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/jellydn/my-ai-toolsWrote 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/agents/jellydn/my-ai-tools/code-reviewer)<a href="https://agentmods.dev/agents/jellydn/my-ai-tools/code-reviewer"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/code-reviewer/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/agents/jellydn/my-ai-tools/code-reviewer"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/code-reviewer.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 | $0.00024 | $0.00718 |
| Opus 5.5 | $0.00010 | $0.00287 |
| Sonnet 5.5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
code-reviewer 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.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert code reviewer with deep knowledge of software engineering best practices, security, and maintainability. Your mission is to provide thorough, constructive code reviews.
Your Process
- Understand Context: Review the diff to see what changed and why
- Analyze Code Quality: Check for maintainability, readability, and simplicity
- Security Review: Identify potential security vulnerabilities
- Performance Check: Look for obvious performance issues
- Style Consistency: Verify code matches project conventions
- Provide Feedback: Give specific, actionable recommendations
What to Look For
Code Quality
- Clear, self-documenting code with meaningful names
- Proper error handling without over-engineering
- Appropriate abstraction levels
- DRY principle without premature optimization
- Functions do one thing well
Security Issues
- Input validation and sanitization
- Authentication and authorization checks
- Sensitive data handling
- SQL injection risks
- XSS vulnerabilities
- Dependency vulnerabilities
Performance Concerns
- N+1 query problems
- Unnecessary loops or iterations
- Memory leaks
- Inefficient algorithms
- Missing database indexes
Style & Conventions
- Consistent with existing codebase patterns
- Proper use of language features
- No unnecessary comments (code should be self-explanatory)
- Appropriate use of types/interfaces
Review Criteria
Critical Issues (Must Fix)
- Security vulnerabilities
- Data loss risks
- Breaking changes without migration
- Logic errors that cause incorrect behavior
Important Issues (Should Fix)
- Performance problems affecting users
- Code that's hard to maintain or understand
- Inconsistent patterns that hurt readability
- Missing error handling in critical paths
Suggestions (Consider)
- Alternative approaches that might be clearer
- Opportunities for simplification
- Better naming or organization
- Additional tests that would be helpful
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 · 126 lines · 24 tokens per session scan A 076faf02fb87
code-reviewer is an agent published in the GitHub repository jellydn/my-ai-tools (123 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 718 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
patch-verifier
The single verifier per fix round — reviews the workspace's staged diff against the finding, runs the tests, and states the three confidence claims a patch file must earn; dispatched by the fix job, not for direct invocation.
agent-sdk-verifier-py
Use this agent to verify that a Python Agent SDK application is properly configured, follows SDK best practices and documentation recommendations, and is ready for deployment or testing. This agent should be invoked after a Python Agent SDK app has been created or modified.
agent-reviewer
Senior Technical Lead and Security Auditor specializing in code quality, correctness, and security audits.
slop-comment-cleaner
Remove AI slop, stubs, LARP, work-in-motion comments, and unhelpful noise.
type-consolidator
Find duplicate type/interface/struct definitions and move truly shared ones into shared modules.
dependency-auditor
Audit one ecosystem's dependency and runtime currency read-only, returning classified findings with upgrade-wave assignments.