feishu-automation▌
aaaaqwq/claude-code-skills · updated Apr 8, 2026
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使用 lark-mcp 工具实现飞书平台的全面自动化操作。
飞书全通道自动化
使用 lark-mcp 工具实现飞书平台的全面自动化操作。
核心功能
1. 多维表格(Bitable)
- 创建多维表格和数据表
- 添加、修改、删除字段
- 增删改查记录数据
- 批量导入导出数据
- 数据筛选和排序
2. 消息发送
- 发送文本、富文本、卡片消息
- 群组消息和私聊消息
- 消息模板和交互式卡片
- 文件和图片发送
3. 文档管理
- 搜索云文档
- 创建新文档
- 编辑文档内容
- 文档权限管理
- 文档协作者管理
4. 群组管理
- 创建群组
- 添加/移除成员
- 获取群组列表
- 群组信息查询
5. 知识库(Wiki)
- 搜索知识库节点
- 获取节点详情
- 创建和管理知识库内容
6. 日历和任务
- 创建和查询日历事件
- 创建和管理任务
- 任务分配和跟踪
快速开始
检查 MCP 可用性
// 检查 lark-mcp 工具是否可用
// 可用工具前缀:mcp__lark-mcp_
发送测试消息
// 发送文本消息到群组
await mcp__lark-mcp_sendMessage({
receive_id: "oc_xxxxxxxxx",
msg_type: "text",
content: JSON.stringify({
text: "Hello from Clawdbot!"
})
});
工作流程
数据同步流程
- 连接数据源
- 转换数据格式
- 创建/更新多维表格
- 批量写入数据
- 发送通知
消息推送流程
- 触发事件(定时/事件驱动)
- 构建消息内容
- 获取接收者 ID
- 发送消息
- 记录日志
文档自动化流程
- 获取文档模板
- 填充内容
- 创建新文档
- 设置权限
- 分享给团队
API 工具参考
多维表格相关
createBitable- 创建多维表格createTable- 创建数据表addRecord- 添加记录updateRecord- 更新记录deleteRecord- 删除记录searchRecords- 搜索记录getRecord- 获取记录详情
消息相关
sendMessage- 发送消息getMessages- 获取消息历史replyMessage- 回复消息
文档相关
searchDocs- 搜索文档createDoc- 创建文档getDoc- 获取文档内容updateDoc- 更新文档setDocPermission- 设置文档权限
📄 Markdown 导入云文档(推荐方式)
最佳实践:将本地 Markdown 文件直接导入为飞书云文档,格式完整保留。
# 1. 获取 access_token
TOKEN=$(curl -s -X POST 'https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal' \
-H 'Content-Type: application/json' \
-d '{"app_id":"YOUR_APP_ID","app_secret":"YOUR_APP_SECRET"}' \
| python3 -c "import sys,json; print(json.load(sys.stdin).get('tenant_access_token',''))")
# 2. 上传 md 文件到飞书云盘
FILE_TOKEN=$(curl -s -X POST 'https://open.feishu.cn/open-apis/drive/v1/files/upload_all' \
-H "Authorization: Bearer $TOKEN" \
-F "file_name=document.md" \
-F "parent_type=explorer" \
-F "parent_node=" \
-F "size=$(stat -c%s /path/to/document.md)" \
-F "file=@/path/to/document.md" \
| python3 -c "import sys,json; print(json.load(sys.stdin).get('data',{}).get('file_token',''))")
# 3. 导入为飞书云文档
TICKET=$(curl -s -X POST 'https://open.feishu.cn/open-apis/drive/v1/import_tasks' \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d '{
"file_extension": "md",
"file_token": "'"$FILE_TOKEN"'",
"type": "docx",
"point": {"mount_type": 1, "mount_key": ""}
}' | python3 -c "import sys,json; print(json.load(sys.stdin).get('data',{}).get('ticket',''))")
# 4. 等待导入完成,获取文档链接
sleep 2
curl -s -X GET "https://open.feishu.cn/open-apis/drive/v1/import_tasks/$TICKET" \
-H "Authorization: Bearer $TOKEN" \
| python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('data',{}).get('result',{}).get('url',''))"
支持的导入格式:
md→ Markdowndocx→ Word 文档xlsx→ Excel 表格
注意事项:
- 导入是异步操作,需要轮询
import_tasks/{ticket}获取结果 mount_type: 1表示导入到"我的空间"- 导入后的文档 URL 格式:
https://xxx.feishu.cn/docx/{token}
📤 完整输出云文档流程(标准操作)
每次输出云文档时,必须完成以下步骤:
- 生成本地 Markdown 文件
- 上传到飞书云盘 →
drive/v1/files/upload_all - 导入为云文档 →
drive/v1/import_tasks - 设置权限为组织内可编辑 →
drive/v1/permissions/{token}/public - 发送链接到目标群
# 完整流程脚本
TOKEN=$(curl -s -X POST 'https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal' \
-H 'Content-Type: application/json' \
-d '{"app_id":"APP_ID","app_secret":"APP_SECRET"}' \
| python3 -c "import sys,json; print(json.load(sys.stdin).get('tenant_access_token',''))")
# 1. 上传文件
FILE_TOKEN=$(curl -s -X POST 'https://open.feishu.cn/open-apis/drive/v1/files/upload_all' \
-H "Authorization: Bearer $TOKEN" \
-F "file_name=document.md" \
-F "parent_type=explorer" \
-F "parent_node=" \
-F "size=$(stat -c%s document.md)" \
-F "[email protected]" \
| python3 -c "import sys,json; print(json.load(sys.stdin).get('data',{}).get('file_token',''))")
# 2. 导入为云文档
TICKET=$(curl -s -X POST 'https://open.feishu.cn/open-apis/drive/v1/import_tasks' \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d '{"file_extension":"md","file_token":"'"$FILE_TOKEN"'","type":"docx","point":{"mount_type":1,"mount_key":""}}' \
| python3 -c "import sys,json; print(json.load(sys.stdin).get('data',{}).get('ticket',''))")
sleep 2
# 3. 获取文档 token
DOC_RESULT=$(curl -s -X GET "https://open.feishu.cn/open-apis/drive/v1/import_tasks/$TICKET" \
-H "Authorization: Bearer $TOKEN")
DOC_TOKEN=$(echo "$DOC_RESULT" | python3 -c "import sys,json; print(json.load(sys.stdin).get('data',{}).get('result',{}).get('token',''))")
DOC_URL=$(echo "$DOC_RESULT" | python3 -c "import sys,json; print(json.load(sys.stdin).get('data',{}).get('result',{}).get('url',''))")
# 4. 设置权限:组织How to use feishu-automation on Cursor
AI-first code editor with Composer
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 feishu-automation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches feishu-automation from GitHub repository aaaaqwq/claude-code-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate feishu-automation. Access the skill through slash commands (e.g., /feishu-automation) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★28 reviews- ★★★★★Dhruvi Jain· Dec 24, 2024
I recommend feishu-automation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kofi Brown· Dec 16, 2024
feishu-automation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Oshnikdeep· Nov 15, 2024
Useful defaults in feishu-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dev Sanchez· Nov 15, 2024
Registry listing for feishu-automation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ava Okafor· Nov 7, 2024
feishu-automation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Nia Gupta· Oct 26, 2024
I recommend feishu-automation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Oct 6, 2024
feishu-automation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Nikhil Gupta· Oct 6, 2024
feishu-automation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Mei Shah· Sep 21, 2024
feishu-automation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sakshi Patil· Sep 13, 2024
Keeps context tight: feishu-automation is the kind of skill you can hand to a new teammate without a long onboarding doc.
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