feat(multi-llm): Phase 1 - Foundation adaptor architecture
Implement base adaptor pattern for multi-LLM support (Issue #179) **Architecture:** - Created adaptors/ package with base SkillAdaptor class - Implemented factory pattern with get_adaptor() registry - Refactored Claude-specific code into ClaudeAdaptor **Changes:** - New: src/skill_seekers/cli/adaptors/base.py (SkillAdaptor + SkillMetadata) - New: src/skill_seekers/cli/adaptors/__init__.py (registry + factory) - New: src/skill_seekers/cli/adaptors/claude.py (refactored upload + enhance logic) - Modified: package_skill.py (added --target flag, uses adaptor.package()) - Modified: upload_skill.py (added --target flag, uses adaptor.upload()) - Modified: enhance_skill.py (added --target flag, uses adaptor.enhance()) **Tests:** - New: tests/test_adaptors/test_base.py (10 tests passing) - All existing tests still pass (backward compatible) **Backward Compatibility:** - Default --target=claude maintains existing behavior - All CLI tools work exactly as before without --target flag - No breaking changes **Next:** Phase 2 - Implement Gemini, OpenAI, Markdown adaptors
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yusyus
@@ -1,15 +1,20 @@
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#!/usr/bin/env python3
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"""
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Automatic Skill Uploader
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Uploads a skill .zip file to Claude using the Anthropic API
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Uploads a skill package to LLM platforms (Claude, Gemini, OpenAI, etc.)
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Usage:
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# Set API key (one-time)
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# Claude (default)
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export ANTHROPIC_API_KEY=sk-ant-...
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skill-seekers upload output/react.zip
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# Upload skill
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python3 upload_skill.py output/react.zip
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python3 upload_skill.py output/godot.zip
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# Gemini
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export GOOGLE_API_KEY=AIzaSy...
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skill-seekers upload output/react-gemini.tar.gz --target gemini
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# OpenAI
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export OPENAI_API_KEY=sk-proj-...
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skill-seekers upload output/react-openai.zip --target openai
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"""
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import os
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@@ -21,108 +26,84 @@ from pathlib import Path
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# Import utilities
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try:
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from utils import (
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get_api_key,
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get_upload_url,
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print_upload_instructions,
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validate_zip_file
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)
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except ImportError:
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sys.path.insert(0, str(Path(__file__).parent))
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from utils import (
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get_api_key,
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get_upload_url,
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print_upload_instructions,
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validate_zip_file
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)
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def upload_skill_api(zip_path):
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def upload_skill_api(package_path, target='claude', api_key=None):
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"""
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Upload skill to Claude via Anthropic API
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Upload skill package to LLM platform
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Args:
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zip_path: Path to skill .zip file
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package_path: Path to skill package file
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target: Target platform ('claude', 'gemini', 'openai')
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api_key: Optional API key (otherwise read from environment)
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Returns:
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tuple: (success, message)
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"""
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# Check for requests library
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try:
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import requests
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from skill_seekers.cli.adaptors import get_adaptor
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except ImportError:
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return False, "requests library not installed. Run: pip install requests"
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return False, "Adaptor system not available. Reinstall skill-seekers."
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# Validate zip file
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is_valid, error_msg = validate_zip_file(zip_path)
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if not is_valid:
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return False, error_msg
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# Get platform-specific adaptor
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try:
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adaptor = get_adaptor(target)
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except ValueError as e:
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return False, str(e)
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# Get API key
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api_key = get_api_key()
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if not api_key:
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return False, "ANTHROPIC_API_KEY not set. Run: export ANTHROPIC_API_KEY=sk-ant-..."
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api_key = os.environ.get(adaptor.get_env_var_name(), '').strip()
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zip_path = Path(zip_path)
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skill_name = zip_path.stem
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if not api_key:
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return False, f"{adaptor.get_env_var_name()} not set. Export your API key first."
