lark-openapi-explorer

lark-openapi-explorer is a skill for Claude Code, Codex from ddpie/lark-mcp-on-agentcore. It costs 71 tokens per session (1,425 once invoked), scanned A, original, MIT.

A skill for finding and calling Feishu/Lark OpenAPI endpoints that are not covered by existing skills or registered commands. OpenAPI is the documented interface that software uses to access a service.

In plain words
What is it for?
Searching Lark's official documentation, identifying the correct endpoint and parameters, and invoking the native API through the available Lark call mechanism.
Why use it?
It provides a documented path for handling Lark requests when no prepared tool exists, while requiring the relevant official API documentation to be located first.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Searching Lark's official documentation, identifying the correct endpoint and parameters, and invoking the native API through the available Lark call mechanism.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ddpie/lark-mcp-on-agentcore/lark-openapi-explorer
Install

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.

Any agent
npx skills add ddpie/lark-mcp-on-agentcore --skill lark-openapi-explorer
Clone the repo
git clone --depth 1 https://github.com/ddpie/lark-mcp-on-agentcore

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for lark-openapi-explorer

README.md
[![agentmods](https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-openapi-explorer/github.svg)](https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-openapi-explorer)
Your own site
<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-openapi-explorer"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-openapi-explorer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for lark-openapi-explorer

Your own site · 80×15
<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-openapi-explorer"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-openapi-explorer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,425 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00071 $0.01425
Opus 5 $0.00036 $0.00713
Sonnet 5 $0.00014 $0.00285
Haiku 4.5 $0.00007 $0.00143

Measured 8d ago against content hash f0a464de1b8f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

lark-openapi-explorer 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 8d 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.

docker/skills/lark-openapi-explorer/SKILL.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OpenAPI Explorer

(authentication is handled automatically by the MCP server)

当用户的需求无法被现有 skill 或已注册 MCP tool 覆盖时,使用本技能从飞书官方 markdown 文档库中逐层挖掘原生 OpenAPI 接口,然后通过 lark_invoke 裸调完成任务。

文档库结构

飞书 OpenAPI 文档以 markdown 层级组织:

llms.txt                          ← 顶层索引,列出所有模块文档链接
  └─ llms-<module>.txt            ← 模块文档,包含功能概述 + 底层 API 文档链接
       └─ <api-doc>.md            ← 单个 API 的完整说明(方法/路径/参数/响应/错误码)

文档入口:

品牌 入口 URL
飞书 (Feishu) https://open.feishu.cn/llms.txt
Lark https://open.larksuite.com/llms.txt

所有文档以中文编写。如果用户使用英文交流,需将文档内容翻译为英文后输出。

挖掘流程

严格按以下步骤逐层检索,不要跳步或猜测 API

Step 1:确认现有能力不足

# 先检查是否已有对应的 MCP tool
lark_discover(category="<可能的service>")

如果已有对应工具,直接使用,不需要继续挖掘

Step 2:从顶层索引定位模块

用 WebFetch 获取顶层索引,找到与需求相关的模块文档链接:

WebFetch https://open.feishu.cn/llms.txt
  → 提取问题:"列出所有模块文档链接,找出与 <用户需求关键词> 相关的链接"
  • 飞书品牌使用 open.feishu.cn
  • Lark 品牌使用 open.larksuite.com
  • 如不确定用户品牌,默认使用飞书

Step 3:从模块文档定位具体 API

用 WebFetch 获取模块文档,找到具体 API 的文档链接:

WebFetch https://open.feishu.cn/llms-docs/zh-CN/llms-<module>.txt
  → 提取问题:"找出与 <用户需求> 相关的 API 说明和文档链接"

Step 4:获取 API 完整规范

用 WebFetch 获取具体 API 文档,提取完整的调用规范:

WebFetch https://open.feishu.cn/document/server-docs/.../<api>.md
  → 提取问题:"返回完整 API 规范:HTTP 方法、URL 路径、路径参数、查询参数、请求体字段(名称/类型/必填/说明)、响应字段、所需权限、错误码"

Step 5:通过 MCP tool 调用 API

使用 lark_invoke 裸调:

# GET 请求
lark_invoke(tool_name="lark_api_GET", args={
  params: {"path": "/open-apis/<path>", "key": "value"}
})

# POST 请求
lark_invoke(tool_name="lark_api_POST", args={
  params: {"path": "/open-apis/<path>"},
  data: {"key": "value"}
})

# PUT 请求
lark_invoke(tool_name="lark_api_PUT", args={
  params: {"path": "/open-apis/<path>"},
  data: {"key": "value"}
})

# DELETE 请求
lark_invoke(tool_name="lark_api_DELETE", args={
  params: {"path": "/open-apis/<path>"}
})

输出规范

向用户呈现挖掘结果时,按以下格式组织:

  1. API 名称与功能:一句话描述
  2. HTTP 方法与路径METHOD /open-apis/...
  3. 关键参数:列出必填和常用可选参数
  4. 所需权限:scope 列表
  5. 调用示例:给出 lark_invoke 的完整工具调用
  6. 注意事项:频率限制、特殊约束等

Read the full file on GitHub · 157 lines

Changes

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.

  1. 8d ago First seen · 157 lines · 71 tokens per session scan A f0a464de1b8f

Subscribe to this mod's changes

lark-openapi-explorer is a skill published in the GitHub repository ddpie/lark-mcp-on-agentcore (8 stars, last pushed 10d ago), licensed MIT. It adds 71 tokens to every session and 1,425 once invoked, about $0.0004 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 2026-08-31.

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