Skill Exporter

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.

Security Scan

The skill's code and instructions are consistent with its stated purpose (exporting a Clawdbot skill into a deployable microservice); nothing in the provided files indicates hidden exfiltration or unrelated credential requests, though the generated service templates include things (open CORS, subprocess usage, optional LLM API keys) you should review before deploying publicly.

Purpose & Capability

Name/description match what the files do: scripts/export.py reads a local skill directory and emits Dockerfile, FastAPI wrapper, requirements, llm client stubs, and copies the skill scripts. Required binary (python3) is appropriate and no unrelated external credentials or binaries are requested.

Instruction Scope

SKILL.md instructs running export.py against a local skill path; export.py reads SKILL.md, scripts/, and .env (if present) from the source skill to detect metadata, env var names, and imports. It does not appear to execute or import the target scripts, but it will copy them into the output service. The generated api.py template allows CORS for all origins and includes subprocess usage — acceptable for a generic wrapper but worth reviewing if you plan to deploy publicly.

Install Mechanism

No install spec — this is instruction + a generator script. There are no downloads or extracted archives in the skill itself, so there is no installer risk from this package.

Credentials

The exporter itself doesn't require any credentials. When you request LLM integration (--llm), the generated llm_client files expect standard provider API keys (ANTHROPIC_API_KEY / OPENAI_API_KEY) in the runtime environment — this is proportional to optional LLM functionality. The exporter scans for env var NAMES in the source skill and .env to populate .env.example, but it does not appear to exfiltrate secret VALUES.

Persistence & Privilege

always is false and the skill doesn't request persistent platform privileges. The tool writes files to the chosen output directory and marks copied scripts executable; it does not modify other skills or global agent configuration.

Assessment

This exporter appears to do what it says, but review the generated service before deploying. Specific suggestions: (1) inspect generated api.py for the wide-open CORS policy and adjust or add authentication if the service will be public; (2) review any subprocess calls and copied scripts to ensure they don't run unsafe commands or expect secrets embedded in code; (3) the exporter will detect and surface environment variable NAMES from the source skill and create a .env.example — do not put real secret values into the source skill's .env when running the exporter (work on a sanitized copy) and do not commit .env with secrets to source control; (4) if you enable --llm you will need to provide provider API keys at runtime — only supply keys you trust and scope them appropriately; and (5) test the generated docker image locally before deploying to Railway/Fly or exposing it to the internet.

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