前置条件： 先阅读 ../lark-shared/SKILL.md 了解认证、身份切换和安全规则。
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlark-openapi-explorerExecute the skills CLI command in your project's root directory to begin installation:
Fetches lark-openapi-explorer from larksuite/cli and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate lark-openapi-explorer. Access via /lark-openapi-explorer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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前置条件： 先阅读
../lark-shared/SKILL.md了解认证、身份切换和安全规则。
当用户的需求无法被现有 skill 或 CLI 已注册 API 覆盖时，使用本技能从飞书官方 markdown 文档库中逐层挖掘原生 OpenAPI 接口，然后通过 lark-cli api 裸调完成任务。
飞书 OpenAPI 文档以 markdown 层级组织：
llms.txt ← 顶层索引，列出所有模块文档链接
└─ llms-<module>.txt ← 模块文档，包含功能概述 + 底层 API 文档链接
└─ <api-doc>.md ← 单个 API 的完整说明（方法/路径/参数/响应/错误码）
文档入口：
| 品牌 | 入口 URL |
|---|---|
| 飞书 (Feishu) | https://open.feishu.cn/llms.txt |
| Lark | https://open.larksuite.com/llms.txt |
所有文档以中文编写。如果用户使用英文交流，需将文档内容翻译为英文后输出。
严格按以下步骤逐层检索，不要跳步或猜测 API：
# 先检查是否已有对应的 skill 或已注册 API
lark-cli <可能的service> --help
如果已有对应命令或 shortcut，直接使用，不需要继续挖掘。
用 WebFetch 获取顶层索引，找到与需求相关的模块文档链接：
WebFetch https://open.feishu.cn/llms.txt
→ 提取问题："列出所有模块文档链接，找出与 <用户需求关键词> 相关的链接"
open.feishu.cnopen.larksuite.com用 WebFetch 获取模块文档，找到具体 API 的文档链接：
WebFetch https://open.feishu.cn/llms-docs/zh-CN/llms-<module>.txt
→ 提取问题："找出与 <用户需求> 相关的 API 说明和文档链接"
用 WebFetch 获取具体 API 文档，提取完整的调用规范：
WebFetch https://open.feishu.cn/document/server-docs/.../<api>.md
→ 提取问题："返回完整 API 规范：HTTP 方法、URL 路径、路径参数、查询参数、请求体字段（名称/类型/必填/说明）、响应字段、所需权限、错误码"
使用 lark-cli api 裸调：
# GET 请求
lark-cli api GET /open-apis/<path> --params '{"key":"value"}'
# POST 请求
lark-cli api POST /open-apis/<path> --data '{"key":"value"}'
# PUT 请求
lark-cli api PUT /open-apis/<path> --data '{"key":"value"}'
# DELETE 请求
lark-cli api DELETE /open-apis/<path>
向用户呈现挖掘结果时，按以下格式组织：
METHOD /open-apis/...lark-cli api 的完整命令如果用户使用英文交流，将以上所有内容翻译为英文。
--dry-run 预览请求（如支持）# Step 1: 确认 CLI 没有封装
lark-cli im --help
# → 发现没有 chat_members 相关的 create 命令
# Step 2-4: 通过文档挖掘获得 API 规范
# → POST /open-apis/im/v1/chats/:chat_id/members
# Step 5: 调用
lark-cli api POST /open-apis/im/v1/chats/oc_xxx/members \
--data '{"id_list":["ou_xxx","ou_yyy"]}' \
--params '{"member_id_type":"open_id"}'
# Step 1: 确认 CLI 没有封装
lark-cli im --help
# → 没有 announcement 相关命令
# Step 2-4: 挖掘文档
# → PATCH /open-apis/im/v1/chats/:chat_id/announcement
# Step 5: 调用
lark-cli api PATCH /open-apis/im/v1/chats/oc_xxx/announcement \
--data '{"revision":"0","requests":["<html>公告内容</html>"]}'
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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lark-openapi-explorer reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for lark-openapi-explorer matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: lark-openapi-explorer is focused, and the summary matches what you get after install.
I recommend lark-openapi-explorer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: lark-openapi-explorer is focused, and the summary matches what you get after install.
lark-openapi-explorer reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend lark-openapi-explorer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend lark-openapi-explorer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in lark-openapi-explorer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
lark-openapi-explorer reduced setup friction for our internal harness; good balance of opinion and flexibility.
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