SkillHub

skill-exporter

v1.0.0

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.

Sourced from ClawHub, Authored by MacStenk

Installation

Please help me install the skill `skill-exporter` from SkillHub official store. npx skills add MacStenk/skill-exporter

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

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