code-review

code-review is a skill for Claude Code, Codex from asgarovf/locusai. It costs 49 tokens per session (2,179 once invoked), scanned A, original, MIT.

A structured review of code changes for bugs, security risks, maintainability, performance, and test coverage.

In plain words
What is it for?
Use it to review pull requests, audit security, assess implementation quality, and suggest improvements.
Why use it?
It helps catch problems and unclear design before changes are merged into the project.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/asgarovf/locusai/code-review
Any agent
npx skills add asgarovf/locusai --skill code-review
Clone the repo
git clone --depth 1 https://github.com/asgarovf/locusai

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/asgarovf/locusai/code-review.svg)](https://agentmods.dev/skills/asgarovf/locusai/code-review)
Your own site
<a href="https://agentmods.dev/skills/asgarovf/locusai/code-review"><img src="https://agentmods.dev/badge/skills/asgarovf/locusai/code-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,179 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00049 $0.02179
Opus 5 $0.00024 $0.01090
Sonnet 5 $0.00010 $0.00436
Haiku 4.5 $0.00005 $0.00218

Measured 4d ago against content hash a0d8668746db, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 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.

.agents/skills/code-review/SKILL.md · 392 lines

How it starts

The opening of the file, as written. The whole thing — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Review

When to use this skill

  • Reviewing pull requests
  • Checking code quality
  • Providing feedback on implementations
  • Identifying potential bugs
  • Suggesting improvements
  • Security audits
  • Performance analysis

Instructions

Step 1: Understand the context

Read the PR description:

  • What is the goal of this change?
  • Which issues does it address?
  • Are there any special considerations?

Check the scope:

  • How many files changed?
  • What type of changes? (feature, bugfix, refactor)
  • Are tests included?

Step 2: High-level review

Architecture and design:

  • Does the approach make sense?
  • Is it consistent with existing patterns?
  • Are there simpler alternatives?
  • Is the code in the right place?

Code organization:

  • Clear separation of concerns?
  • Appropriate abstraction levels?
  • Logical file/folder structure?

Step 3: Detailed code review

Naming:

  • Variables: descriptive, meaningful names
  • Functions: verb-based, clear purpose
  • Classes: noun-based, single responsibility
  • Constants: UPPER_CASE for true constants
  • Avoid abbreviations unless widely known

Functions:

  • Single responsibility
  • Reasonable length (< 50 lines ideally)
  • Clear inputs and outputs
  • Minimal side effects
  • Proper error handling

Classes and objects:

  • Single responsibility principle
  • Open/closed principle
  • Liskov substitution principle
  • Interface segregation
  • Dependency inversion

Error handling:

  • All errors caught and handled
  • Meaningful error messages
  • Proper logging
  • No silent failures
  • User-friendly errors for UI

Code quality:

  • No code duplication (DRY)
  • No dead code
  • No commented-out code
  • No magic numbers
  • Consistent formatting

Step 4: Security review

Input validation:

  • All user inputs validated
  • Type checking
  • Range checking
  • Format validation

Authentication & Authorization:

  • Proper authentication checks
  • Authorization for sensitive operations
  • Session management
  • Password handling (hashing, salting)

Read the full file on GitHub · 392 lines

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. 4d ago First seen · 392 lines · 49 tokens per session scan A a0d8668746db

Subscribe to this mod's changes

code-review is a skill published in the GitHub repository asgarovf/locusai (23 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 2,179 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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