Skill by ara.so — Daily 2026 Skills collection.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncopaw-ai-assistantExecute the skills CLI command in your project's root directory to begin installation:
Fetches copaw-ai-assistant from aradotso/trending-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate copaw-ai-assistant. Access via /copaw-ai-assistant in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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/.
pip install copaw
copaw init --defaults # non-interactive setup with sensible defaults
copaw app # starts the web Console + backend
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
git clone https://github.com/agentscope-ai/CoPaw.git
cd CoPaw
pip install -e ".[dev]"
copaw init --defaults
copaw app
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
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
config.yaml)copaw init generates this file. Edit it directly or use the Console UI.
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:
name: CoPaw
language: en # en, zh, ja, etc.
provider_id: openai-main
context_limit: 8000
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
channels:
- type: feishu
app_id: ${FEISHU_APP_ID}
app_secret: ${FEISHU_APP_SECRET}
mention_only: false
channels:
- type: discord
token: ${DISCORD_BOT_TOKEN}
mention_only: true
channels:
- type: telegram
token: ${TELEGRAM_BOT_TOKEN}
channels:
- type: qq
uin: ${QQ_UIN}
password: ${QQ_PASSWORD}
channels:
- type: mattermost
url: ${MATTERMOST_URL}
token: ${MATTERMOST_TOKEN}
team: my-team
channels:
- type: matrix
homeserver: ${MATRIX_HOMESERVER}
user_id: ${MATRIX_USER_ID}
access_token: ${MATRIX_ACCESS_TOKEN}
Skills are Python files placed in ~/.copaw/workspace/skills/. They are auto-loaded when CoPaw starts — no registration step needed.
# ~/.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}"
# ~/.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": SKILPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
aradotso/trending-skills
aradotso/trending-skills
aradotso/trending-skills
aradotso/trending-skills
bradsjm/hassio-addons
davila7/claude-code-templates
Solid pick for teams standardizing on skills: copaw-ai-assistant is focused, and the summary matches what you get after install.
Registry listing for copaw-ai-assistant matched our evaluation — installs cleanly and behaves as described in the markdown.
copaw-ai-assistant has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for copaw-ai-assistant matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: copaw-ai-assistant is focused, and the summary matches what you get after install.
Useful defaults in copaw-ai-assistant — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend copaw-ai-assistant for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
copaw-ai-assistant has been reliable in day-to-day use. Documentation quality is above average for community skills.
copaw-ai-assistant is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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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