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# Validate API key format
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if not adaptor.validate_api_key(api_key):
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return False, f"Invalid API key format for {adaptor.PLATFORM_NAME}"
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package_path = Path(package_path)
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# Basic file validation
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if not package_path.exists():
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return False, f"File not found: {package_path}"
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skill_name = package_path.stem
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print(f"📤 Uploading skill: {skill_name}")
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print(f" Source: {zip_path}")
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print(f" Size: {zip_path.stat().st_size:,} bytes")
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print(f" Target: {adaptor.PLATFORM_NAME}")
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print(f" Source: {package_path}")
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print(f" Size: {package_path.stat().st_size:,} bytes")
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print()
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# Prepare API request
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api_url = "https://api.anthropic.com/v1/skills"
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headers = {
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"x-api-key": api_key,
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"anthropic-version": "2023-06-01",
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"anthropic-beta": "skills-2025-10-02"
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}
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# Upload using adaptor
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print(f"⏳ Uploading to {adaptor.PLATFORM_NAME}...")
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try:
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# Read zip file
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with open(zip_path, 'rb') as f:
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zip_data = f.read()
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result = adaptor.upload(package_path, api_key)
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# Upload skill
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print("⏳ Uploading to Anthropic API...")
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files = {
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'files[]': (zip_path.name, zip_data, 'application/zip')
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}
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response = requests.post(
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api_url,
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headers=headers,
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files=files,
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timeout=60
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)
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# Check response
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if response.status_code == 200:
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if result['success']:
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print()
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print("✅ Skill uploaded successfully!")
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print(f"✅ {result['message']}")
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print()
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print("Your skill is now available in Claude at:")
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print(f" {get_upload_url()}")
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if result['url']:
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print("Your skill is now available at:")
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print(f" {result['url']}")
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if result['skill_id']:
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print(f" Skill ID: {result['skill_id']}")
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print()
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return True, "Upload successful"
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elif response.status_code == 401:
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return False, "Authentication failed. Check your ANTHROPIC_API_KEY"
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elif response.status_code == 400:
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error_msg = response.json().get('error', {}).get('message', 'Unknown error')
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return False, f"Invalid skill format: {error_msg}"
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else:
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error_msg = response.json().get('error', {}).get('message', 'Unknown error')
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return False, f"Upload failed ({response.status_code}): {error_msg}"
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except requests.exceptions.Timeout:
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return False, "Upload timed out. Try again or use manual upload"
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except requests.exceptions.ConnectionError:
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return False, "Connection error. Check your internet connection"
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return False, result['message']
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except Exception as e:
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return False, f"Unexpected error: {str(e)}"
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@@ -130,36 +111,55 @@ def upload_skill_api(zip_path):
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def main():
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parser = argparse.ArgumentParser(
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description="Upload a skill .zip file to Claude via Anthropic API",
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description="Upload a skill package to LLM platforms",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Setup:
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1. Get your Anthropic API key from https://console.anthropic.com/
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2. Set the API key:
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export ANTHROPIC_API_KEY=sk-ant-...
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Claude:
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export ANTHROPIC_API_KEY=sk-ant-...
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Gemini:
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export GOOGLE_API_KEY=AIzaSy...
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OpenAI:
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export OPENAI_API_KEY=sk-proj-...
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Examples:
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# Upload skill
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python3 upload_skill.py output/react.zip
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# Upload to Claude (default)
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skill-seekers upload output/react.zip
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# Upload with explicit path
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python3 upload_skill.py /path/to/skill.zip
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# Upload to Gemini
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skill-seekers upload output/react-gemini.tar.gz --target gemini
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Requirements:
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- ANTHROPIC_API_KEY environment variable must be set
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- requests library (pip install requests)
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# Upload to OpenAI
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skill-seekers upload output/react-openai.zip --target openai
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# Upload with explicit API key
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skill-seekers upload output/react.zip --api-key sk-ant-...
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"""
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)
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parser.add_argument(
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'zip_file',
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help='Path to skill .zip file (e.g., output/react.zip)'
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'package_file',
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help='Path to skill package file (e.g., output/react.zip)'
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)
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parser.add_argument(
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'--target',
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choices=['claude', 'gemini', 'openai'],
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default='claude',
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help='Target LLM platform (default: claude)'
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)
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parser.add_argument(
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'--api-key',
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help='Platform API key (or set environment variable)'
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)
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args = parser.parse_args()
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# Upload skill
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success, message = upload_skill_api(args.zip_file)
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success, message = upload_skill_api(args.package_file, args.target, args.api_key)
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if success:
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sys.exit(0)
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@@ -167,7 +167,7 @@ Requirements:
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print(f"\n❌ Upload failed: {message}")
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print()
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print("📝 Manual upload instructions:")
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print_upload_instructions(args.zip_file)
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print_upload_instructions(args.package_file)
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sys.exit(1)
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