$ # GOG Skill Installer for OpenClaw
$ # Search Intent: "OpenClaw GOG skill install for email automation"

$ # Step 1: Clone the skill repository
$ git clone https://github.com/openclaw/gog-skill ~/.openclaw/skills/gog

$ # Step 2: Install Python dependencies
$ pip install google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client

$ # Step 3: Set up OAuth credentials
$ echo "Visit: https://console.cloud.google.com/apis/credentials"
$ echo "Create OAuth 2.0 Client ID for Desktop App"
$ echo "Download credentials.json to ~/.openclaw/skills/gog/"

$ # Step 4: Run initial authentication
$ python ~/.openclaw/skills/gog/auth.py
$ # Follow browser flow to authorize access

$ # Step 5: Test the skill
$ openclaw skill run gog --command "list unread emails from today"

$ # Step 6: Enable in OpenClaw config
$ echo 'skills: ["gog"]' >> ~/.openclaw/config.yaml

$ # Firecrawl CLI Skill Installer
$ # Search Intent: "Firecrawl sandbox script for parallel web extraction"

$ # Step 1: Install Firecrawl CLI via npm
$ npm install -g @mendable/firecrawl-cli

$ # Step 2: Set up OpenClaw skill wrapper
$ mkdir -p ~/.openclaw/skills/firecrawl
$ cat > ~/.openclaw/skills/firecrawl/skill.yaml << 'EOF'
name: firecrawl
description: Extract web content as clean Markdown
commands:
  - name: scrape
    description: Scrape a single URL
    args: ["url"]
  - name: crawl
    description: Crawl entire website
    args: ["url", "--limit"]
  - name: search
    description: Search and extract
    args: ["query"]
EOF

$ # Step 3: Get your API key
$ echo "Get API key at: https://firecrawl.dev"
$ export FIRECRAWL_API_KEY="your-key-here"

$ # Step 4: Test the skill
$ openclaw skill run firecrawl --scrape "https://news.ycombinator.com"

$ # Step 5: Parallel extraction example
$ firecrawl crawl "https://docs.example.com" --limit 50 --parallel 10 --output markdown

$ # Composio Skill Installer
$ # Search Intent: "Composio multi-tool auth script"

$ # Step 1: Install Composio SDK
$ pip install composio-core

$ # Step 2: Create skill directory
$ mkdir -p ~/.openclaw/skills/composio

$ # Step 3: Set up OpenClaw wrapper
$ cat > ~/.openclaw/skills/composio/skill.py << 'EOF'
import os
from composio import ComposioToolSet

toolset = ComposioToolSet(api_key=os.getenv("COMPOSIO_API_KEY"))

def execute(app: str, action: str, params: dict):
    """Execute action on any connected app."""
    tools = toolset.get_tools(apps=[app])
    result = toolset.execute_action(action, params)
    return result
EOF

$ # Step 4: Authenticate your apps
$ composio add slack
$ composio add jira
$ composio add salesforce
$ composio add github

$ # Step 5: List connected apps
$ composio list

$ # Step 6: Test the skill
$ openclaw skill run composio --app slack --action "send_message" --params '{"channel":"general","text":"Hello from AI"}'

$ # Self-Improver Skill Installer
$ # Search Intent: "Self-improving agent memory script"

$ # Step 1: Create skill directory
$ mkdir -p ~/.openclaw/skills/self_improver
$ cd ~/.openclaw/skills/self_improver

$ # Step 2: Create memory database
$ cat > memory.py << 'EOF'
import sqlite3
import json
from datetime import datetime

class AgentMemory:
    def __init__(self, db_path="~/.openclaw/memory.db"):
        self.conn = sqlite3.connect(os.path.expanduser(db_path))
        self._init_db()
    
    def _init_db(self):
        self.conn.execute("""
            CREATE TABLE IF NOT EXISTS corrections (
                id INTEGER PRIMARY KEY,
                user_input TEXT,
                wrong_response TEXT,
                correct_response TEXT,
                correction_reason TEXT,
                created_at TIMESTAMP
            )
        """)
        self.conn.execute("""
            CREATE TABLE IF NOT EXISTS preferences (
                key TEXT PRIMARY KEY,
                value TEXT,
                updated_at TIMESTAMP
            )
        """)
        self.conn.commit()
    
    def learn_correction(self, user_input, wrong, correct, reason):
        """Store a correction so agent never repeats the mistake."""
        self.conn.execute(
            "INSERT INTO corrections (user_input, wrong_response, correct_response, correction_reason, created_at) VALUES (?, ?, ?, ?, ?)",
            (user_input, wrong, correct, reason, datetime.now())
        )
        self.conn.commit()
    
    def recall(self, user_input):
        """Retrieve relevant corrections for this input."""
        cursor = self.conn.execute(
            "SELECT wrong_response, correct_response FROM corrections WHERE user_input LIKE ? LIMIT 5",
            (f"%{user_input}%",)
        )
        return [{"wrong": row[0], "correct": row[1]} for row in cursor.fetchall()]
    
    def set_preference(self, key, value):
        """Store user preference."""
        self.conn.execute(
            "REPLACE INTO preferences (key, value, updated_at) VALUES (?, ?, ?)",
            (key, value, datetime.now())
        )
        self.conn.commit()
    
    def get_preference(self, key):
        cursor = self.conn.execute("SELECT value FROM preferences WHERE key = ?", (key,))
        row = cursor.fetchone()
        return row[0] if row else None
EOF

$ # Step 3: Test memory
$ python -c "from memory import AgentMemory; m=AgentMemory(); m.learn_correction('write email', 'Dear Sir', 'Hi [Name]', 'Use first name for casual emails'); print(m.recall('email'))"

$ # Whisper Local Skill Installer
$ # Search Intent: "Private offline meeting transcription skill"

$ # Step 1: Install Whisper and dependencies
$ pip install openai-whisper
$ pip install torch torchaudio  # CPU or CUDA version

$ # Step 2: Create skill directory
$ mkdir -p ~/.openclaw/skills/whisper
$ cd ~/.openclaw/skills/whisper

$ # Step 3: Create skill wrapper
$ cat > skill.py << 'EOF'
import whisper
import os
import sys

model_name = os.getenv("WHISPER_MODEL", "base")
model = whisper.load_model(model_name)

def transcribe(audio_path: str, language: str = "en") -> dict:
    """Transcribe audio file to text (100% local)."""
    result = model.transcribe(audio_path, language=language)
    return {
        "text": result["text"],
        "segments": result["segments"],
        "language": result["language"]
    }

if __name__ == "__main__":
    if len(sys.argv) > 1:
        audio_file = sys.argv[1]
        output = transcribe(audio_file)
        print(output["text"])
        # Optionally save to file
        with open(audio_file + ".txt", "w") as f:
            f.write(output["text"])
EOF

$ # Step 4: Test with a sample audio file
$ python skill.py ~/Downloads/meeting_recording.mp3

$ # Step 5: Download larger model for better accuracy (first run only)
$ export WHISPER_MODEL=medium
$ python -c "import whisper; whisper.load_model('medium')"