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-suitenpx skills add larksuite/cli --skill lark-suitegit 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-suite)<a href="https://agentmods.dev/skills/larksuite/cli/lark-suite"><img src="https://agentmods.dev/badge/skills/larksuite/cli/lark-suite.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 | $0.00069 | $0.00533 |
| Opus 5 | $0.00034 | $0.00267 |
| Sonnet 5 | $0.00014 | $0.00107 |
| Haiku 4.5 | $0.00007 | $0.00053 |
Grade A, and why
lark-suite 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 4d 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.
What it actually says
Lark Suite
你是飞书/Lark 能力的聚合路由层。你的职责是先判断用户要使用哪个 lark-* 子能力，再读取并遵循对应子能力的说明。
lark-suite 不直接承载具体 API 操作步骤。除非对应子能力已被读取，否则不要仅根据本文件拼命令、猜参数或执行复杂操作。
所有子能力统一收纳在当前 skill 的 references/ 目录。选择 lark-foo 后，直接读取 references/lark-foo/SKILL.md；不要再次调用 Skill(lark-foo)，也不要使用 Find/Glob 遍历或探测整个 references 目录。
使用流程
- 根据用户意图从下方路由表选择一个或多个子能力；即使用户尚未提供链接、ID 或具体工作表，也先选择能力，再由子能力询问缺失信息。
- 直接读取
references/<skill-name>/SKILL.md加载所选子能力，不要把收纳后的子能力当作独立 skill 再次调用。 - 仅使用本文件列出的路由与对应子能力入口，不要遍历或探测其他技能目录。
- 如果目标能力未列出，返回无法路由的明确提示。
- 仅读取当前已选子能力明确要求的前置文件。
- 按目标子能力的说明执行；认证、租户、身份、权限和通用排障优先遵循
lark-shared。
多步任务可以组合多个子能力，但每一步都应由具体子能力驱动。例如“查联系人并发消息”先用 lark-contact 解析身份，再用 lark-im 发消息。
能力路由
根据用户意图从以下条目选择对应子能力；如果一个任务涉及多个能力，按实际操作顺序逐步读取并使用对应子能力。
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.
- 4d ago First seen · 35 lines · 69 tokens per session scan A 47cde72469fa
lark-suite is a skill published in the GitHub repository larksuite/cli (16,925 stars, last pushed 2d ago), licensed MIT. It adds 69 tokens to every session and 533 once invoked, about $0.0003 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
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
azure-mgmt-botservice-dotnet
Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: "Bot Service", "BotResource", "Azure Bot", "DirectLine channel", "Teams channel", "bot management .NET", "create bot".
diagnostic-stem-delivery
Audio production with diagnostic analysis, timecode parsing from documents, and verified export workflow.
accessibility
Consolidated accessibility skill entrypoint for WCAG 2.2, ARIA Authoring Practices, cognitive accessibility, Section 508, EN 301 549, design intent verification, and the Accessibility Planner workflow.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.