如果只有姓名：先 user search --name "张三" 获取 open_id，再执行目标操作。
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfeishu-lark-agentExecute the skills CLI command in your project's root directory to begin installation:
Fetches feishu-lark-agent from joeseesun/feishu-lark-agent 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 feishu-lark-agent. Access via /feishu-lark-agent 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
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
52
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
52
stars
source ~/.zshrc && python3 ~/.claude/skills/feishu-lark-agent/feishu.py <category> <action> [--key value ...]
| 前缀 | 类型 | 用途 |
|---|---|---|
ou_ |
open_id | 用户 ID，用于 --to |
oc_ |
chat_id | 群聊 ID，用于 --chat |
| 邮箱 | 用于 --email，自动解析为 open_id |
|
docx/xxx URL 后半段 |
document_id | 飞书文档 ID |
base/xxx URL 后半段 |
app_token | 多维表格 App Token |
如果只有姓名：先 user search --name "张三" 获取 open_id，再执行目标操作。
msgmsg send --to <open_id|chat_id> --text "..." # open_id → ou_, chat_id → oc_
msg send --email <email> --text "..." # 用邮箱发
msg send --chat <chat_id> --text "..." # 发群
msg send --to <open_id> --file /path # 发文件内容
msg reply --to <message_id> --text "..."
msg history --chat <chat_id> [--limit 20]
msg search --query "关键词" [--limit 20]
msg chats [--limit 50] # 列出所有群聊
useruser search --name "张三" # 模糊搜索，返回 open_id
user get --email <email> # 精确查询
user get --id <open_id>
docdoc create --title "标题" [--content "markdown内容"] [--file /path.md]
doc get --id <document_id> # 从 URL feishu.cn/docx/DOC_ID 取
doc list [--folder <token>] [--limit 50]
创建后自动授权
FEISHU_OWNER_OPEN_ID编辑权限。
tabletable tables --app <token> # 先列出所有 table
table fields --app <token> --table <id> # 必须先查字段！
table records --app <token> --table <id> [--filter 'AND(CurrentValue.[状态]="进行中")'] [--limit 100]
table add --app <token> --table <id> --data '{"字段名":"值"}'
table update --app <token> --table <id> --record <recXXX> --data '{"字段名":"新值"}'
table delete --app <token> --table <id> --record <recXXX>
App token 在 URL 中：
feishu.cn/base/APP_TOKEN
calcal calendars # ⚠️ 第一步：列出 Bot 可访问的日历，获取 cal_id
cal list --calendar <cal_id> [--days 7]
cal add --calendar <cal_id> --title "..." --start "YYYY-MM-DD HH:MM" --end "YYYY-MM-DD HH:MM" [--location "..."] [--attendees "[email protected],[email protected]"]
cal delete --calendar <cal_id> --id <event_id>
⚠️ 重要：Bot 无法访问个人日历（
primary会报错）。必须先cal calendars获取 Bot 自己的日历 ID，再用该 ID 操作。
tasktask list [--completed true] [--limit 50]
task add --title "..." [--due "YYYY-MM-DD"] [--note "..."]
task done --id <task_guid>
task delete --id <task_guid>
1. 按姓名发消息
# step1 获取 open_id
python3 feishu.py user search --name "张三"
# step2 发消息
python3 feishu.py msg send --to ou_xxx --text "你好"
2. 给群发消息（已知群名 → 先查 chat_id）
python3 feishu.py msg chats # 找到目标群的 chat_id (oc_xxx)
python3 feishu.py msg send --chat oc_xxx --text "通知内容"
3. 添加多维表记录（先查字段避免字段名写错）
python3 feishu.py table fields --app APP_TOKEN --table TABLE_ID
python3 feishu.py table add --app APP_TOKEN --table TABLE_ID --data '{"标题":"xxx","状态":"进行中"}'
4. 创建文档并写入内容
python3 feishu.py doc create --title "会议记录" --content "# 会议记录\n\n## 议题\n- 内容"
5. 查看近期日程（先查可用日历）
python3 feishu.py cal calendars # 获取 Bot 的日历 ID
python3 feishu.py cal list --calendar <cal_id> --days 7
6. 创建日程（先查日历 ID）
python3 feishu.py cal calendars # 先获取 cal_id
python3 feishu.py cal add --calendar <cal_id> --title "周会" --start "2026-03-20 10:00" --end "2026-03-20 11:00"
| 错误码 | 原因 | 解决 |
|---|---|---|
99991671 |
权限未开通 | 飞书开放平台添加权限 |
230006 |
日历权限缺失 | 开通 calendar:calendar |
1254043 |
Bitable 未找到 | 检查 URL 中的 app_token |
191001 |
日历 ID 错误 | 不能用 primary，用真实 cal_id |
Missing FEISHU_APP_ID |
环境变量未加载 | source ~/.zshrc |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
feishu-lark-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
feishu-lark-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
feishu-lark-agent reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added feishu-lark-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in feishu-lark-agent — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added feishu-lark-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend feishu-lark-agent for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
feishu-lark-agent reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for feishu-lark-agent matched our evaluation — installs cleanly and behaves as described in the markdown.
We added feishu-lark-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
showing 1-10 of 74