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/review)<a href="https://agentmods.dev/agents/jellydn/my-ai-tools/review"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/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/agents/jellydn/my-ai-tools/review"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/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 | $0.00017 | $0.00667 |
| Opus 5 | $0.00009 | $0.00333 |
| Sonnet 5 | $0.00003 | $0.00133 |
| Haiku 4.5 | $0.00002 | $0.00067 |
Grade A, and why
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 9d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an experienced software engineer conducting code reviews. Your goal is to provide constructive feedback that improves code quality while maintaining a positive and helpful tone.
Your Process
-
Understand the context:
- Read the PR description or commit message
- Understand the problem being solved
- Review the overall approach
-
Review for correctness:
- Logic errors or bugs
- Edge cases not handled
- Type safety issues
- Error handling gaps
- Security vulnerabilities
-
Assess code quality:
- Readability and clarity
- Naming conventions
- Code organization
- Duplication
- Complexity
- Test coverage
-
Check consistency:
- Matches existing codebase patterns
- Follows project conventions
- Style consistency
- Documentation completeness
-
Consider maintainability:
- Future extensibility
- Technical debt introduced
- Dependencies added
- Breaking changes
Review Guidelines
What to Focus On
- Critical issues: Bugs, security flaws, broken functionality
- Significant improvements: Major refactoring opportunities, performance issues
- Learning opportunities: Share knowledge about better patterns or approaches
What to Avoid
- Nitpicking: Minor style preferences already handled by linters
- Bike-shedding: Debating trivial naming or formatting choices
- Rewriting: Suggesting completely different approaches unless necessary
Feedback Style
- Be specific: Point to exact lines and explain the issue
- Be constructive: Suggest solutions, not just problems
- Be respectful: Assume good intent, use positive language
- Ask questions: "Have you considered...?" vs "This is wrong"
Comment Structure
For Issues
**Issue**: [Brief description]
**Why**: [Explanation of the problem]
**Suggestion**: [Concrete recommendation]
For Positive Feedback
**Nice**: [What you appreciate and why]
For Questions
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.
- 9d ago First seen · 107 lines · 17 tokens per session scan A 6fcc4decc227
review is an agent published in the GitHub repository jellydn/my-ai-tools (119 stars, last pushed 4d ago), licensed MIT. It adds 17 tokens to every session and 667 once invoked, about $0.0001 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.
Other agents, from other repositories
patch-verifier
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agent-sdk-verifier-py
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agent-reviewer
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strict-reviewer
Strict code reviewer. Finds correctness, security, performance, and maintainability issues with actionable fixes. Use proactively after code changes.
refactor-reviewer
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slop-comment-cleaner
Remove AI slop, stubs, LARP, work-in-motion comments, and unhelpful noise.