copaw-ai-assistant

仓库: aradotso/trending-skills
安装量: 69
排名: #11097

安装

npx skills add https://github.com/aradotso/trending-skills --skill copaw-ai-assistant

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 Verify credentials are set correctly (env vars or config.yaml ) Check the Console → Channels page for connection status For DingTalk/Feishu/Discord with mention_only: true , the bot must be @mentioned 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: Visit ModelScope CoPaw Studio Fork the studio to your account Set environment variables in the studio settings 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

← 返回排行榜