lark-whiteboard▌
larksuite/cli · updated Jun 1, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
[!NOTE]
- ›环境依赖:绘制画板需要 @larksuite/whiteboard-cli(画板 Node.js CLI 工具),以及 lark-cli(LarkSuite CLI 工具)。
- ›如果执行失败,手动安装后重试:npm install -g @larksuite/whiteboard-cli@^0.1.0
Whiteboard Cli Skill
[!NOTE] 环境依赖:绘制画板需要
@larksuite/whiteboard-cli(画板 Node.js CLI 工具),以及lark-cli(LarkSuite CLI 工具)。 如果执行失败,手动安装后重试:npm install -g @larksuite/whiteboard-cli@^0.1.0
[!IMPORTANT] 执行
npm install安装新的依赖前,务必征得用户同意!
Workflow
这是画板,不是网页。 画板是无限画布上自由放置元素,flex 布局是可选增强。
Step 1: 路由 & 读取知识
- 判断渲染路径(见路由表):Mermaid 还是 DSL?
- 读对应 scene 指南 — 了解结构特征和布局策略
- 确定布局策略(见下方快速判断)和构建方式
- 读 references/ 核心模块 — 语法、布局、配色、排版、连线
Step 2: 生成完整 DSL(含颜色)
- 按 content.md 规划信息量和分组
- 按 layout.md 选择布局模式和间距
- 按 style.md 上色(用户没指定时用默认经典色板)
- 按 schema.md 语法输出完整 JSON
- 连线参考 connectors.md,排版参考 typography.md
注意:部分图形(鱼骨/飞轮/柱状/折线等)等, 要按 scene 指南的脚本模板写 .js 脚本生成 JSON:
- node xxx.js → 产出 JSON 文件
- 用产出的 JSON 文件进入 Step 3
Step 3: 渲染 & 审查 → 交付
- 渲染前自查(见下方检查清单)
- 渲染 PNG,检查:
· 信息完整?布局合理?配色协调?
· 文字无截断?连线无交叉?
- 有问题 → 按症状表修复 → 重新渲染(最多 2 轮)
- 2 轮后仍有严重问题 → 考虑走 Mermaid 路径兜底
- 没问题 → 交付:
· 用户要求上传飞书 → 见下方”上传飞书画板”章节中的说明
· 用户未指定 → 展示 PNG 图片给用户
布局策略快速判断(详见 layout.md):
| 判断条件 | 布局策略 | 构建方式 |
|---|---|---|
| 有明确上下层级(用户层→服务层→数据层) | Flex 分层 | 直接写 JSON |
| 空间位置承载信息(地理、拓扑、角度) | 纯绝对定位 | 写脚本算坐标(node xxx.js) |
| 多个独立模块平级互联 | 混合(岛屿式) | 直接写 JSON + 估高辅助 |
| 不确定 | 默认 Flex(最安全) | 直接写 JSON |
构建方式是强约束:当 scene 指南要求"脚本生成"时,必须先写脚本(.js)并用
node执行来产出 JSON 文件。绝对定位场景(鱼骨图、飞轮图、柱状图、折线图等)的坐标需要数学计算,直接手写 JSON 极易导致节点重叠或连线穿模。
渲染路径选择(DSL or Mermaid)
| 图表类型 | 路径 | 理由 |
|---|---|---|
| 思维导图 | Mermaid | 辐射结构自动布局 |
| 时序图 | Mermaid | 参与方+消息自动排列 |
| 类图 | Mermaid | 类关系自动布局 |
| 饼图 | Mermaid | Mermaid 原生支持 |
| 流程图 | Mermaid | 通过 Mermaid 语法稳定生成结构 |
| 其他所有类型 | DSL | 精确控制样式和布局 |
路由规则:
- 自动 Mermaid:思维导图、时序图、类图、饼图、流程图 → 默认走 Mermaid
- 显式 Mermaid:用户输入包含 Mermaid 语法 → 走 Mermaid
- DSL 路径:其他所有类型 → 先读核心模块,再读对应场景指南
Mermaid 路径:参考 scenes/mermaid.md 编写 .mmd 文件,跳过 DSL 模块。
DSL 路径:按 Workflow 3 步执行。
模块索引
核心参考(DSL 路径必读)
| 模块 | 文件 | 说明 |
|---|---|---|
| DSL 语法 | references/schema.md |
节点类型、属性、尺寸值 |
| 内容规划 | references/content.md |
信息提取、密度决策、连线预判 |
| 布局系统 | references/layout.md |
网格方法论、Flex 映射、间距规则 |
| 排版规则 | references/typography.md |
字号层级、对齐、行距 |
| 连线系统 | references/connectors.md |
拓扑规划、锚点选择 |
| 配色系统 | references/style.md |
多色板、视觉层级 |
场景指南(按类型选读一个)
| 图表类型 | 文件 | 适用场景 |
|---|---|---|
| 架构图 | scenes/architecture.md |
分层架构、微服务架构 |
