

# Code Review: {Feature/Module Name}
Date: {date}
Reviewer: AI Agent (fresh context)

## Summary
- **Files reviewed:** N
- **Issues found:** N (X critical, Y major, Z minor, W enhancement)

## Critical Issues
- [ ] **[SEC]** {description} — [{file}:{line}](file:///path)
- [ ] **[DATA]** {description} — [{file}:{line}](file:///path)

## Major Issues
- [ ] **[TEST]** {description} — [{file}:{line}](file:///path)
- [ ] **[OBS]** {description} — [{file}:{line}](file:///path)

## Minor Issues
- [ ] **[PAT]** {description} — [{file}:{line}](file:///path)

## Enhancement Issues
- [ ] {description} — [{file}:{line}](file:///path)

## Rules Applied
List of rules referenced during this review.

Read the full file on GitHub · 178 lines

Files

What ships with it

12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 9d ago First seen · 178 lines · 44 tokens per session scan A 149cbe9486bb

Subscribe to this mod's changes

code-review is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 18d ago), licensed MIT. It adds 44 tokens to every session and 1,812 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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