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skill-exporter

by openclaw

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

Installation

Pick a client and clone the repository into its skills directory.

Installation

Quick info

Author
openclaw
Category
Mobile
Views
1

About this skill

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.

How to use

  1. Upewnij się, że masz zainstalowany Python 3 oraz umiejętność Clawdbota, którą chcesz wyeksportować (powinna być przetestowana i działająca).

  2. Uruchom skrypt eksportu, podając ścieżkę do katalogu umiejętności, docelową platformę wdrożeniową (railway, fly lub docker) oraz opcjonalnie dostawcę LLM (anthropic, openai lub none). Przykład: python3 scripts/export.py --skill ~/.clawdbot/skills/instagram --target railway --llm anthropic --output ~/projects/instagram-service

  3. Wybierz platformę wdrożeniową: dla Railway otrzymasz railway.json i zoptymalizowany Dockerfile z health checkami, dla Fly.io plik fly.toml przygotowany do wdrożenia wieloregionowego, dla Docker generyczny Dockerfile i docker-compose.yml.

  4. Jeśli wybrałeś integrację LLM (--llm anthropic lub openai), narzędzie wygeneruje plik llm_client.py z gotowymi funkcjami do generowania podpisów, wspomagania decyzji oraz obsługi limitów i błędów.

  5. W wygenerowanym katalogu znajdziesz plik .env.example — skopiuj go na .env i uzupełnij wymagane zmienne środowiskowe (klucze API, porty itp.).

  6. Wdrażaj mikrousługę na wybraną platformę: dla Railway wystarczy push do repozytorium, dla Fly.io użyj flyctl deploy, dla Docker uruchom docker-compose up.

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