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/adonai-labs/agent-runwayWrote 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/adonai-labs/agent-runway/code-review)<a href="https://agentmods.dev/rules/adonai-labs/agent-runway/code-review"><img src="https://agentmods.dev/badge/rules/adonai-labs/agent-runway/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/rules/adonai-labs/agent-runway/code-review"><img src="https://agentmods.dev/badge/rules/adonai-labs/agent-runway/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 | $0.01954 |
| Opus 5.5 | $0.00000 | $0.00782 |
| Sonnet 5.5 | $0.00000 | $0.00391 |
| Haiku 4.5 | $0.00000 | $0.00195 |
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.
How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Guidelines
Objective
Provide a clear, concise, and actionable review of the Pull Request (PR). Focus on overall codebase quality, including readability, maintainability, functionality, adherence to best practices, performance optimizations, and testing coverage. Avoid minor nitty-picky comments and repetitive feedback.
Focus Areas
- Code Quality: Assess the readability, organization, and maintainability of the code
- Functionality: Ensure the PR meets its intended purpose and functions as expected
- Best Practices: Evaluate adherence to coding standards, design patterns, and project guidelines
- Performance: Identify any potential performance improvements or optimizations
- Testing: Review the comprehensiveness and effectiveness of the test coverage
- Security: Identify any potential security vulnerabilities or concerns
- Bugs Found: List any bugs identified in the PR
- Issues Found: Consolidate performance and testing issues into a unified section
Critical Instructions
- If you have nothing to say about a particular section, you can omit it from the review
- If you think there are no issues and the PR is good to go, mention it in the conclusion. No need to add unnecessary feedback. Don't be redundant
Scoring Criteria
90-100: Exceptional Quality
- Clean, efficient, and well-documented code
- Comprehensive test coverage (>90%)
- Follows all best practices and design patterns
- No security vulnerabilities
- Optimal performance considerations
- Clear documentation and comments
75-89: High Quality
- Well-structured and maintainable code
- Good test coverage (70-90%)
- Minor optimization opportunities
- No critical security issues
- Few non-critical issues
- Adequate documentation
60-74: Average Quality
- Functional but needs improvement
- Basic test coverage (40-70%)
- Some code duplication
- Multiple minor issues
- Basic security considerations
- Limited documentation
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 · 239 lines · 0 tokens per session scan A 24d4676f7d35
code-review is a cursor rule published in the GitHub repository adonai-labs/agent-runway (2 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,954 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 2026-08-31.
Other cursor rules, from other repositories
optimize-dry-solid-principles-cursorrules-prompt-f
Cursor rules for optimization development with DRY and SOLID principles integration.
code-optimization
Guidelines for optimizing duplicate and poorly structured code.
language-agnostic-patterns
Language-agnostic programming patterns: SOLID, design patterns, clean code, and architecture. Load when refactoring, designing abstractions, or reviewing structure — not for everyday syntax.
code-review-en
Code Review Guidelines (English).
agent-master
Meta-orchestrator that classifies tasks and routes to the right combination of installed skills across caveman (output compression), superpowers (dev workflow), anthropic-skills (business/domain expertise), and wired tactical libraries (wshobson, davila7, impeccable, anthropic-official). 26 routing categories…
ai-dev-os-review
Performs a comprehensive self-review before creating a PR. Combines guideline compliance checking (L3) with design-level review (L2) and philosophical alignment (L1). Unlike @ai-dev-os-check which only checks rules, this also evaluates architecture decisions and code design quality.