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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。
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
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