copaw-ai-assistant（Copa AI 助理）

CoPaw AI Assistant Skill

Skill by ara.so — Daily 2026 Skills collection.

CoPaw is a personal AI assistant framework you deploy on your own machine or in the cloud. It connects to multiple chat platforms (DingTalk, Feishu, QQ, Discord, iMessage, Telegram, Mattermost, Matrix, MQTT) through a single agent, supports custom Python skills, scheduled cron jobs, local and cloud LLMs, and provides a web Console at http://127.0.0.1:8088/.

Installation

pip (recommended if Python 3.10–3.13 is available)

pip install copaw
copaw init --defaults    # non-interactive setup with sensible defaults
copaw app                # starts the web Console + backend

Script install (no Python setup required)

macOS / Linux:

curl -fsSL https://copaw.agentscope.io/install.sh | bash
# With Ollama support:
curl -fsSL https://copaw.agentscope.io/install.sh | bash -s -- --extras ollama
# Multiple extras:
curl -fsSL https://copaw.agentscope.io/install.sh | bash -s -- --extras ollama,llamacpp

Windows CMD:

curl -fsSL https://copaw.agentscope.io/install.bat -o install.bat && install.bat

Windows PowerShell:

irm https://copaw.agentscope.io/install.ps1 | iex

After script install, open a new terminal:

copaw init --defaults
copaw app

Install from source

git clone https://github.com/agentscope-ai/CoPaw.git
cd CoPaw
pip install -e ".[dev]"
copaw init --defaults
copaw app

CLI Reference

copaw init                  # interactive workspace setup
copaw init --defaults       # non-interactive setup
copaw app                   # start the Console (http://127.0.0.1:8088/)
copaw app --port 8090       # use a custom port
copaw --help                # list all commands

Workspace Structure

After copaw init, a workspace is created (default: ~/.copaw/workspace/):

~/.copaw/workspace/
├── config.yaml          # agent, provider, channel configuration
├── skills/              # custom skill files (auto-loaded)
│   └── my_skill.py
├── memory/              # conversation memory storage
└── logs/                # runtime logs

Configuration (config.yaml)

copaw init generates this file. Edit it directly or use the Console UI.

LLM Provider (OpenAI-compatible)

providers:
  - id: openai-main
    type: openai
    api_key: ${OPENAI_API_KEY}        # use env var reference
    model: gpt-4o
    base_url: https://api.openai.com/v1

  - id: local-ollama
    type: ollama
    model: llama3.2
    base_url: http://localhost:11434

Agent Settings

agent:
  name: CoPaw
  language: en                        # en, zh, ja, etc.
  provider_id: openai-main
  context_limit: 8000

Channel: DingTalk

channels:
  - type: dingtalk
    app_key: ${DINGTALK_APP_KEY}
    app_secret: ${DINGTALK_APP_SECRET}
    agent_id: ${DINGTALK_AGENT_ID}
    mention_only: true                # only respond when @mentioned in groups

Channel: Feishu (Lark)

channels:
  - type: feishu
    app_id: ${FEISHU_APP_ID}
    app_secret: ${FEISHU_APP_SECRET}
    mention_only: false

Channel: Discord

channels:
  - type: discord
    token: ${DISCORD_BOT_TOKEN}
    mention_only: true

Channel: Telegram

channels:
  - type: telegram
    token: ${TELEGRAM_BOT_TOKEN}

Channel: QQ

channels:
  - type: qq
    uin: ${QQ_UIN}
    password: ${QQ_PASSWORD}

Channel: Mattermost

channels:
  - type: mattermost
    url: ${MATTERMOST_URL}
    token: ${MATTERMOST_TOKEN}
    team: my-team

Channel: Matrix

channels:
  - type: matrix
    homeserver: ${MATRIX_HOMESERVER}
    user_id: ${MATRIX_USER_ID}
    access_token: ${MATRIX_ACCESS_TOKEN}

Custom Skills

Skills are Python files placed in ~/.copaw/workspace/skills/. They are auto-loaded when CoPaw starts — no registration step needed.

