Skill Exporter

读取 Clawdbot 技能目录，生成独立可部署微服务的整套文件：Dockerfile、FastAPI 包装 api.py、requirements.txt、.env.example，以及 Railway/Fly.io/Docker 部署配置；通过 --llm 可选生成调用 Anthropic/OpenAI 的 llm_client.py。构建与部署由用户自行执行。

macstenk

热度 96.38

最近更新 2026-05-11 15:51:18

分类：开发依赖：代码执行 / 读写文件应用场景：代码生成 / API 集成 / DevOps / 自动化脚本

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add skill-exporter

Metadata

name: skill-exporter
description: Export Clawdbot skills as standalone, deployable microservices. Use when you want to dockerize a skill, deploy it to Railway or Fly.io, or create an independent API service. Generates Dockerfile, FastAPI wrapper, requirements.txt, deployment configs, and optional LLM client integration.
license: MIT
compatibility: Requires python3. Works with any AgentSkills-compatible agent.
metadata: {"author":"MacStenk","version":"1.0.0","clawdbot":{"emoji":"📦","requires":{"bins":["python3"]}}}

Skill Exporter

Transform Clawdbot skills into standalone, deployable microservices.

Workflow

Clawdbot Skill (tested & working)
         ↓
    skill-exporter
         ↓
Standalone Microservice
         ↓
Railway / Fly.io / Docker

Usage

Export a skill

python3 {baseDir}/scripts/export.py \
  --skill ~/.clawdbot/skills/instagram \
  --target railway \
  --llm anthropic \
  --output ~/projects/instagram-service

Options

Flag	Description	Default
`--skill`	Path to skill directory	required
`--target`	Deployment target: `railway`, `fly`, `docker`	`docker`
`--llm`	LLM provider: `anthropic`, `openai`, `none`	`none`
`--output`	Output directory	`./<skill-name>-service`
`--port`	API port	`8000`

Targets

railway — Generates railway.json, optimized Dockerfile, health checks fly — Generates fly.toml, multi-region ready docker — Generic Dockerfile, docker-compose.yml

LLM Integration

When --llm is set, generates llm_client.py with:

Caption/prompt generation
Decision making helpers
Rate limiting and error handling

What Gets Generated

<skill>-service/
├── Dockerfile
├── docker-compose.yml
├── api.py              # FastAPI wrapper
├── llm_client.py       # If --llm specified
├── requirements.txt
├── .env.example
├── railway.json        # If --target railway
├── fly.toml            # If --target fly
└── scripts/            # Copied from original skill
    └── *.py

Requirements

The source skill must have:

SKILL.md with valid frontmatter
At least one script in scripts/
Scripts should be callable (functions, not just inline code)

Post-Export

Copy .env.example to .env and fill in secrets
Test locally: docker-compose up
Deploy: railway up or fly deploy