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/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/commongit clone --depth 1 https://github.com/XingjianTao/Cursor-Rules-for-PyTorch-DeepLearning-BeginnerWrote 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/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/common)<a href="https://agentmods.dev/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/common"><img src="https://agentmods.dev/badge/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/common.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 | $0.00369 | $0.00369 |
| Opus 5 | $0.00185 | $0.00185 |
| Sonnet 5 | $0.00074 | $0.00074 |
| Haiku 4.5 | $0.00037 | $0.00037 |
Grade A, and why
common 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 4d 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
全局代码规范
1. 基本原则
- 优先选择简单方案
- 避免过度设计，用最直接的方式实现需求。
- 严格避免代码重复
- 修改前检查代码库是否已存在相似逻辑，复用现有实现。
2. 环境管理
- 明确区分环境
- 开发环境（
dev）、测试环境（test）、生产环境（prod）必须隔离，禁止混用配置。
- 开发环境（
- 禁用模拟数据滥用
- 仅测试环境允许使用 Mock 数据，开发和生产环境严禁使用。
3. 代码修改准则
- 谨慎修改
- 仅针对明确需求更改，确保理解修改的影响范围。专注任务相关的代码区域。不触碰与任务无关的代码。
- 为所有主要功能编写全面测试。
- 在功能运行良好后，避免对其模式和架构进行重大更改（除非明确要求）
- 始终考虑代码变更可能影的的其他方法和代码区域
- 避免引入新技术
- 优先排查现有方案，若必须引入新逻辑，需同步清理旧代码。
4. 代码整洁性
- 禁止临时脚本驻留
- 一次性脚本（如数据迁移）执行后需立即删除，禁止留存于代码库。
- 控制文件体积
- 单文件代码超过 200~300 行时需重构拆分。
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.
- 4d ago First seen · 39 lines · 369 tokens per session scan A a9fdde34bca1
common is a cursor rule published in the GitHub repository XingjianTao/Cursor-Rules-for-PyTorch-DeepLearning-Beginner (3 stars, last pushed 1y ago), licensed MIT. It adds 369 tokens to every session, about $0.0018 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.
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