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/holasoymalva/AI-PR-Reviewer-TasksWrote 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/rules/holasoymalva/ai-pr-reviewer-tasks/suggest-improvements)<a href="https://agentmods.dev/rules/holasoymalva/ai-pr-reviewer-tasks/suggest-improvements"><img src="https://agentmods.dev/badge/rules/holasoymalva/ai-pr-reviewer-tasks/suggest-improvements/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/rules/holasoymalva/ai-pr-reviewer-tasks/suggest-improvements"><img src="https://agentmods.dev/badge/rules/holasoymalva/ai-pr-reviewer-tasks/suggest-improvements.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 | $0.00007 | $0.01595 |
| Opus 5 | $0.00003 | $0.00797 |
| Sonnet 5 | $0.00001 | $0.00319 |
| Haiku 4.5 | $0.00001 | $0.00160 |
Grade A, and why
suggest-improvements 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 11d 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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rule: Suggest Code Improvements
Goal
To guide an AI assistant in providing specific, actionable improvement suggestions for Pull Request changes, focusing on code quality, performance, security, and maintainability enhancements.
Process
-
Receive Analysis Context: Build upon previous analysis from
analyze-pr-changes.mdcanddetect-code-smells.mdc. -
Prioritization Discussion: Understand improvement focus:
- Priority Areas: "Should I focus on specific improvements? (performance, security, readability)"
- Impact Level: "Do you prefer high-impact changes or quick wins?"
- Implementation Scope: "Should I suggest refactoring or just surface-level improvements?"
- Timeline: "Are these for immediate implementation or future planning?"
-
Generate Improvement Suggestions: Create specific, actionable recommendations with code examples.
-
Provide Implementation Roadmap: Organize suggestions by priority and effort level.
Improvement Categories
🚀 Performance Optimizations
Algorithm Improvements
- Replace inefficient algorithms with better alternatives
- Optimize loop structures and conditions
- Implement caching strategies
- Reduce computational complexity
Memory Management
- Eliminate memory leaks
- Optimize data structure usage
- Implement object pooling where appropriate
- Reduce memory allocations
Database Optimizations
- Query optimization and indexing
- Batch operations instead of individual calls
- Connection pooling
- Lazy loading strategies
🔒 Security Enhancements
Input Validation
- Implement comprehensive input sanitization
- Add parameter validation
- Prevent injection attacks
- Validate file uploads and data types
Authentication & Authorization
- Strengthen access controls
- Implement proper session management
- Add audit logging
- Secure API endpoints
Data Protection
- Encrypt sensitive data
- Secure credential storage
- Implement proper error handling
- Add rate limiting
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.
- 11d ago First seen · 295 lines · 7 tokens per session scan A 13d877666879
suggest-improvements is a cursor rule published in the GitHub repository holasoymalva/AI-PR-Reviewer-Tasks (14 stars, last pushed 1y ago), licensed Apache-2.0. It adds 7 tokens to every session and 1,595 once invoked, about $0.0000 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 cursor rules, from other repositories
refactoring
Refactoring: systematic approach, extract/inline, guard clauses, early returns.
clean-code
Clean code: naming, functions, simplicity.
code-review
Code review: reviewing approach, authoring PRs, feedback conventions.
git-workflow
Git workflow: commits, branches, PRs, history management.
refactor-test
Review and refactor generated unit tests for improved quality and coverage.
generate-unit-test
Generate comprehensive unit tests based on function analysis.