Minimal skill structure

# ~/.copaw/workspace/skills/weather.py

SKILL_NAME = "get_weather"
SKILL_DESCRIPTION = "Get current weather for a city"

# Tool schema (OpenAI function-calling format)
SKILL_SCHEMA = {
    "type": "function",
    "function": {
        "name": SKILL_NAME,
        "description": SKILL_DESCRIPTION,
        "parameters": {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "City name, e.g. 'Tokyo'"
                }
            },
            "required": ["city"]
        }
    }
}

def get_weather(city: str) -> str:
    """Fetch weather data for the given city."""
    import os
    import requests

    api_key = os.environ["OPENWEATHER_API_KEY"]
    url = f"https://api.openweathermap.org/data/2.5/weather"
    resp = requests.get(url, params={"q": city, "appid": api_key, "units": "metric"})
    resp.raise_for_status()
    data = resp.json()
    temp = data["main"]["temp"]
    desc = data["weather"][0]["description"]
    return f"{city}: {temp}°C, {desc}"

Skill with async support

# ~/.copaw/workspace/skills/summarize_url.py

SKILL_NAME = "summarize_url"
SKILL_DESCRIPTION = "Fetch and summarize the content of a URL"

SKILL_SCHEMA = {
    "type": "function",
    "function": {
        "name": SKILL_NAME,
        "description": SKILL_DESCRIPTION,
        "parameters": {
            "type": "object",
            "properties": {
                "url": {"type": "string", "description": "The URL to summarize"}
            },
            "required": ["url"]
        }
    }
}

async def summarize_url(url: str) -> str:
    import httpx

    async with httpx.AsyncClient(timeout=15) as client:
        resp = await client.get(url)
        text = resp.text[:4000]   # truncate for context limit
    return f"Content preview from {url}:\n{text}"

Skill returning structured data

# ~/.copaw/workspace/skills/list_files.py

import os
import json

SKILL_NAME = "list_files"
SKILL_DESCRIPTION = "List files in a directory"

SKILL_SCHEMA = {
    "type": "function",
    "function": {
        "name": SKILL_NAME,
        "description": SKILL_DESCRIPTION,
        "parameters": {
            "type": "object",
            "properties": {
                "path": {
                    "type": "string",
                    "description": "Absolute or relative directory path"
                },
                "extension": {
                    "type": "string",
                    "description": "Filter by extension, e.g. '.py'. Optional."
                }
            },
            "required": ["path"]
        }
    }
}

def list_files(path: str, extension: str = "") -> str:
    entries = os.listdir(os.path.expanduser(path))
    if extension:
        entries = [e for e in entries if e.endswith(extension)]
    return json.dumps(sorted(entries))

Cron / Scheduled Tasks

Define cron jobs in config.yaml to run skills on a schedule and push results to a channel:

cron:
  - id: daily-digest
    schedule: "0 8 * * *"            # every day at 08:00
    skill: get_weather
    skill_args:
      city: "Tokyo"
    channel_id: dingtalk-main         # matches a channel id below
    message_template: "Good morning! Today's weather: {result}"

  - id: hourly-news
    schedule: "0 * * * *"
    skill: fetch_tech_news
    channel_id: discord-main

Local Model Setup

Ollama

# Install Ollama: https://ollama.ai
ollama pull llama3.2
ollama serve   # starts on http://localhost:11434

# config.yaml
providers:
  - id: ollama-local
    type: ollama
    model: llama3.2
    base_url: http://localhost:11434

LM Studio

providers:
  - id: lmstudio-local
    type: lmstudio
    model: lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF
    base_url: http://localhost:1234/v1

llama.cpp (extra required)

pip install "copaw[llamacpp]"

providers:
  - id: llamacpp-local
    type: llamacpp
    model_path: /path/to/model.gguf

Tool Guard (Security)

Tool Guard blocks risky tool calls and requires user approval before execution. Configure in config.yaml:

agent:
  tool_guard:
    enabled: true
    risk_patterns:
      - "rm -rf"
      - "DROP TABLE"
      - "os.system"
    auto_approve_low_risk: true

When a call is blocked, the Console shows an approval prompt. The user can approve or deny before the tool runs.

Token Usage Tracking

Token usage is tracked automatically and visible in the Console dashboard. Access programmatically:

# In a skill or debug script
from copaw.telemetry import get_usage_summary

summary = get_usage_summary()
print(summary)
# {'total_tokens': 142300, 'prompt_tokens': 98200, 'completion_tokens': 44100, 'by_provider': {...}}

Environment Variables

Set these before running copaw app, or reference them in config.yaml as ${VAR_NAME}:

# LLM providers
export OPENAI_API_KEY=...
export ANTHROPIC_API_KEY=...

# Channels
export DINGTALK_APP_KEY=...
export DINGTALK_APP_SECRET=...
export DINGTALK_AGENT_ID=...

export FEISHU_APP_ID=...
export FEISHU_APP_SECRET=...

export DISCORD_BOT_TOKEN=...
export TELEGRAM_BOT_TOKEN=...

export QQ_UIN=...
export QQ_PASSWORD=...

export MATTERMOST_URL=...
export MATTERMOST_TOKEN=...

export MATRIX_HOMESERVER=...
export MATRIX_USER_ID=...
export MATRIX_ACCESS_TOKEN=...

