Token导航 LogoToken导航

Agent Skill · 研究检索

feishu-master(飞书大师)

feishu-master 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

feishu-master 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 15,226。

总安装 7,999周安装 330GitHub Stars 公开资料未说明下载 2,614核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:feishu-master(飞书大师)
来源仓库:https://github.com/soraclub/feishu-master
安装命令:
openclaw skills install feishu-master
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install feishu-master

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

飞书 API 人工智能工具扩展,使人工智能工具能够实时利用飞书功能。适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。逐步扩展的技能,支持多种飞书功能的集成。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。适用于高级飞书功能集成场景。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install feishu-master;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

feishu-master 怎么安装?

在终端执行 openclaw skills install feishu-master。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

feishu-master 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

feishu-master 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

feishu-master 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

feishu-master 用的人多吗?

公开数据是累计安装 7,999 次,近一周安装 330 次,下载 2,614 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

feishu-master 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
feishu-master

飞书大师 · 本页

7,999—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
feishu-master
description
AI Tool Extension for Feishu API - A progressively extensible skill that enables AI tools (OpenClaw, Claude Code, etc.) to leverage Feishu capabilities on-demand. Only implements APIs when needed, avoiding premature development.

Feishu Skill - AI Tool Extension for Feishu API

A progressively extensible skill designed for AI tools to leverage Feishu capabilities. The core philosophy is implement only what you need, when you need it.

⚠️ CRITICAL: After Creating New Scripts

You MUST update scripts/script_index.json immediately after creating any new script.

Failure to update the index will cause:

  • Future AI lookup cannot find the newly created script
  • Duplicate implementation of the same functionality
  • Confusion for other AI tools using this skill

Action required: Add an entry to scripts/script_index.json with fields: name, description, usage_hint, added_date, tags

Design Philosophy

This skill is designed specifically for AI tools, not for human manual use.

When an AI tool receives a task involving Feishu:

  1. Check existing scripts first - Search scripts/script_index.json for already-implemented APIs
  2. Implement only if needed - If no match exists, use Context7 to query Feishu OpenAPI documentation
  3. Add to index - Once implemented, update the index to avoid future re-implementation

This approach ensures:

  • No wasted effort - Only implement APIs when actually needed
  • Progressive expansion - The skill grows based on real usage patterns
  • Efficient lookup - AI tools can quickly find what's already available

Quick Start

This skill works out of the box once authentication is configured:

# Configure authentication (one-time setup)
cd scripts/env
echo '{"app_id": "your_app_id", "app_secret": "your_app_secret"}' > app.json

AI Workflow

Decision Logic

When AI encounters a Feishu-related task, follow this decision tree:

Step 1: Check index file

Read scripts/script_index.json
Search by: description, tags

If match found:

  • Run python3 script.py --help to understand parameters
  • Execute the script
  • Return result

If NO match found:

  • Use Context7 to query Feishu API documentation:
  Library: open.feishu.cn / larksuite
  Query: "[specific requirement, e.g., 如何获取群组成员列表]"
  • Implement new Python script based on documentation
  • Execute and test
  • IMPORTANT: Update scripts/script_index.json to add the new script

If execution fails:

  • Check API error messages
  • Use Context7 or references/doc_urls.txt to consult documentation
  • Fix the script
  • Re-test

Critical Decision Points

When deciding between "use existing" vs "implement new", consider:

  • Is there a semantic match? Even if parameter names differ, if the functionality is equivalent, use existing script
  • What's the cost of implementing? Simple single-call APIs → implement immediately. Complex multi-call workflows → consider reusing existing scripts
  • Can this compose from existing scripts? Sometimes new features can be achieved by combining multiple existing scripts

Technical Reference

Script Index Format

scripts/script_index.json structure:

{
  "version": "1.0",
  "last_updated": "2026-02-12T12:00:00Z",
  "scripts": [
    {
      "name": "get_group_members",
      "description": "获取指定群组的人员列表",
      "usage_hint": "python3 get_group_members.py <open_chat_id>",
      "added_date": "2026-02-12",
      "tags": ["group", "member", "user"]
    }
  ]
}

Key fields for AI search:

  • description: Human-readable explanation of functionality
  • tags: Array of keywords for matching (prioritize these for fuzzy matching)

Authentication

Configuration file: scripts/env/app.json

{
  "app_id": "cli_xxxxxxxxxxxxxxxx",
  "app_secret": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}

To obtain credentials:

