lark-task▌
larksuite/cli · updated Apr 8, 2026
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CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理
task (v2)
CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理
搜索技巧:如果用户的查询只指定了任务名称(例如“完成任务龙虾一号”),请直接使用
+get-my-tasks --query "龙虾一号"命令搜索(不要带--complete参数,这样可以同时搜索未完成和已完成的任务)。 用户身份识别:在用户身份(user identity)场景下,如果用户提到了“我”(例如“分配给我”、“由我创建”),请默认获取当前登录用户的open_id作为对应的参数值。 术语理解:如果用户提到 “todo”(待办),应当思考其是否是指“task”(任务),并优先尝试使用本 Skill 提供的命令来处理。 友好输出:在输出任务(或清单)的执行结果给用户时,建议同时提取并输出命令返回结果中的url字段(任务链接),以便用户可以直接点击跳转查看详情。
创建/更新注意:
- 只有在设置了
due(截止时间)的情况下,才能设置repeat_rule(重复规则)和reminder(提醒时间)。- 若同时设置了
start(开始时间)和due(截止时间),开始时间必须小于或等于截止时间。- 使用 tenant_access_token(应用身份)时,无法跨租户添加任务成员。
查询注意:
- 在输出任务详情时,如果需要渲染负责人、创建人等人员字段,除了展示
id(例如 open_id) 外,还必须通过其他方式(例如调用通讯录技能)尝试获取并展示这个人的真实名字,以便用户更容易识别。- 在输出任务详情时,如果需要渲染创建时间、截止时间等字段,需要使用本地时区来渲染(格式为2006-01-02 15:04:05)。
Shortcuts
+create— Create a task+update— Update a task+comment— Add a comment to a task+complete— Complete a task+reopen— Reopen a task+assign— Assign or remove members from a task+followers— Manage task followers+reminder— Manage task reminders+get-my-tasks— List tasks assigned to me+tasklist-create— Create a tasklist and batch add tasks+tasklist-task-add— Add existing tasks to a tasklist+tasklist-members— Manage tasklist members
API Resources
lark-cli schema task.<resource>.<method> # 调用 API 前必须先查看参数结构
lark-cli task <resource> <method> [flags] # 调用 API
重要:使用原生 API 时,必须先运行
schema查看--data/--params参数结构,不要猜测字段格式。
tasks
create— 创建任务delete— 删除任务get— 获取任务详情list— 列取任务列表patch— 更新任务
tasklists
add_members— 添加清单成员create— 创建清单delete— 删除清单get— 获取清单详情list— 获取清单列表patch— 更新清单remove_members— 移除清单成员tasks— 获取清单任务列表
subtasks
create— 创建子任务list— 获取任务的子任务列表
members
add— 添加任务成员remove— 移除任务成员
权限表
| 方法 | 所需 scope |
|---|---|
tasks.create |
task:task:write |
tasks.delete |
task:task:write |
tasks.get |
task:task:read |
tasks.list |
task:task:read |
tasks.patch |
task:task:write |
tasklists.add_members |
task:tasklist:write |
tasklists.create |
task:tasklist:write |
tasklists.delete |
task:tasklist:write |
tasklists.get |
task:tasklist:read |
tasklists.list |
task:tasklist:read |
tasklists.patch |
task:tasklist:write |
tasklists.remove_members |
task:tasklist:write |
tasklists.tasks |
task:tasklist:read |
subtasks.create |
task:task:write |
subtasks.list |
task:task:read |
members.add |
task:task:write |
members.remove |
task:task:write |
How to use lark-task 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 lark-task
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches lark-task from GitHub repository larksuite/cli 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 lark-task. Access the skill through slash commands (e.g., /lark-task) 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
4.5★★★★★70 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
Solid pick for teams standardizing on skills: lark-task is focused, and the summary matches what you get after install.
- ★★★★★Mateo Agarwal· Dec 24, 2024
I recommend lark-task for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Camila Li· Dec 12, 2024
Registry listing for lark-task matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Fatima Ndlovu· Dec 12, 2024
lark-task reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ira Khanna· Dec 8, 2024
lark-task reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Rahul Santra· Nov 27, 2024
lark-task is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Amelia Jain· Nov 27, 2024
I recommend lark-task for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Oshnikdeep· Nov 19, 2024
We added lark-task from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Zaid Abbas· Nov 15, 2024
lark-task reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Jin Gonzalez· Nov 3, 2024
Useful defaults in lark-task — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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