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。
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlark-sharedExecute the skills CLI command in your project's root directory to begin installation:
Fetches lark-shared from larksuite/cli 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 lark-shared. Access via /lark-shared 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.
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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
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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。
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
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 等 |
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 |
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:
--as bot 查日程返回 bot 自己的(空)日历auth loginauth login 授权,两层都要满足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--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。
--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 或 --scope)。多次 login 的 scope 会累积(增量授权)。
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"
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 message、command 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:
npm update -g @larksuite/cli && npx skills add larksuite/cli -g -y
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。
--dry-run 预览危险请求。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
pproenca/dot-skills
mattpocock/skills
We added lark-shared from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
lark-shared fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
lark-shared is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: lark-shared is the kind of skill you can hand to a new teammate without a long onboarding doc.
lark-shared is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: lark-shared is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: lark-shared is focused, and the summary matches what you get after install.
I recommend lark-shared for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in lark-shared — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
lark-shared has been reliable in day-to-day use. Documentation quality is above average for community skills.
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