lark-wiki▌
larksuite/cli · updated Apr 17, 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
wiki (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
API Resources
lark-cli schema wiki.<resource>.<method> # 调用 API 前必须先查看参数结构
lark-cli wiki <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。
spaces
get— 获取知识空间信息get_node— 获取知识空间节点信息list— 获取知识空间列表
nodes
copy— 创建知识空间节点副本create— 创建知识空间节点list— 获取知识空间子节点列表
权限表
| 方法 | 所需 scope |
|---|---|
spaces.get |
wiki:space:read |
spaces.get_node |
wiki:node:read |
spaces.list |
wiki:space:retrieve |
nodes.copy |
wiki:node:copy |
nodes.create |
wiki:node:create |
nodes.list |
wiki:node:retrieve |
How to use lark-wiki 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-wiki
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches lark-wiki 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-wiki. Access the skill through slash commands (e.g., /lark-wiki) 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
4.6★★★★★50 reviews- ★★★★★Alexander Mehta· Dec 28, 2024
We added lark-wiki from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Chinedu Martinez· Dec 16, 2024
Keeps context tight: lark-wiki is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ganesh Mohane· Dec 12, 2024
lark-wiki fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chinedu Ghosh· Dec 8, 2024
I recommend lark-wiki for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Alexander Singh· Nov 19, 2024
lark-wiki reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Neel Rao· Nov 7, 2024
lark-wiki has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakshi Patil· Nov 3, 2024
Registry listing for lark-wiki matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Jin Abbas· Oct 26, 2024
Solid pick for teams standardizing on skills: lark-wiki is focused, and the summary matches what you get after install.
- ★★★★★Chaitanya Patil· Oct 22, 2024
lark-wiki reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hana Choi· Oct 10, 2024
Registry listing for lark-wiki matched our evaluation — installs cleanly and behaves as described in the markdown.
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