lark-shared

larksuite/cli · updated Apr 8, 2026

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

本技能指导你如何通过lark-cli操作飞书资源, 以及有哪些注意事项。

skill.md

lark-cli 共享规则

本技能指导你如何通过lark-cli操作飞书资源, 以及有哪些注意事项。

配置初始化

首次使用需运行 lark-cli config init 完成应用配置。

当你帮用户初始化配置时,使用background方式使用下面的命令发起配置应用流程,启动后读取输出,从中提取授权链接并发给用户:

# 发起配置(该命令会阻塞直到用户打开链接并完成操作或过期)
lark-cli config init --new

认证

身份类型

两种身份类型,通过 --as 切换:

身份 标识 获取方式 适用场景
user 用户身份 --as user lark-cli auth login 访问用户自己的资源(日历、云空间等)
bot 应用身份 --as bot 自动,只需 appId + appSecret 应用级操作,访问bot自己的资源

身份选择原则

输出的 [identity: bot/user] 代表当前身份。bot 与 user 表现差异很大,需确认身份符合目标需求:

  • Bot 看不到用户资源:无法访问用户的日历、云空间文档、邮箱等个人资源。例如 --as bot 查日程返回 bot 自己的(空)日历
  • Bot 无法代表用户操作:发消息以应用名义发送,创建文档归属 bot
  • Bot 权限:只需在飞书开发者后台开通 scope,无需 auth login
  • User 权限:后台开通 scope + 用户通过 auth login 授权,两层都要满足

权限不足处理

遇到权限相关错误时,根据当前身份类型采取不同解决方案

错误响应中包含关键信息:

  • permission_violations:列出缺失的 scope (N选1)
  • console_url:飞书开发者后台的权限配置链接
  • hint:建议的修复命令

Bot 身份(--as bot

将错误中的 console_url 提供给用户,引导去后台开通 scope。禁止对 bot 执行 auth login

User 身份(--as user

lark-cli auth login --domain <domain>           # 按业务域授权
lark-cli auth login --scope "<missing_scope>"   # 按具体 scope 授权(推荐,符合最小权限原则)

规则:auth login 必须指定范围(--domain--scope)。多次 login 的 scope 会累积(增量授权)。

Agent 代理发起认证(推荐)

当你作为 AI agent 需要帮用户完成认证时,使用background方式 执行以下命令发起授权流程, 并将授权链接发给用户:

# 发起授权(阻塞直到用户授权完成或过期)
lark-cli auth login --scope "calendar:calendar:readonly"

更新检查

lark-cli 命令执行后,如果检测到新版本,JSON 输出中会包含 _notice.update 字段(含 messagecommand 等)。

当你在输出中看到 _notice.update 时,完成用户当前请求后,主动提议帮用户更新

  1. 告知用户当前版本和最新版本号
  2. 提议执行更新(CLI 和 Skills 需要同时更新):
    npm update -g @larksuite/cli && npx skills add larksuite/cli -g -y
    
  3. 更新完成后提醒用户:退出并重新打开 AI Agent以加载最新 Skills

规则:不要静默忽略更新提示。即使当前任务与更新无关,也应在完成用户请求后补充告知。

安全规则

  • 禁止输出密钥(appSecret、accessToken)到终端明文。
  • 写入/删除操作前必须确认用户意图
  • --dry-run 预览危险请求。
how to use lark-shared

How to use lark-shared 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-shared
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-shared

The skills CLI fetches lark-shared 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-shared

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

GET_STARTED →

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

4.738 reviews
  • Kabir Mehta· Dec 12, 2024

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

  • Li Johnson· Dec 8, 2024

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

  • Henry Huang· Nov 27, 2024

    lark-shared is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Harper Diallo· Nov 3, 2024

    Keeps context tight: lark-shared is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kabir Anderson· Oct 22, 2024

    lark-shared is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Camila Abbas· Oct 18, 2024

    Keeps context tight: lark-shared is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aarav Ramirez· Sep 21, 2024

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

  • Piyush G· Sep 13, 2024

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

  • Aisha Yang· Sep 13, 2024

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

  • Aarav Menon· Aug 12, 2024

    lark-shared has been reliable in day-to-day use. Documentation quality is above average for community skills.

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