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/yunbow/ai-dev-os-plugin-cursorWrote 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/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-review)<a href="https://agentmods.dev/rules/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-review"><img src="https://agentmods.dev/badge/rules/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-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/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-review"><img src="https://agentmods.dev/badge/rules/yunbow/ai-dev-os-plugin-cursor/ai-dev-os-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 | $0.00056 | $0.00683 |
| Opus 5.5 | $0.00022 | $0.00273 |
| Sonnet 5.5 | $0.00011 | $0.00137 |
| Haiku 4.5 | $0.00006 | $0.00068 |
Grade A, and why
ai-dev-os-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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Dev OS Pre-PR Self-Review
Execution Flow
1. Determine Base Branch
- Ask the user for the base branch (default:
main) - Get all changed files:
git diff [base]...HEAD --name-only - Get commit messages:
git log [base]...HEAD --oneline
2. Parse .cursorrules
Extract the list of guideline file paths from .cursorrules (or CLAUDE.md / AGENTS.md if present). Also load L1 philosophy and L2 principles files for design-level review.
3. L3 Guideline Compliance Check
Run the same checks as @ai-dev-os-check:
- Build file-to-guideline mapping
- Extract and verify check items
- Collect violations and review items
4. L2 Design Review
For each changed file, evaluate higher-level design concerns:
- Single Responsibility: Does each module/function have one clear purpose?
- Dependency Direction: Do dependencies flow correctly (inward, not outward)?
- Separation of Concerns: Is UI / business logic / data access properly separated?
- Naming Intent: Do names express domain concepts accurately?
- Error Handling Strategy: Is error handling consistent with L2 principles?
- Testability: Is the design easy to test?
5. L1 Philosophy Alignment
Check whether the overall change aligns with the project's core values:
- Does this change serve the stated philosophical goals?
- Are there trade-offs that conflict with core values?
6. PR Description Draft (Optional)
If the review passes, offer to draft a PR description:
- Summary of changes
- Design decisions and rationale
- Guideline compliance status
7. Report Output
## AI Dev OS Pre-PR Review
### Overview
- Base branch: {branch}
- Commits: N
- Files changed: N
### L3 Guideline Compliance
- ✅ Passed: N / ⚠️ Review: N / ❌ Violation: N
### L2 Design Review
| Perspective | Status | Notes |
|-------------|--------|-------|
| Single Responsibility | ✅/⚠️/❌ | |
| Dependency Direction | ✅/⚠️/❌ | |
| Separation of Concerns | ✅/⚠️/❌ | |
| Naming Intent | ✅/⚠️/❌ | |
| Error Handling | ✅/⚠️/❌ | |
| Testability | ✅/⚠️/❌ | |
### L1 Philosophy Alignment
- [Assessment of overall alignment with core values]
### Action Items
1. [Must fix before PR] ...
2. [Should consider] ...
3. [Nice to have] ...
### PR Description Draft
> [Auto-generated if review passes]
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 · 85 lines · 56 tokens per session scan A 64e815c24d00
ai-dev-os-review is a cursor rule published in the GitHub repository yunbow/ai-dev-os-plugin-cursor (2 stars, last pushed 6mo ago), licensed MIT. It adds 56 tokens to every session and 683 once invoked, about $0.0002 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 2026-08-31.
Other cursor rules, from other repositories
refactoring
Refactoring: systematic approach, extract/inline, guard clauses, early returns.
code-review
Code review: reviewing approach, authoring PRs, feedback conventions.
clean-code
Clean code: naming, functions, simplicity.
git-workflow
Git workflow: commits, branches, PRs, history management.
pr-code-review
Code review rule for PR analysis. Challenges architectural decisions, examines the diff with the default branch, and provides comprehensive feedback on code quality, security, performance, and maintainability before publishing.
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.