| 组织架构图 | scenes/organization.md |
公司组织、树形层级 |
| 对比图 | scenes/comparison.md |
方案对比、功能矩阵 |
| 鱼骨图 | scenes/fishbone.md |
因果分析、根因分析 |
| 柱状图 | scenes/bar-chart.md |
柱状图、条形图 |
| 折线图 | scenes/line-chart.md |
折线图、趋势图 |
| 树状图 | scenes/treemap.md |
矩形树图、层级占比 |
| 漏斗图 | scenes/funnel.md |
转化漏斗、销售漏斗 |
| 金字塔图 | scenes/pyramid.md |
层级结构、需求层次 |
| 循环/飞轮图 | scenes/flywheel.md |
增长飞轮、闭环链路 |
| 里程碑 | scenes/milestone.md |
时间线、版本演进 |
| Mermaid | scenes/mermaid.md |
思维导图、时序图、类图、饼图、流程图 |
CLI 命令
渲染:
npx -y @larksuite/whiteboard-cli@^0.1.0 -i my-diagram.json -o ./images/my-diagram.png # DSL 路径
npx -y @larksuite/whiteboard-cli@^0.1.0 -i diagram.mmd -o ./images/diagram.png # Mermaid 路径
npx -y @larksuite/whiteboard-cli@^0.1.0 -i skeleton.json -o ./images/step1.png -l coords.json # 两阶段(提取坐标)
上传飞书画板:
上传需要飞书认证。遇到认证或权限错误时,阅读
../lark-shared/SKILL.md了解登录和权限处理。
第一步:获取画板 Token
| 用户给了什么 | 怎么获取 Token |
|---|---|
画板 Token(XXX) |
直接使用 |
| 文档 URL 或 doc_id,文档中已有画板 | lark-cli docs +fetch --doc <URL> --as user,从返回的 <whiteboard token=”XXX”/> 中提取 token |
| 文档 URL 或 doc_id,需要新建画板 | lark-cli docs +update --doc <doc_id> --mode append --markdown '<whiteboard type=”blank”></whiteboard>' --as user,从响应的 data.board_tokens[0] 获取 token |
关于飞书文档的创建,读取等更多操作,请参考 lark-doc skill ../lark-doc/SKILL.md。
第二步:上传
[!CAUTION] MANDATORY PRE-FLIGHT CHECK (上传前强制拦截检查) 当你要向一个已存在的画板 Token 写入内容时,绝对禁止直接执行上传命令!你必须严格遵守以下两步: 强制执行 Dry Run(状态探测) 必须先在命令中添加
--overwrite --dry-run参数来探测画板当前状态。示例命令:npx -y @larksuite/whiteboard-cli@^0.1.0 --to openapi -i <输入文件> --format json | lark-cli docs +whiteboard-update --whiteboard-token <Token> --overwrite --dry-run --as user解析结果并拦截
- 仔细阅读 Dry Run 的输出日志。
- 如果日志包含
XX whiteboard nodes will be deleted:这说明画板非空,当前操作会覆盖并摧毁用户的原有图表!- 你必须立即停止操作,并通过
AskUserQuestion工具(或直接回复)向用户确认:”目标画板当前非空,继续更新将清空原有的 XX 个节点,是否确认覆盖?”- 只有在用户明确授权”同意覆盖”后,你才能移除
--dry-run真正执行上传。- 用户可能会要求你不覆盖更新画板内容,在这种情况下,移除
--overwrite和--dry-run参数再上传。
npx -y @larksuite/whiteboard-cli@^0.1.0 --to openapi -i <输入文件> --format json | lark-cli docs +whiteboard-update --whiteboard-token <画板Token> --yes --as user
画板一经上传不可修改。如需应用身份上传,将
--as user替换为--as bot。 如果画板非空,先加--overwrite --dry-run检查待删除节点数,向用户确认后去掉--dry-run执行。
症状→修复表(视觉审查发现问题时参照):
| 看到的问题 | 改什么 |
|---|---|
| 文字被截断 | height 改为 fit-content |
| 文字溢出容器右侧 | 增大 width,或缩短文字 |
| 节点重叠粘连 | 增大 gap |
| 节点挤成一团 | 增大 padding 和 gap |
| 连线穿过节点 | 调整 fromAnchor/toAnchor 或增大间距 |
| 大面积空白 | 缩小外层 frame 宽度 |
| 文字和背景色太接近 | 调整 fillColor 或 textColor |
