code-review-checklist

code-review-checklist is a skill for Claude Code from ashrafmusa/agenticana. It costs 18 tokens per session (642 once invoked), scanned A, a copy of code-review-checklist, MIT.

A tool for replaying the latest global execution-planner trace from a Tlamatini log file. A planner trace records how an automated system scored and selected available capabilities.

In plain words
What is it for?
It is for inspecting the newest planner run, recording each capability's score and contributing keywords, and explaining unexpected selections. Results are kept under 32 KB to limit the amount of log detail returned.
Why use it?
It explains why capabilities were selected or omitted instead of leaving planner decisions as unexplained scores. It also shows which keywords affected each score and whether a selection limit prevented a capability from being chosen.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit It is for inspecting the newest planner run, recording each capability's score and contributing keywords, and explaining unexpected selections. Results are kept under 32 KB to limit the amount of log detail returned.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ashrafmusa/agenticana/code-review-checklist
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.

Any agent
npx skills add ashrafmusa/agenticana --skill code-review-checklist
Clone the repo
git clone --depth 1 https://github.com/ashrafmusa/agenticana

Made for: Claude Code.

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-checklist

README.md
[![agentmods](https://agentmods.dev/badge/skills/ashrafmusa/agenticana/code-review-checklist.svg)](https://agentmods.dev/skills/ashrafmusa/agenticana/code-review-checklist)
Your own site
<a href="https://agentmods.dev/skills/ashrafmusa/agenticana/code-review-checklist"><img src="https://agentmods.dev/badge/skills/ashrafmusa/agenticana/code-review-checklist.svg" alt="Measured on agentmods" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 642 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.
Origin 89% copy Near-identical to another mod 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 $0.00018 $0.00642
Opus 5 $0.00009 $0.00321
Sonnet 5 $0.00004 $0.00128
Haiku 4.5 $0.00002 $0.00064

Measured 7d ago against content hash 905ec9820ad5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

code-review-checklist 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 7d 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.

Origin

This is a copy

89% identical to code-review-checklist — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/code-review-checklist/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Review Checklist

Quick Review Checklist

Correctness

  • Code does what it's supposed to do
  • Edge cases handled
  • Error handling in place
  • No obvious bugs

Security

  • Input validated and sanitized
  • No SQL/NoSQL injection vulnerabilities
  • No XSS or CSRF vulnerabilities
  • No hardcoded secrets or sensitive credentials
  • AI-Specific: Protection against Prompt Injection (if applicable)
  • AI-Specific: Outputs are sanitized before being used in critical sinks

Performance

  • No N+1 queries
  • No unnecessary loops
  • Appropriate caching
  • Bundle size impact considered

Code Quality

  • Clear naming
  • DRY - no duplicate code
  • SOLID principles followed
  • Appropriate abstraction level

Testing

  • Unit tests for new code
  • Edge cases tested
  • Tests readable and maintainable

Documentation

  • Complex logic commented
  • Public APIs documented
  • README updated if needed

AI & LLM Review Patterns (2025)

Logic & Hallucinations

  • Chain of Thought: Does the logic follow a verifiable path?
  • Edge Cases: Did the AI account for empty states, timeouts, and partial failures?
  • External State: Is the code making safe assumptions about file systems or networks?

Prompt Engineering Review

// ❌ Vague prompt in code
const response = await ai.generate(userInput);

// ✅ Structured & Safe prompt
const response = await ai.generate({
  system: "You are a specialized parser...",
  input: sanitize(userInput),
  schema: ResponseSchema
});

Anti-Patterns to Flag

// ❌ Magic numbers
if (status === 3) { ... }

// ✅ Named constants
if (status === Status.ACTIVE) { ... }

// ❌ Deep nesting
if (a) { if (b) { if (c) { ... } } }

// ✅ Early returns
if (!a) return;
if (!b) return;
if (!c) return;
// do work

// ❌ Long functions (100+ lines)
// ✅ Small, focused functions

// ❌ any type
const data: any = ...

// ✅ Proper types
const data: UserData = ...

Read the full file on GitHub · 110 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. 7d ago First seen · 110 lines · 18 tokens per session scan A 905ec9820ad5

Subscribe to this mod's changes

code-review-checklist is a skill published in the GitHub repository ashrafmusa/agenticana (2 stars, last pushed 7d ago), licensed MIT. It adds 18 tokens to every session and 642 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to code-review-checklist, differing in 4 lines, and is treated as a copy.

Related

Other skills, from other repositories

code-review-recent-changes

Review recent changes since a fixed point (commit, branch, tag, or merge-base) across three independent axes - Standards, Spec, and Maintainability - producing severity-ordered findings with an explicit verdict. Use when the user wants to review a branch, a PR, or recent committed changes.

sammcj/agentic-coding · 66 tokens

code-review

Use this skill after completing multiple, complex software development tasks before informing the user that work is complete.

sammcj/agentic-coding · 23 tokens

code-simplification

Use this skill when you need to review and refactor code to make it simpler, more maintainable, and easier to understand. Helps with identifying overly complex solutions, unnecessary abstractions.

sammcj/agentic-coding · 42 tokens

self-review

Use to critically self-review your changes, or when you want to delegate the review to a sub-agent.

sammcj/agentic-coding · 24 tokens

improve-codebase-architecture

Find deepening opportunities in a codebase, informed by whatever domain language and architectural decisions are already documented in the repo. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

sammcj/agentic-coding · 66 tokens

facts-discover

Scan the codebase and classify every fact by lifecycle stage — tag @draft, @spec, or @implemented based on what the code actually shows. Add missing facts, fix inaccurate ones, remove obsolete ones. Use when asked to discover facts, bootstrap or update a fact sheet, scan the codebase for truths, sync facts to match…

av/facts · 82 tokens