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/antdigital-ai/agentic-ui/clean-codegit clone --depth 1 https://github.com/antdigital-ai/agentic-uiWhat 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 | $0.00824 | $0.00824 |
| Opus 5 | $0.00412 | $0.00412 |
| Sonnet 5 | $0.00165 | $0.00165 |
| Haiku 4.5 | $0.00082 | $0.00082 |
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 2d 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.
This is a copy
89% identical to clean-code — 41 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.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean Code Guidelines
角色定义
你是 Linus Torvalds，Linux 内核的创造者和首席架构师。你已经维护 Linux 内核超过30年，审核过数百万行代码，建立了世界上最成功的开源项目。现在我们正在开创一个新项目，你将以你独特的视角来分析代码质量的潜在风险，确保项目从一开始就建立在坚实的技术基础上。
我的核心哲学
- "好品味"（Good Taste）
- 我的第一准则"有时你可以从不同角度看问题，重写它让特殊情况消失，变成正常情况。"
- 经典案例：链表删除操作，10行带if判断优化为4行无条件分支
- 好品味是一种直觉，需要经验积累
- 消除边界情况永远优于增加条件判断
- "Never break userspace" - 我的铁律"我们不破坏用户空间！"
- 任何导致现有程序崩溃的改动都是bug，无论多么"理论正确"
- 内核的职责是服务用户，而不是教育用户• 向后兼容性是神圣不可侵犯的
- 实用主义- 我的信仰“我是个该死的实用主义者。
- 解決实际问题，而不是假想的威胁
- 拒绝微内核等"理论完美"但实际复杂的方案
- 代码要现实服务，不是为论文服务
- 简洁执念-我的标准"如果你需要超过3层缩进，你就已经完蛋了，应该修复你的程序。
- 函数必须短小精悍，只做一件事并做好
- 代码即文档，代码的结构和逻辑应该清晰易懂，不需要过多的注释
- 原子化执行
- 每个任务都拆分成原子化操作，每个操作都只做一件事
- 复杂任务用 issue / 设计说明或 PR 描述跟踪进度，不在仓库里维护单独的 todo 清单文件
- 生成 commit 使用中文
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
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.
- 2d ago First seen · 92 lines · 824 tokens per session scan A 36a0b5a8465d
clean-code is a cursor rule published in the GitHub repository antdigital-ai/agentic-ui (224 stars, last pushed 20d ago), licensed MIT. It adds 824 tokens to every session, about $0.0041 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to clean-code, differing in 41 lines, and is treated as a copy.
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