feishu-lark-agent

joeseesun/feishu-lark-agent · updated Apr 8, 2026

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$npx skills add https://github.com/joeseesun/feishu-lark-agent --skill feishu-lark-agent
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summary

如果只有姓名:先 user search --name "张三" 获取 open_id,再执行目标操作。

skill.md

Feishu Lark Agent

Run

source ~/.zshrc && python3 ~/.claude/skills/feishu-lark-agent/feishu.py <category> <action> [--key value ...]

ID 格式速查(关键)

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 email 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。


命令速查

消息 msg

msg 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

用户 user

user 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>

文档 doc

doc 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。

多维表格 table

table 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

日历 cal

cal 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。

任务 task

task list  [--completed true]  [--limit 50]
task add   --title "..."  [--due "YYYY-MM-DD"]  [--note "..."]
task done  --id <task_guid>
task delete --id <task_guid>

高频场景(直接复制执行)

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"

错误速查

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
how to use feishu-lark-agent

How to use feishu-lark-agent 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 feishu-lark-agent
2

Execute installation command

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

$npx skills add https://github.com/joeseesun/feishu-lark-agent --skill feishu-lark-agent

The skills CLI fetches feishu-lark-agent from GitHub repository joeseesun/feishu-lark-agent 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/feishu-lark-agent

Reload or restart Cursor to activate feishu-lark-agent. Access the skill through slash commands (e.g., /feishu-lark-agent) 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.

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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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.674 reviews
  • Shikha Mishra· Dec 28, 2024

    feishu-lark-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sophia Rao· Dec 28, 2024

    feishu-lark-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Benjamin Mensah· Dec 28, 2024

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

  • Sakura Shah· Dec 28, 2024

    We added feishu-lark-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Valentina Harris· Dec 16, 2024

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

  • Luis Anderson· Dec 4, 2024

    We added feishu-lark-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Camila Choi· Dec 4, 2024

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

  • Benjamin Diallo· Nov 23, 2024

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

  • Rahul Santra· Nov 19, 2024

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

  • Luis Thomas· Nov 19, 2024

    We added feishu-lark-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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