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创建 lark-cli 的自定义 Skill。当用户需要把飞书 API 操作封装成可复用的 Skill（包装原子 API 或编排多步流程）时使用。
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Configure official native OpenCode to add a local LiteLLM OpenAI-compatible Gemini model as a selectable provider using config-file secret references, without wrappers and without changing the current default model.
Diagnose and repair a local LiteLLM + Vertex AI proxy on macOS, especially when `http://127.0.0.1:4000/` or `/v1` is down, startup hangs at `Waiting for application startup`, `/ui/login/` says `Authentication Error, Not connected to DB!`, or Prisma/PostgreSQL issues need to be isolated from the API proxy by splitting `lite` and `full` modes.
Configure an existing Hermes Agent deployment to use a local LiteLLM Vertex Proxy as an additional model option, without disturbing the current Hermes configuration. Use when you need Hermes to access Gemini models via a local LiteLLM gateway already running at 127.0.0.1:4000.
Configure OpenClaw to use an already-running local LiteLLM gateway for Gemini on macOS, with a low-risk add-as-option workflow, exact openclaw.json snippets, verification commands, rollback steps, and the real caveat that some per-run model overrides are rejected unless you use --local or switch the active alias first.
Build a local macOS LiteLLM gateway that exposes Google Cloud Vertex AI Gemini behind an Anthropic-compatible endpoint, then connect Claude Code and OpenClaw to it without breaking existing setups. Use when starting from a fresh machine, when you need a self-starting LaunchAgent service on 127.0.0.1, when Claude Code should route through LiteLLM, or when OpenClaw needs a selectable Gemini-via-LiteLLM model.
Fix packaging and validation failures caused by fragile SKILL frontmatter when publishing or syncing skills into Toby's davidtoby/agent-skills repository. Use when rebuild_all_packages.py fails early on a promoted skill, especially after copying local-only community skills such as lark-* into the repo.
| name | lark-skill-maker |
| description | 创建 lark-cli 的自定义 Skill。当用户需要把飞书 API 操作封装成可复用的 Skill（包装原子 API 或编排多步流程）时使用。 |
| metadata | {"requires":{"bins":["lark-cli"]}} |
基于 lark-cli 创建新 Skill。Skill = 一份 SKILL.md，教 AI 用 CLI 命令完成任务。
lark-cli <service> <resource> <method> # 已注册 API
lark-cli <service> +<verb> # Shortcut（高级封装）
lark-cli api <METHOD> <path> [--data/--params] # 任意飞书 OpenAPI
lark-cli schema <service.resource.method> # 查参数定义
优先级：Shortcut > 已注册 API > api 裸调。
# 1. 查看已有的 API 资源和 Shortcut
lark-cli <service> --help
# 2. 查参数定义
lark-cli schema <service.resource.method>
# 3. 未注册的 API，用 api 直接调用
lark-cli api GET /open-apis/vc/v1/rooms --params '{"page_size":"50"}'
lark-cli api POST /open-apis/vc/v1/rooms/search --data '{"query":"5F"}'
如果以上命令无法覆盖需求（CLI 没有对应的已注册 API 或 Shortcut），使用 lark-openapi-explorer 从飞书官方文档库逐层挖掘原生 OpenAPI 接口，获取完整的方法、路径、参数和权限信息，再通过 lark-cli api 裸调完成任务。
通过以上流程确定需要哪些 API、参数和 scope。
文件放在 skills/lark-<name>/SKILL.md：
---
name: lark-<name>
version: 1.0.0
description: "<功能描述>。当用户需要<触发场景>时使用。"
metadata:
requires:
bins: ["lark-cli"]
---
# <标题>
> **前置条件：** 先阅读 [`../lark-shared/SKILL.md`](../lark-shared/SKILL.md)。
## 命令
\```bash
# 单步操作
lark-cli api POST /open-apis/xxx --data '{...}'
# 多步编排：说明步骤间数据传递
# Step 1: ...（记录返回的 xxx_id）
# Step 2: 使用 Step 1 的 xxx_id
\```
## 权限
| 操作 | 所需 scope |
|------|-----------|
| xxx | `scope:name` |
lark-cli auth login --domain <name>--dry-run 预览