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/nongjun/feishu-cursor-clawWrote 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/nongjun/feishu-cursor-claw/cursor-capabilities)<a href="https://agentmods.dev/rules/nongjun/feishu-cursor-claw/cursor-capabilities"><img src="https://agentmods.dev/badge/rules/nongjun/feishu-cursor-claw/cursor-capabilities/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/nongjun/feishu-cursor-claw/cursor-capabilities"><img src="https://agentmods.dev/badge/rules/nongjun/feishu-cursor-claw/cursor-capabilities.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 | $0.00541 | $0.00541 |
| Opus 5.5 | $0.00216 | $0.00216 |
| Sonnet 5.5 | $0.00108 | $0.00108 |
| Haiku 4.5 | $0.00054 | $0.00054 |
Grade A, and why
cursor-capabilities 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.
What it actually says
Cursor Agent 能力引导
完整的工具清单见 .cursor/TOOLS.md。这里只列出遇到具体需求时该怎么做。
决策树
用户说"帮我查XX最新价格/新闻/信息"
→ WebSearch 搜索 → 返回结果
用户说"帮我看看这个网站/链接"
→ WebFetch 抓取内容 → 分析并回复
用户说"每天早上9点提醒我XX"
→ 编辑 cron-jobs.json 创建定时任务（详见 scheduler-protocol.mdc）
用户说"监控服务器/检查某个状态"
→ 创建定时任务，message 里写 Shell 检查命令
用户说"当XX发生时通知我"
→ 创建定时轮询任务（每N分钟检查一次条件）
用户说"帮我整理这个文件夹"
→ Glob 列出文件 → Read 读取 → Write/Shell 整理
用户说"帮我写个脚本/程序"
→ Write 创建文件 → Shell 测试运行
用户说"记住XX / 以后注意XX"
→ 写入 .cursor/memory/YYYY-MM-DD.md 或 .cursor/MEMORY.md
用户说"帮我在网页上操作/填表/点击"
→ Browser MCP（browser_navigate → browser_snapshot → browser_click/fill）
用户说"这个任务很复杂/需要同时处理多个事"
→ 启动多个子 Agent 并行处理
三条原则
- 先行动后汇报 — 不要问"要不要我帮你搜索？"，直接搜。
- 组合能力 — 复杂任务通过组合多个工具完成（Shell + 网络 + 文件 + 定时）。
- 不说做不到 — 如果看起来不可能，想想能否通过工具组合实现。
输出格式约束
回复通过飞书卡片展示，受以下限制：
- 单张卡片最多 5 个 Markdown 表格，建议不超过 4 个
- 卡片总大小 30KB（约 3500-4000 中文字符）
- 超限内容会被截断或渲染失败
应对策略：内容多时主动分次回复，少用表格多用列表，先给结论再展开。
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 · 59 lines · 541 tokens per session scan A c93a8c087aeb
cursor-capabilities is a cursor rule published in the GitHub repository nongjun/feishu-cursor-claw (14 stars, last pushed 3mo ago), licensed MIT. It adds 541 tokens to every session, about $0.0022 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 2026-08-30.
Other cursor rules, from other repositories
code-optimization
Guidelines for optimizing duplicate and poorly structured code.
guardrails
Security guardrails applied to all AI interactions.
documentation-standards
Documentation standards for code, APIs, and architecture.
data-fetching
Data fetching patterns with TanStack Query, Server Components, and Supabase.
token-efficiency
Token optimization rules to reduce AI costs by 30%+.
new_features
Guidelines for integrating new features into the Task Master CLI.