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.
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/commands/adriannoes/awesome-agentic-ai/code-review)<a href="https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/code-review"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/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.
<a href="https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/code-review"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/code-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00387 |
| Opus 5.5 | $0.00000 | $0.00155 |
| Sonnet 5.5 | $0.00000 | $0.00077 |
| Haiku 4.5 | $0.00000 | $0.00039 |
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 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:
- code-review — 94% identical, 39 lines differ
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Overview
Perform a thorough code review that verifies functionality, maintainability, and security before approving a change. Focus on architecture, readability, performance implications, and provide actionable suggestions for improvement.
Steps
-
Understand the change
- Read the PR description and related issues for context
- Identify the scope of files and features impacted
- Note any assumptions or questions to clarify with the author
-
Validate functionality
- Confirm the code delivers the intended behavior
- Exercise edge cases or guard conditions mentally or by running locally
- Check error handling paths and logging for clarity
-
Assess quality
- Ensure functions are focused, names are descriptive, and code is readable
- Watch for duplication, dead code, or missing tests
- Verify documentation and comments reflect the latest changes
-
Review security and risk
- Look for injection points, insecure defaults, or missing validation
- Confirm secrets or credentials are not exposed
- Evaluate performance or scalability impacts of the change
Review Checklist
Functionality
- Intended behavior works and matches requirements
- Edge cases handled gracefully
- Error handling is appropriate and informative
Code Quality
- Code structure is clear and maintainable
- No unnecessary duplication or dead code
- Tests/documentation updated as needed
Security & Safety
- No obvious security vulnerabilities introduced
- Inputs validated and outputs sanitized
- Sensitive data handled correctly
Additional Review Notes
- Architecture and design decisions considered
- Performance bottlenecks or regressions assessed
- Coding standards and best practices followed
- Resource management, error handling, and logging reviewed
- Suggested alternatives, additional test cases, or documentation updates captured
Provide constructive feedback with concrete examples and actionable guidance for the author.
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 · 58 lines · 0 tokens per session scan A 7425c7e656df
code-review is a command published in the GitHub repository adriannoes/awesome-agentic-ai (69 stars, last pushed 11d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 387 tokens. 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 commands, from other repositories
review
A command that reviews code changes across correctness, readability, architecture, security, and performance.
review-code
Read ALL memory bank code rules + best practices, check the files that changed, and APPLY fixes so they adhere. The active counterpart to /scan (which is read-only). Use after an AI session, before commit, to make changed files compliant.
review-code
Agent review pass plus manual checklist for correctness, security, performance, and accessibility.
pr-description
Write a pull request description that helps reviewers: what changed, why, and how to verify, from a diff.
start
Read and follow the skill at .cursor/skills/start/SKILL.md.
pre-check
Run automated code quality pre-checks (linting, type validation, security audit) before manual review to reduce review time by 50%.