lark-task

larksuite/cli · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

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

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

skill.md

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

  1. 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)。
  2. 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。
  3. 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

  1. 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。
  2. 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

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

How to use lark-task 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-task
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-task

The skills CLI fetches lark-task 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-task

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

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)
  • No comments yet — start the thread.
general reviews

Ratings

4.570 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.

showing 1-10 of 70

1 / 7