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 commands/nongjun/feishu-cursor-claw/aigit 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/commands/nongjun/feishu-cursor-claw/ai)<a href="https://agentmods.dev/commands/nongjun/feishu-cursor-claw/ai"><img src="https://agentmods.dev/badge/commands/nongjun/feishu-cursor-claw/ai.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 | $0.00916 |
| Opus 5 | $0.00000 | $0.00458 |
| Sonnet 5 | $0.00000 | $0.00183 |
| Haiku 4.5 | $0.00000 | $0.00092 |
Grade A, and why
团队基线总结-企微Ai搭子 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 5d 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
团队基线总结 - 企微AI搭子
当用户调用此命令时，对刚才的问题解决过程进行复盘，提炼可复用的规则，并录入团队基线文档。
触发条件
- 刚解决了一个耗时较长或反复试错的问题
- 踩了一个"本可避免"的坑
- 发现了一个"如果当初知道就好了"的知识点
操作步骤
1. 复盘问题本质
分析刚才的问题：
- 根因是什么：不是"做错了什么"，而是"为什么会做错"
- 时间花在哪：哪个环节耗时最多？是排查方向错误还是知识盲区？
- 触发条件：什么情况下会遇到这个问题？
2. 提炼可复用规则
将经验转化为可直接执行的规则，遵循以下原则：
- 一句话说清：如果需要解释，说明还没提炼到位
- 有明确触发点：在什么情况下应该想起这条规则
- 有具体动作：告诉未来的自己"做什么"而非"别做什么"
规则格式模板：
### {序号}. {规则标题}（{日期} 新增）
**现象**：{什么时候会遇到}
**根因**：{为什么会发生}
**规则**：{应该怎么做}
3. 检查现有规则（必须先做！）
在写入之前，必须先搜索团队基线文档，检查是否已有类似规则：
grep -n "关键词" /root/企微SCRM/文档/核心信念/团队基线.md
根据搜索结果决定下一步：
| 搜索结果 | 动作 |
|---|---|
| 已有完全相同的规则 | 不添加，告知用户"已有规则 #XX" |
| 已有类似规则但不完整 | 补充现有规则，而非新增 |
| 已有相关规则可合并 | 合并到现有规则下作为子场景 |
| 确实没有相关规则 | 继续下一步，新增规则 |
4. 确定规则归属分类
根据问题类型，将规则放入正确的分类：
| 问题类型 | 归属分类 |
|---|---|
| 前后端接口/响应格式 | 前后端交互规范 |
| 企微API/权限/回调 | 对应企微相关分类 |
| Docker/部署/端口 | Docker 部署避坑规范 |
| 前端状态/组件/路由 | 前端状态管理规范 |
| Nginx/代理/502 | Nginx 路由规范 或 调试排查规范 |
| 公共模块/复用 | 公共模块集成避坑规范 |
| 新分类 | 创建新的 ## 分类名称 章节 |
5. 写入团队基线文档
将提炼的规则追加到文档：
- 目标文件：
/root/企微SCRM/文档/核心信念/团队基线.md - 序号规则：查看目标分类下最后一条规则的序号，新规则序号 = 最大序号 + 1
- 日期标注：在规则标题后添加
（YYYY-MM-DD 新增）
6. 输出确认
完成后向用户展示：
- 问题复盘：一句话概括根因
- 新增规则：展示写入的规则内容
- 规则序号：告知在文档中的位置
注意事项
- 不要录入"太具体"的规则（如某个变量名拼错），要提炼出普适性
- 不要录入"太抽象"的规则（如"要仔细"），要有可执行的动作
- 优先合并到已有规则，而非新增重复规则
- 规则应该让一个月后的自己或新同事能直接受益
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.
- 5d ago First seen · 87 lines · 0 tokens per session scan A df2a2d20b10c
团队基线总结-企微Ai搭子 is a command published in the GitHub repository nongjun/feishu-cursor-claw (14 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 916 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 2026-08-30.
Other commands, from other repositories
handoff
Compact the current conversation into a handoff document for a fresh agent session.
context
Dynamic context management — status, clean, log, save, read.
init-workspace-flow-questions
Phase 8 Questions of init-workspace-flow.
kiro-steering-custom
Command "kiro-steering-custom" from gotalab/cc-sdd, covering kiro custom steering creation, workflow, available templates, steering principles and tool guidance.
review-code
Read ALL memory bank code rules + best practices, check the files that changed, and APPLY fixes so they adhere. The active counterpart to /scan (which is read-only). Use after an AI session, before commit, to make changed files compliant.
wiki-synthesize
Synthesize wiki/sources/ .md pages from raw session transcripts using the configured LLM backend.