"zoom.send_chat|||text=Hello" → message typed into Zoom
"weibo.trends"                 → real-time trending topics  
"terminal.run|||cmd=dir"       → shell command executed

┌──────────┐     ┌──────────────┐     ┌──────────┐
│ Any Agent │ ──▶ │ App Adapter  │ ──▶ │ Real App │
│ (Claude,  │     │ (MCP Server) │     │ (Zoom,   │
│  Codex,   │     │              │     │  WeChat, │
│  Cursor)  │     │ Strategies:  │     │  Excel,  │
│           │     │ • http/api   │     │  etc.)   │
│           │     │ • cdp/browser│     │          │
│           │     │ • uia/desktop│     │          │
│           │     │ • shell/cmd  │     │          │
└──────────┘     └──────────────┘     └──────────┘

# One-line install (recommended)
pip install git+https://github.com/codenoob-jacky/app-adapter.git

# Or clone + dev install
git clone https://github.com/codenoob-jacky/app-adapter.git
cd app-adapter
pip install -e .

# With all optional deps (browser + desktop automation):
pip install "app-adapter[all] @ git+https://github.com/codenoob-jacky/app-adapter.git"

# Windows: scripts\install.bat   |   Mac/Linux: bash scripts/install.sh

app-adapter                          # Quick overview
app-adapter list                     # List all 26 apps
app-adapter scan                     # Find installable apps
app-adapter do "zoom.send_chat|||text=Hello"
app-adapter prompt --style brief     # Get contribution prompt for your agent
app-adapter server --port 8080       # Start HTTP server
app-adapter server --mcp             # Start MCP stdio server

from app_adapter import app_do, app_list, app_scan, get_agent_prompt

print(app_list())
app_do("terminal.run|||cmd=echo Hello from Python")
print(get_agent_prompt("brief"))  # Inject this into your agent

app-adapter server --port 8080
  -d '{"tool":"app_do","arguments":{"action":"zoom.send_chat|||text=Hello from AI"}}'

# 1. Discover what's on this system
app_scan()                     # → "Found Zoom, Slack, VS Code, Spotify..."

# 2. Register a new app  
app_register("spotify|||Spotify|||https://open.spotify.com|||cdp|||entertainment")

# 3. Teach it actions
app_learn("spotify|||search|||Search tracks|||cdp|||input[data-testid='search-input']|||fill")
app_learn("spotify|||play|||Play/pause|||cdp|||button[data-testid='play-button']|||click")

# 4. Test before sharing
app_test("spotify.search|||query=Bohemian Rhapsody")

# 5. Publish to community
app_publish("spotify")

# Other agents can then discover and install it:
app_search("spotify")          # → Found in registry
app_install("spotify")         # → Downloaded and merged
app_do("spotify.play")

# Get the contribution prompt
app-adapter prompt --style full

# Inject it into your agent's system message.
# Now your agent will:
#   - Discover unregistered apps with app_scan()
#   - Check the registry with app_search()
#   - Register + teach + test + publish autonomously

{
  "mcpServers": {
    "app-adapter": {
      "command": "app-adapter",
      "args": ["server", "--mcp"]
    }
  }
}

# codex.yaml or .codex/config.json
mcp:
  app-adapter:
    type: stdio
    command: app-adapter
    args: [server, --mcp]

// .cursor/mcp.json
{
  "mcpServers": {
    "app-adapter": {
      "command": "app-adapter",
      "args": ["server", "--mcp"]
    }
  }
}

from app_adapter import app_do, get_agent_prompt
# Inject the prompt + wrap tools. See docs/INTEGRATION.md for full examples.