# Custom skill secrets
export OPENWEATHER_API_KEY=...

Common Patterns

Pattern: Morning briefing to DingTalk

# config.yaml excerpt
channels:
  - id: dingtalk-main
    type: dingtalk
    app_key: ${DINGTALK_APP_KEY}
    app_secret: ${DINGTALK_APP_SECRET}
    agent_id: ${DINGTALK_AGENT_ID}

cron:
  - id: morning-brief
    schedule: "30 7 * * 1-5"         # weekdays 07:30
    skill: daily_briefing
    channel_id: dingtalk-main

# skills/daily_briefing.py
SKILL_NAME = "daily_briefing"
SKILL_DESCRIPTION = "Compile a morning briefing with weather and news"

SKILL_SCHEMA = {
    "type": "function",
    "function": {
        "name": SKILL_NAME,
        "description": SKILL_DESCRIPTION,
        "parameters": {"type": "object", "properties": {}, "required": []}
    }
}

def daily_briefing() -> str:
    import os, requests, datetime

    today = datetime.date.today().strftime("%A, %B %d")
    # Add your own data sources here
    return f"Good morning! Today is {today}. Have a productive day!"

Pattern: Multi-channel broadcast

# skills/broadcast.py
SKILL_NAME = "broadcast_message"
SKILL_DESCRIPTION = "Send a message to all configured channels"

SKILL_SCHEMA = {
    "type": "function",
    "function": {
        "name": SKILL_NAME,
        "description": SKILL_DESCRIPTION,
        "parameters": {
            "type": "object",
            "properties": {
                "message": {"type": "string", "description": "Message to broadcast"}
            },
            "required": ["message"]
        }
    }
}

def broadcast_message(message: str) -> str:
    # CoPaw handles routing; return the message and let the agent deliver it
    return f"[BROADCAST] {message}"

Pattern: File summarization skill

# skills/summarize_file.py
SKILL_NAME = "summarize_file"
SKILL_DESCRIPTION = "Read and summarize a local file"

SKILL_SCHEMA = {
    "type": "function",
    "function": {
        "name": SKILL_NAME,
        "description": SKILL_DESCRIPTION,
        "parameters": {
            "type": "object",
            "properties": {
                "file_path": {"type": "string", "description": "Absolute path to the file"}
            },
            "required": ["file_path"]
        }
    }
}

def summarize_file(file_path: str) -> str:
    import os

    path = os.path.expanduser(file_path)
    if not os.path.exists(path):
        return f"File not found: {path}"

    with open(path, "r", encoding="utf-8", errors="ignore") as f:
        content = f.read(8000)

    return f"File: {path}\nSize: {os.path.getsize(path)} bytes\nContent preview:\n{content}"

Troubleshooting

Console not accessible at port 8088

# Use a different port
copaw app --port 8090

# Check if another process is using 8088
lsof -i :8088    # macOS/Linux
netstat -ano | findstr :8088   # Windows

Skills not loading

• Confirm the skill file is in ~/.copaw/workspace/skills/
• Confirm SKILL_NAME, SKILL_DESCRIPTION, SKILL_SCHEMA, and the handler function are all defined at module level
• Check ~/.copaw/workspace/logs/ for import errors
• Restart copaw app after adding new skill files

Channel not receiving messages

1. Verify credentials are set correctly (env vars or config.yaml)
2. Check the Console → Channels page for connection status
3. For DingTalk/Feishu/Discord with mention_only: true, the bot must be @mentioned
4. Discord messages over 2000 characters are split automatically — ensure the bot has Send Messages permission

LLM provider connection fails

# Test provider from CLI (Console → Providers → Test Connection)
# Or check logs:
tail -f ~/.copaw/workspace/logs/copaw.log

• For Ollama: confirm ollama serve is running and base_url matches
• For OpenAI-compatible APIs: verify base_url ends with /v1
• LLM calls auto-retry with exponential backoff — transient failures resolve automatically

Windows encoding issues

# Set UTF-8 encoding for CMD
chcp 65001

Or set in environment:

export PYTHONIOENCODING=utf-8

Workspace reset

# Reinitialize workspace (preserves skills/)
copaw init

# Full reset (destructive)
rm -rf ~/.copaw/workspace
copaw init --defaults

ModelScope Cloud Deployment

For one-click cloud deployment without local setup:

1. Visit ModelScope CoPaw Studio
2. Fork the studio to your account
3. Set environment variables in the studio settings
4. Start the studio — Console is accessible via the studio URL

Key Links

• Documentation: https://copaw.agentscope.io/
• Channel setup guides: https://copaw.agentscope.io/docs/channels
• Release notes: https://agentscope-ai.github.io/CoPaw/release-notes
• GitHub: https://github.com/agentscope-ai/CoPaw
• PyPI: https://pypi.org/project/copaw/
• Discord community: https://discord.gg/eYMpfnkG8h