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 skills add ashrafmusa/agenticana --skill code-review-checklistgit clone --depth 1 https://github.com/ashrafmusa/agenticanaWrote 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/ashrafmusa/agenticana/code-review-checklist)<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>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 | $0.00018 | $0.00642 |
| Opus 5 | $0.00009 | $0.00321 |
| Sonnet 5 | $0.00004 | $0.00128 |
| Haiku 4.5 | $0.00002 | $0.00064 |
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.
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.
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 = ...
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.
- 7d ago First seen · 110 lines · 18 tokens per session scan A 905ec9820ad5
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.
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.
code-review
Use this skill after completing multiple, complex software development tasks before informing the user that work is complete.
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.
self-review
Use to critically self-review your changes, or when you want to delegate the review to a sub-agent.
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.
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…