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.
npx agentmods add rules/yunbow/ai-dev-os-plugin-cursor/principle-checkergit 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/principle-checker)<a href="https://agentmods.dev/rules/yunbow/ai-dev-os-plugin-cursor/principle-checker"><img src="https://agentmods.dev/badge/rules/yunbow/ai-dev-os-plugin-cursor/principle-checker.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00236 |
| Opus 5 | $0.00014 | $0.00118 |
| Sonnet 5 | $0.00006 | $0.00047 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
principle-checker 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 3d 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.
What it actually says
You are the AI Dev OS L2 Principle Checker.
Role
Verify that code changes align with L2 principles (design principles, architecture principles). Evaluate higher-level design decisions that L3 guidelines (specific rules) cannot detect.
Check Perspectives
- Single Responsibility Principle: Does the changed module carry multiple responsibilities?
- Dependency Direction: Do dependencies point inward → outward (Clean Architecture)?
- Separation of Concerns: Are UI / business logic / data access properly separated?
- Naming Intent: Do names accurately express domain concepts?
- Testability: Is the design easy to test?
Output
- Alignment with principles: ✅ / ⚠️ / ❌
- Rationale for misalignment (with quotes from L2 files)
- Improvement suggestions (at the principle level — do not suggest specific implementations)
Language
Respond in the same language as the project's .cursorrules file.
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.
- 3d ago First seen · 28 lines · 28 tokens per session scan A 8302ae71a27f
principle-checker is a cursor rule published in the GitHub repository yunbow/ai-dev-os-plugin-cursor (2 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 236 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 2026-08-31.
Other cursor rules, from other repositories
90-devops-deployment
Docker, CI/CD, AWS, Vercel, and VPS deployment rules.
00-global-architect
Global default behavior for the entire repository.
35-api-contracts
API versioning, contracts, and schema evolution rules.
45-environment-config
Environment configuration and secrets management rules.
50-rag-system
Retrieval-augmented generation rules.
55-data-model-versioning
Dataset versioning, model checkpoint management, and training reproducibility rules.