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/holtwood/awesome-cursorrules-zhWrote 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/holtwood/awesome-cursorrules-zh/llm-integration-rules)<a href="https://agentmods.dev/rules/holtwood/awesome-cursorrules-zh/llm-integration-rules"><img src="https://agentmods.dev/badge/rules/holtwood/awesome-cursorrules-zh/llm-integration-rules.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 5.1 | $0.00000 | $0.00223 |
| Opus 5 | $0.00000 | $0.00112 |
| Sonnet 5 | $0.00000 | $0.00045 |
| Haiku 4.5 | $0.00000 | $0.00022 |
Grade A, and why
llm-integration-rules 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
- Python 包装器：使用专门的 Python 包装器进行 LLM 交互，确保接口统一和易于管理。
- API 端点：通过清晰定义的 API 端点连接前端与 Python 后端，实现前后端数据传输和功能调用。
- 异步处理：LLM 调用通常耗时较长，应采用异步处理机制，避免阻塞主线程。
- 错误处理与重试：实现健壮的错误处理和重试机制，应对 LLM 服务可能出现的临时故障。
- 安全性：确保 LLM 交互过程中的数据安全，例如敏感信息加密、API 密钥管理等。
- 性能优化：对 LLM 请求和响应进行优化，例如批量请求、数据压缩等，提升整体性能。
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 · 11 lines · 0 tokens per session scan A b4251ea9d86c
llm-integration-rules is a cursor rule published in the GitHub repository holtwood/awesome-cursorrules-zh (232 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 223 tokens. 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 2026-09-03.
Other cursor rules, from other repositories
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Cursor rules for Next.js development with Type LLM integration.
dataflow-json-schema
When using jsonschema in FormatStrPromptedGenerator, PromptedGenerator, or operators.json to constrain LLM structured output.
react-router
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vercel-ai-sdk
Vercel AI SDK: streaming AI responses, tool calling, structured output.
ai-security
Security best practices for LLM/AI applications - prompt injection defense, rate limiting, PII protection.
prompt-engineering
When we are creating prompts that will be used by an LLM for agents.