  1. Login to Feishu Open Platform
  2. Create or select an application
  3. Get App ID and App Secret from application details

Token management: get_token.py handles token lifecycle:

  • Automatic caching during validity period
  • Auto-refresh 5 minutes before expiration
  • Cache file: scripts/env/token_cache.json

Script Template

When implementing new APIs, use this template:

#!/usr/bin/env python3
"""
[Complete functional description]

Usage:
    python3 script.py <param1> <param2> [options]

Parameters:
    param1: Parameter description
    param2: Parameter description

Options:
    --page-size: Page size (default: 20)
    --page-token: Pagination token

Output:
    JSON format output:
    {
      "code": 0,
      "msg": "success",
      "data": { ... }
    }

References:
    Feishu API: https://open.feishu.cn/document/...
"""

import sys
import json
import subprocess
from pathlib import Path
import requests

# Configuration
TOKEN_SCRIPT = Path(__file__).parent / "get_token.py"
BASE_URL = "https://open.feishu.cn"

def get_token():
    """Get Feishu access token"""
    result = subprocess.run(
        [sys.executable, str(TOKEN_SCRIPT)],
        capture_output=True,
        text=True,
        check=True
    )
    return result.stdout.strip()

def api_function(param1, param2, token, **kwargs):
    """API call function"""
    headers = {"Authorization": f"Bearer {token}"}
    # ... implement API call
    pass

def main():
    # Handle --help
    if "--help" in sys.argv or "-h" in sys.argv:
        print(__doc__)
        return

    # Parse required parameters
    if len(sys.argv) < 3:
        print(__doc__, file=sys.stderr)
        sys.exit(1)

    param1 = sys.argv[1]
    param2 = sys.argv[2]
    kwargs = {}

    # Parse optional parameters
    for i in range(3, len(sys.argv)):
        if sys.argv[i].startswith("--"):
            key = sys.argv[i][2:].replace("-", "_")
            i += 1
            if i < len(sys.argv):
                kwargs[key] = sys.argv[i]

    # Get token
    token = get_token()

    # Call API
    result = api_function(param1, param2, token, **kwargs)

    # Output JSON
    print(json.dumps(result, indent=2, ensure_ascii=False))

if __name__ == "__main__":
    main()

Development standards:

  1. ⚠️ CRITICAL: After creating any new script, you MUST update scripts/script_index.json immediately
  2. Complete docstring with Usage, Parameters, Options, Output sections
  3. Support --help parameter
  4. Use get_token.py for authentication
  5. Output standard JSON format
  6. Proper error handling with friendly messages

📋 New Script Creation Checklist

When implementing a new API, you MUST complete ALL steps:

  • [ ] Query Context7 for API documentation
  • [ ] Create the Python script following the template
  • [ ] Test the script execution (verify it returns correct JSON)
  • [ ] ⚠️ UPDATE scripts/script_index.json ← Don't forget this!
  • [ ] Save the API documentation URL to references/doc_urls.txt

Using Context7 for Feishu API

When implementing new APIs, query Feishu documentation via Context7:

Library ID: open.feishu.cn / larksuite
Query examples:
- "如何获取群组成员列表"
- "发送消息到飞书群组的 API 使用方法"
- "飞书文档操作的 OpenAPI"

Why Context7?

  • Provides up-to-date API documentation
  • Includes code examples and parameters
  • Faster than searching the web manually
  • Reduces trial-and-error

Examples

AI Task Example 1: Get Group Members

User request: "Get the member list for group oc_xxxxxxxxxxxxx"

AI decision process:

  1. Search scripts/script_index.json → Found match: get_group_members
  2. Execute: python3 scripts/get_group_members.py oc_xxxxxxxxxxxxx
  3. Return result

AI Task Example 2: Add New API Capability

User request: "Delete a message from the group"

AI decision process:

  1. Search scripts/script_index.json → No match
  2. Context7 query: "飞书删除消息 API"
  3. Get documentation showing message/delete endpoint
  4. Implement delete_message.py script
  5. Test execution
  6. ⛔ CRITICAL STEP: Update scripts/script_index.json with new entry - DO NOT skip this!
  7. Return result

Why step 6 is critical:

  • If you skip it, next AI will NOT find delete_message.py in the index
  • This causes duplicate work: same functionality will be re-implemented
  • The progressive expansion philosophy breaks down

References

平台分布

OpenClaw

93.26%
按下载量换算2,438

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills