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lark-openapi-explorer▌

larksuite/cli · updated Apr 8, 2026

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$npx skills add https://github.com/larksuite/cli --skill lark-openapi-explorer
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summary

前置条件： 先阅读 ../lark-shared/SKILL.md 了解认证、身份切换和安全规则。

skill.md

OpenAPI Explorer

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。

文档库结构

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。

挖掘流程

严格按以下步骤逐层检索，不要跳步或猜测 API：

Step 1：确认现有能力不足

# 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。

Step 2：从顶层索引定位模块

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"
  • 飞书品牌使用 open.feishu.cn
  • Lark 品牌使用 open.larksuite.com
  • 如不确定用户品牌，默认使用飞书

Step 3：从模块文档定位具体 API

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"

Step 4：获取 API 完整规范

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"

Step 5：通过 CLI 调用 API

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>

输出规范

向用户呈现挖掘结果时，按以下格式组织：

  1. API 名称与功能：一句话描述
  2. HTTP 方法与路径：METHOD /open-apis/...
  3. 关键参数：列出必填和常用可选参数
  4. 所需权限：scope 列表
  5. 调用示例：给出 lark-cli api 的完整命令
  6. 注意事项：频率限制、特殊约束等

如果用户使用英文交流，将以上所有内容翻译为英文。

安全规则

  • 写入/删除类 API（POST/PUT/DELETE）调用前必须确认用户意图
  • 建议先用 --dry-run 预览请求（如支持）
  • 不要猜测 API 路径或参数——必须从文档中获取确认
  • 涉及敏感操作（删除群、移除成员等）时，向用户说明影响范围

使用场景示例

场景 1：用户需要拉人进群（未被 CLI 封装）

# 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"}'

场景 2：用户需要设置群公告

# 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>"]}'

参考

how to use lark-openapi-explorer

How to use lark-openapi-explorer on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add lark-openapi-explorer
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/larksuite/cli --skill lark-openapi-explorer

The skills CLI fetches lark-openapi-explorer from GitHub repository larksuite/cli and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/lark-openapi-explorer

Reload or restart Cursor to activate lark-openapi-explorer. Access the skill through slash commands (e.g., /lark-openapi-explorer) or your agent's skill management interface.

⚠

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client with skill support
  • ›Clear understanding of task or problem to solve
  • ›Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • ⚠Expecting perfect results without iteration
  • ⚠Not providing enough context in prompts
  • ⚠Using skill for tasks outside its intended scope
  • ⚠Accepting outputs without review and validation

Best Practices▌

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • −Don't use without understanding skill limitations
  • −Don't skip validation of outputs
  • −Don't share sensitive information in prompts
  • −Don't expect skill to replace human judgment

💡 Pro Tips

  • ★Be specific about desired format and style
  • ★Ask for multiple options to choose from
  • ★Request explanations to understand reasoning
  • ★Combine AI efficiency with human expertise

When to Use This▌

✓ 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.

Learning Path▌

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

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general reviews

Ratings

4.6★★★★★50 reviews
  • ★★★★★Shikha Mishra· Dec 28, 2024

    lark-openapi-explorer reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Mateo Khanna· Dec 16, 2024

    Registry listing for lark-openapi-explorer matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Mateo Dixit· Dec 12, 2024

    Solid pick for teams standardizing on skills: lark-openapi-explorer is focused, and the summary matches what you get after install.

  • ★★★★★Nikhil Malhotra· Dec 12, 2024

    I recommend lark-openapi-explorer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Ganesh Mohane· Dec 8, 2024

    Solid pick for teams standardizing on skills: lark-openapi-explorer is focused, and the summary matches what you get after install.

  • ★★★★★Amelia Gonzalez· Dec 4, 2024

    lark-openapi-explorer reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Ishan Farah· Nov 23, 2024

    I recommend lark-openapi-explorer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Yash Thakker· Nov 19, 2024

    I recommend lark-openapi-explorer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Sofia Gupta· Nov 7, 2024

    Useful defaults in lark-openapi-explorer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★James Menon· Nov 3, 2024

    lark-openapi-explorer reduced setup friction for our internal harness; good balance of opinion and flexibility.

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