flowchart TD
    subgraph Interfaces ["Multi-Channel Interfaces"]
        UI["FastAPI Web Studio (:8005)"]
        CLI["Master CLI (main_agent.py)"]
        MCP["Claude Desktop & Cursor (MCP Server)"]
        Excel["Master Excel Sheet (saved_videos_log.xlsx)"]
    end

    subgraph Pipeline ["6-Phase Autonomous Meta Pipeline"]
        P1["01_login_setup.py\n(Persistent Chrome + cookies.txt)"]
        P2["02_saved_extractor.py\n(IG/FB Saved Scraper + yt-dlp)"]
        P3["03_media_music_mixer.py\n(FFmpeg 20%/85% Mix + 9:16 Card Reel)"]
        P4["04_ig_publisher_commenter.py\n(Instagram Reels & AI Commenter)"]
        P5["05_fb_publisher_commenter.py\n(Facebook Reels & AI Commenter)"]
        P6["Anti-Ban Guardrails\n(3.5-9.2s Jitter + 50-90ms Typing)"]
    end

    subgraph Intelligence ["Cognitive Brain & Multi-Model LLM"]
        Brain["Story Bank Receipts + Voice Profile"]
        Humanizer["82-Rule Strict Humanizer Gate"]
        LLM["Local Ollama (qwen2.5 / llama3.2)\n+ OpenRouter Free Fallback"]
    end

    Interfaces --> Pipeline
    Pipeline <--> Excel
    Pipeline --> Intelligence

G:\Meta agent\
│
├── chrome_profile\              # Persistent Chrome user data (FB & IG cookies + Netscape cookies.txt for yt-dlp)
├── saved_videos\                # Downloaded raw .mp4 files from FB & IG Saved collections
│   ├── instagram\
│   └── facebook\
├── music_library\               # Local .mp3 songs/audio tracks to attach to Posts & Reels
├── ready_to_post\               # Final processed 1080x1920 Reels & Image-Music videos ready for upload
├── logs\                        # Error logs and execution history
├── storage\                     # Local SQLite database (meta_agent.db) synced with Excel
│
├── saved_videos_log.xlsx        # Master spreadsheet (ID, Platform, Hook, Caption, Creator, URL, MP4, Music, Status, Posted URL)
├── config.json                  # Usernames, posting schedules, human-delay timings, Ollama model name
│
├── 01_login_setup.py            # Phase 1: Run ONCE to log into Facebook & Instagram manually (or inject cookies)
├── 02_saved_extractor.py        # Phase 2: Scrapes Saved posts, downloads videos via yt-dlp, updates Excel sheet
├── 03_media_music_mixer.py      # Phase 3: FFmpeg engine to combine music + video or static image + music
├── 04_ig_publisher_commenter.py # Phase 4 & 5: Playwright UI automation for Instagram Reels, Posts & Comments
├── 05_fb_publisher_commenter.py # Phase 4 & 5: Playwright UI automation for Facebook Reels, Posts & Comments
├── main_agent.py                # Master CLI / AI controller that orchestrates all modules
├── run.py                       # FastAPI Web Studio Dashboard launcher (:8005)
├── run_mcp.py                   # Model Context Protocol (MCP) Server for Claude Desktop & Cursor
└── launch.bat                   # 1-Click Windows Studio Launcher

launch.bat

# Clone the repository
git clone https://github.com/muhdfaisalwork-gif/meta-agent.git
cd meta-agent

# Install dependencies
pip install -r requirements.txt
playwright install chromium

# Launch Web Studio Server (http://localhost:8005)
python run.py

# Phase 1: Log into Instagram (Tab 1) & Facebook (Tab 2) once in persistent Chrome
python 01_login_setup.py

# Phase 2: Scrape Saved Reels/Videos via yt-dlp & update saved_videos_log.xlsx
python 02_saved_extractor.py --platform both --max 5

# Phase 3 (Scenario A): Batch mix downloaded Reels with local MP3s (20% original / 85% music)
python 03_media_music_mixer.py --scenario batch

# Phase 3 (Scenario B): Turn a 9:16 quote card / static image + MP3 into a 15s 1080x1920 Reel
python 03_media_music_mixer.py --scenario b --hook-text "How we cut order execution latency from 1.2s to 420ms"

# Phase 4 & 5: Publish Reels & Auto-Reply to Comments on Instagram and Facebook
python 04_ig_publisher_commenter.py --action publish
python 04_ig_publisher_commenter.py --action auto-reply
python 05_fb_publisher_commenter.py --action publish
python 05_fb_publisher_commenter.py --action auto-reply

# Run Full End-to-End Autonomous Cycle
python main_agent.py --auto

python scripts/install_mcp.py

{
  "mcpServers": {
    "meta-nexus": {
      "command": "python",
      "args": ["G:\\Meta agent\\run_mcp.py"]
    }
  }
}

python -m pytest -v