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/n0rvyn/cursor-rulesWrote 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/n0rvyn/cursor-rules/135-feature-implementation)<a href="https://agentmods.dev/rules/n0rvyn/cursor-rules/135-feature-implementation"><img src="https://agentmods.dev/badge/rules/n0rvyn/cursor-rules/135-feature-implementation/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/n0rvyn/cursor-rules/135-feature-implementation"><img src="https://agentmods.dev/badge/rules/n0rvyn/cursor-rules/135-feature-implementation.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 | $0.00567 | $0.00567 |
| Opus 5.5 | $0.00227 | $0.00227 |
| Sonnet 5.5 | $0.00113 | $0.00113 |
| Haiku 4.5 | $0.00057 | $0.00057 |
Grade A, and why
135-feature-implementation 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 96 lines · 567 tokens per session scan A 5684afdae36e
135-feature-implementation is a cursor rule published in the GitHub repository n0rvyn/cursor-rules (2 stars, last pushed 11mo ago), with no licence file. It adds 567 tokens to every session, about $0.0023 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
ai-dev-os-check
Checks code changes against AI Dev OS guidelines and automatically fixes violations. Based on benchmark Test 011 — check+fix achieves +9.9 improvement vs report-only (+0.8). Supports git diff (default), staged changes, and branch comparison. Use --dry-run for report-only mode.
ai-dev-os-init
AI Dev OS initial setup wizard. Use when starting a new project or introducing AI Dev OS. Asks about the project's tech stack and scale, then generates an optimal 4-layer structure from templates.
ai-dev-os-plan
Creates an implementation plan with relevant AI Dev OS guideline checklists before writing any code. Analyzes the request, identifies affected files and applicable guidelines, then presents a plan with embedded checklist for user approval. Implementation begins only after approval.
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.
ai-dev-os-scan
Scans ALL source files in the project against AI Dev OS guidelines. Unlike @ai-dev-os-check (which only checks git diff), this performs a full project-wide compliance scan. Use for initial audits, periodic full reviews, or after introducing new guidelines.
ai-dev-os-audit
Audits the health of the AI Dev OS 4-layer structure. Detects dependency rule violations, expiration, and coverage gaps. Used for monthly or quarterly periodic maintenance.