Lark CLI is a command-line tool for using Lark/Feishu services such as messaging, documents, spreadsheets, calendars, mail, tasks, and meetings. It is intended for people and AI agents that need to work with these business tools through terminal commands.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/larksuite/cli/lark-whiteboardnpx skills add larksuite/cli --skill lark-whiteboardgit clone --depth 1 https://github.com/larksuite/cliWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/larksuite/cli/lark-whiteboard)<a href="https://agentmods.dev/skills/larksuite/cli/lark-whiteboard"><img src="https://agentmods.dev/badge/skills/larksuite/cli/lark-whiteboard.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00104 | $0.00881 |
| Opus 5 | $0.00052 | $0.00441 |
| Sonnet 5 | $0.00021 | $0.00176 |
| Haiku 4.5 | $0.00010 | $0.00088 |
Grade A, and why
lark-whiteboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- lark-whiteboard — 91% identical, 23 lines differ
- lark-whiteboard — 88% identical, 34 lines differ
What it actually says
[!IMPORTANT]
- 运行
lark-cli --version，确认可用，无需询问用户。- 运行
npx -y @larksuite/whiteboard-cli@^0.2.13 -v，确认可用，无需询问用户。
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
快速决策
身份：画板操作默认使用 --as user。仅当需要以应用身份上传时使用 --as bot。
先判断「只读还是写入」，再在对应表内按上到下匹配，命中即停。
A. 只读 · 查看 / 导出（不改画板）
| 用户需求 | 行动 |
|---|---|
| 查看画板内容 / 导出图片 | +export --output-type preview |
| 导出 SVG 矢量图 | +export --output-type svg |
| 提取画板的 Mermaid/PlantUML 源码 | +export --output-type source |
B. 写入 · 创作 / 编辑（会改画板，命中即停）
| 场景 | 行动 | 写入方式 | 对原内容 |
|---|---|---|---|
| 用户已提供 Mermaid/PlantUML/SVG 代码，或明确指定用该格式 | 使用该代码 → +update，--input_format 取单值 mermaid / plantuml / svg；写入非空已有画板并需要 overwrite 时，先确认会整板重建；若 SVG 用于修改已有画板，先走 routes/svg-edit.md 有损确认 |
overwrite / append | 按用户要求 |
| 从零新建复杂图表（架构/流程/组织等） | → § 创作 Workflow | 首次写入 | — |
| 修改 / 增补已有画板 | → § 编辑 Workflow | 见该表 | 见该表 |
Shortcuts
| Shortcut | 说明 |
|---|---|
+export |
导出画板为预览图片、SVG 矢量图、代码或原始节点结构。 |
+update |
更新画板，支持 PlantUML、Mermaid、SVG 或 OpenAPI 原生格式 |
不在本 skill 范围
- 文档内容编辑 → lark-doc lark-doc
- 在文档中创建画板 → lark-doc-whiteboard.md
- 表格 / Base 操作 → lark-sheets / lark-base
What ships with it
30 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- elements/connectors.md 5.5 KB
- elements/content.md 1.5 KB
- elements/image.md 4.3 KB
- elements/layout.md 19 KB
- elements/schema.md 15 KB
- elements/style.md 11 KB
- elements/typography.md 2.1 KB
- references/lark-whiteboard-export.md 3.0 KB
- references/lark-whiteboard-update.md 4.6 KB
- references/lark-whiteboard-workflow.md 6.1 KB
- routes/dsl.md 6.7 KB
- routes/mermaid.md 1.1 KB
- routes/svg-edit.md 3.8 KB
- routes/svg.md 4.4 KB
- scenes/architecture.md 22 KB
- scenes/bar-chart.md 7.8 KB
- scenes/comparison.md 7.4 KB
- scenes/fishbone.md 9.6 KB
- scenes/flowchart.md 11 KB
- scenes/flywheel.md 8.6 KB
- scenes/funnel.md 4.2 KB
- scenes/line-chart.md 9.4 KB
- scenes/mention.md 3.0 KB
- scenes/mermaid.md 3.4 KB
- scenes/milestone.md 4.0 KB
- scenes/organization.md 8.2 KB
- scenes/photo-showcase.md 6.0 KB
- scenes/pyramid.md 3.9 KB
- scenes/swimlane.md 20 KB
- scenes/treemap.md 6.3 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 56 lines · 104 tokens per session scan A 78a3fbffa0da
lark-whiteboard is a skill published in the GitHub repository larksuite/cli (16,971 stars, last pushed today), licensed MIT. It adds 104 tokens to every session and 881 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…