copaw-ai-assistant

aradotso/trending-skills · updated Apr 8, 2026

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$npx skills add https://github.com/aradotso/trending-skills --skill copaw-ai-assistant
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Skill by ara.so — Daily 2026 Skills collection.

skill.md

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": SKIL
how to use copaw-ai-assistant

How to use copaw-ai-assistant on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add copaw-ai-assistant
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches copaw-ai-assistant from GitHub repository aradotso/trending-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/copaw-ai-assistant

Reload or restart Cursor to activate copaw-ai-assistant. Access the skill through slash commands (e.g., /copaw-ai-assistant) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.634 reviews
  • Layla Abebe· Dec 16, 2024

    Solid pick for teams standardizing on skills: copaw-ai-assistant is focused, and the summary matches what you get after install.

  • Ganesh Mohane· Dec 12, 2024

    Registry listing for copaw-ai-assistant matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ishan Rao· Dec 12, 2024

    copaw-ai-assistant has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Emma Iyer· Nov 7, 2024

    Registry listing for copaw-ai-assistant matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Rahul Santra· Nov 3, 2024

    Solid pick for teams standardizing on skills: copaw-ai-assistant is focused, and the summary matches what you get after install.

  • James Kim· Oct 26, 2024

    Useful defaults in copaw-ai-assistant — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Pratham Ware· Oct 22, 2024

    I recommend copaw-ai-assistant for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yusuf Robinson· Sep 9, 2024

    copaw-ai-assistant has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Fatima Martin· Sep 5, 2024

    copaw-ai-assistant is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Zara Brown· Sep 5, 2024

    Solid pick for teams standardizing on skills: copaw-ai-assistant is focused, and the summary matches what you get after install.

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