| 布局整体偏左/偏右 | 调整绝对定位的 x 坐标使内容居中 |
渲染前自查
生成 DSL 后、渲染前,快速检查:
- 不同分组用了不同颜色?同组节点样式完全一致?
- 外层浅色背景、内层白色节点?(外重内轻)
- 所有节点有边框(borderWidth=2)?文字在背景上清晰可读?
- 连线用灰色(#BBBFC4),不用彩色?
- frame 都写了 layout 属性?gap 和 padding 都显式设置了?
- 含文字节点 height 用 fit-content?connector 在顶层 nodes 数组?
关键约束速查
最高频出错的规则,即使不读子模块文件也必须遵守。
- 含文字节点的 height 必须用
'fit-content'— 写死数值会截断文字 fill-container仅在 flex 父容器中生效 —layout: 'none'下宽度退化为 0- connector 必须放在顶层 nodes 数组 — 不能嵌套在 frame children 里
- 图层顺序 — 数组顺序 = 绘制顺序。后定义的元素层级越高,会覆盖先定义的。重叠/浮层/标注元素务必放在数组末尾。
- flex 容器内的 x/y 会被完全忽略 — 需要自由定位时用
layout: 'none'或放在顶层 nodes
❌ 致命错误:flex 容器内设 x/y,坐标不生效,节点按顺序排列
{ "type": "frame", "layout": "vertical", "children": [
{ "type": "rect", "x": 100, "y": 0, "text": "成都" },
{ "type": "rect", "x": 540, "y": 0, "text": "康定" }
]}
✅ 正确:用 layout: "none" 或放在顶层 nodes 用 x/y 定位。
How to use lark-whiteboard 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-whiteboard
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches lark-whiteboard 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-whiteboard. Access the skill through slash commands (e.g., /lark-whiteboard) 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
4.7★★★★★38 reviews- ★★★★★Nia Ghosh· Dec 12, 2024
Useful defaults in lark-whiteboard — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Zara Mehta· Dec 8, 2024
Registry listing for lark-whiteboard matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Neel Khan· Nov 27, 2024
Solid pick for teams standardizing on skills: lark-whiteboard is focused, and the summary matches what you get after install.
- ★★★★★Naina Khan· Nov 3, 2024
I recommend lark-whiteboard for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Mei Robinson· Nov 3, 2024
lark-whiteboard has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Chinedu Bhatia· Oct 22, 2024
lark-whiteboard reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Arya White· Oct 22, 2024
Keeps context tight: lark-whiteboard is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Neel Zhang· Oct 18, 2024
We added lark-whiteboard from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Naina Gonzalez· Sep 25, 2024
Registry listing for lark-whiteboard matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sakshi Patil· Sep 21, 2024
Solid pick for teams standardizing on skills: lark-whiteboard is focused, and the summary matches what you get after install.
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