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/agentset-ai/agentset/clean-codegit clone --depth 1 https://github.com/agentset-ai/agentsetWhat 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 | $0.00322 | $0.00322 |
| Opus 5 | $0.00161 | $0.00161 |
| Sonnet 5 | $0.00064 | $0.00064 |
| Haiku 4.5 | $0.00032 | $0.00032 |
Grade A, and why
clean-code 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 yesterday.
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.
This is a copy
84% identical to clean-code — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Clean Code Guidelines
Constants Over Magic Numbers
- Replace hard-coded values with named constants
- Use descriptive constant names that explain the value's purpose
- Keep constants at the top of the file or in a dedicated constants file
Meaningful Names
- Variables, functions, and classes should reveal their purpose
- Names should explain why something exists and how it's used
- Avoid abbreviations unless they're universally understood
Smart Comments
- Don't comment on what the code does - make the code self-documenting
- Use comments to explain why something is done a certain way
- Document APIs, complex algorithms, and non-obvious side effects
Single Responsibility
- Each function should do exactly one thing
- Functions should be small and focused
- If a function needs a comment to explain what it does, it should be split
DRY (Don't Repeat Yourself)
- Extract repeated code into reusable functions
- Share common logic through proper abstraction
- Maintain single sources of truth
Clean Structure
- Keep related code together
- Organize code in a logical hierarchy
- Use consistent file and folder naming conventions
Encapsulation
- Hide implementation details
- Expose clear interfaces
- Move nested conditionals into well-named functions
Code Quality Maintenance
- Refactor continuously
- Fix technical debt early
- Leave code cleaner than you found it
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.
- yesterday First seen · 56 lines · 322 tokens per session scan A cf40932014f6
clean-code is a cursor rule published in the GitHub repository agentset-ai/agentset (2,076 stars, last pushed 1mo ago), licensed MIT. It adds 322 tokens to every session, about $0.0016 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to clean-code, differing in 23 lines, and is treated as a copy.
Other cursor rules, from other repositories
project-rules
智能体平台前端项目开发规则和最佳实践.
cursorrules
所有 Git 提交说明必须使用简体中文书写，禁止使用英文标题或英文正文。 专有名词、包名、API 路径等技术标识可保留英文。.
write-migration
Author an idempotent, state-detecting migration under scripts/migrations/ for a breaking llm-wiki-memory change. Use when shipping a breaking change to the engine, or when asked to write a migration / upgrade path. Replaces the retired release-runbook flow.
debug-capture-pipeline
Debug llm-wiki-memory capture/recall — flush breadcrumbs, daily-vs-compile lag, failed-distill stash recovery, embedding cache, hook wiring. Use when memory didn't capture, doesn't recall, or a hook/cron looks broken.
self-observability
Self-observability (watch llm-wiki-memory while you work; offer fixes) — This rule applies to every AI agent (Claude Code, Cursor, Codex, generic MCP clients) working Canonical file is .agents/rules/self-observability.md.
vios-sqa
VIOS SQA agent. One-click build → deploy → test → UI validation cycle. Use when asked to run regression tests, check test results, deploy for testing, or verify UI behavior.