From aradotso-trending-skills-37
Integrates OpenSpace MCP server into Claude Code and other AI agents for self-evolving skills, community skill sharing, and 46% token reduction. Useful for agent optimization and cost savings.
npx claudepluginhub joshuarweaver/cascade-ai-ml-agents-misc-1 --plugin aradotso-trending-skills-37This skill uses the workspace's default tool permissions.
```markdown
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).
Generates original PNG/PDF visual art via design philosophy manifestos for posters, graphics, and static designs on user request.
---
name: openspace-self-evolving-agents
description: Skill for using OpenSpace to make AI agents smarter, lower-cost, and self-evolving through skill sharing and collective intelligence.
triggers:
- add OpenSpace to my agent
- make my agent self-evolving
- reduce agent token costs
- share skills between agents
- plug OpenSpace into Claude Code
- set up skill evolution for my AI agent
- use OpenSpace MCP server
- evolve agent skills automatically
---
# OpenSpace: Self-Evolving Agent Skills
> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.
OpenSpace is a self-evolving engine that plugs into any MCP-compatible AI agent (Claude Code, Codex, OpenClaw, nanobot, Cursor, etc.) and gives it three superpowers: **self-evolving skills** (auto-fix, auto-improve, auto-learn), **collective agent intelligence** (shared skill community), and **token efficiency** (46% fewer tokens, 4.2× better economic output on real-world tasks).
---
## Installation
```bash
git clone https://github.com/HKUDS/OpenSpace.git && cd OpenSpace
pip install -e .
openspace-mcp --help # verify installation
Node.js ≥ 20 is required only for the local dashboard frontend.
Add OpenSpace as an MCP server in your agent's config file:
{
"mcpServers": {
"openspace": {
"command": "openspace-mcp",
"toolTimeout": 600,
"env": {
"OPENSPACE_HOST_SKILL_DIRS": "/path/to/your/agent/skills",
"OPENSPACE_WORKSPACE": "/path/to/OpenSpace",
"OPENSPACE_API_KEY": "$OPENSPACE_API_KEY"
}
}
}
}
Then copy the two bootstrap host skills into your agent's skills directory:
cp -r OpenSpace/openspace/host_skills/delegate-task/ /path/to/your/agent/skills/
cp -r OpenSpace/openspace/host_skills/skill-discovery/ /path/to/your/agent/skills/
These two skills teach the agent when and how to use OpenSpace — no additional prompting needed.
delegate-task — delegate complex tasks to OpenSpace for execution with evolved skillsskill-discovery — let the agent search for and download community skillsCredentials (LLM API key, model) are auto-detected from the agent's own config.
OPENSPACE_API_KEYis optional — local features work without it.
Create a .env file (see openspace/.env.example):
# openspace/.env
ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY # or OPENAI_API_KEY, etc.
OPENSPACE_API_KEY=$OPENSPACE_API_KEY # optional, for cloud community
Then run interactively or with a query:
# Interactive REPL
openspace
# One-shot task
openspace --model "anthropic/claude-sonnet-4-5" \
--query "Create a monitoring dashboard for my Docker containers"
import asyncio
from openspace import OpenSpace
async def main():
async with OpenSpace() as cs:
# Execute a task — skills evolve automatically
result = await cs.execute(
"Analyze GitHub trending repos and create a report"
)
print(result["response"])
# Inspect which skills evolved during this session
for skill in result.get("evolved_skills", []):
print(f" Evolved: {skill['name']} (origin: {skill['origin']})")
asyncio.run(main())
import asyncio
from openspace import OpenSpace
async def run_task():
async with OpenSpace(model="openai/gpt-4o") as cs:
result = await cs.execute(
"Build a payroll calculator from this union contract PDF"
)
print(result["response"])
print(f"Tokens used: {result.get('token_usage')}")
asyncio.run(run_task())
| Command | Description |
|---|---|
openspace | Interactive agent REPL |
openspace --query "..." | Execute a single task |
openspace --model "provider/model" | Specify LLM model |
openspace-mcp | Start the MCP server (for agent integration) |
openspace-dashboard --port 7788 | Start local dashboard API backend |
openspace-download-skill <skill_id> | Download a skill from the cloud community |
openspace-upload-skill /path/to/skill/ | Upload a local skill to the cloud community |
Register at open-space.cloud to get an API key, then set it in env:
export OPENSPACE_API_KEY="$OPENSPACE_API_KEY"
Or add to .env:
OPENSPACE_API_KEY=$OPENSPACE_API_KEY
openspace-download-skill skill_abc123
openspace-upload-skill ./openspace/skills/my-evolved-skill/
Skills can be set to public, private, or team-only access.
Visualize skill evolution lineage, browse versions, compare diffs, and inspect execution sessions.
# Terminal 1 — backend API
openspace-dashboard --port 7788
# Terminal 2 — frontend dev server
cd frontend
npm install # only once
npm run dev # opens at http://localhost:5173
Dashboard panels:
Skills live as directories under openspace/skills/. Each skill directory contains:
my-skill/
SKILL.md # skill description and usage instructions
skill.py # implementation (Python)
requirements.txt # optional dependencies
See openspace/skills/README.md for the full authoring guide.
Example minimal skill:
# openspace/skills/fetch-url/skill.py
import httpx
async def fetch_url(url: str) -> dict:
"""Fetch content from a URL and return text and status."""
async with httpx.AsyncClient(timeout=30) as client:
response = await client.get(url)
return {
"status": response.status_code,
"text": response.text[:4000],
"url": url,
}
| Variable | Required | Description |
|---|---|---|
OPENSPACE_HOST_SKILL_DIRS | For Path A | Path to agent's skills directory |
OPENSPACE_WORKSPACE | For Path A | Path to OpenSpace repo root |
OPENSPACE_API_KEY | Optional | Cloud community access key |
ANTHROPIC_API_KEY | If using Claude | Anthropic API key |
OPENAI_API_KEY | If using OpenAI | OpenAI API key |
All env vars can be set in openspace/.env (copy from openspace/.env.example).
async with OpenSpace() as cs:
result = await cs.execute(
"Prepare a tax return summary from the 15 PDFs in ./documents/tax/"
)
# OpenSpace selects or evolves the right skills automatically
print(result["response"])
# Agent A evolves a skill, upload it
openspace-upload-skill ./openspace/skills/tax-return-summarizer/
# Agent B downloads and uses it immediately
openspace-download-skill skill_tax_return_abc123
Skills auto-track: performance, error rates, and execution success. View in the dashboard or query programmatically:
async with OpenSpace() as cs:
result = await cs.execute("Show skill performance metrics for the last 7 days")
print(result["response"])
claude_desktop_config.json or .claude/mcp.json){
"mcpServers": {
"openspace": {
"command": "openspace-mcp",
"toolTimeout": 600,
"env": {
"OPENSPACE_HOST_SKILL_DIRS": "/Users/me/.claude/skills",
"OPENSPACE_WORKSPACE": "/Users/me/OpenSpace",
"OPENSPACE_API_KEY": "$OPENSPACE_API_KEY"
}
}
}
}
Follow the same MCP config pattern — consult openspace/host_skills/README.md for agent-specific paths and any extra flags.
openspace-mcp not found after install
pip install -e . # re-run from repo root
which openspace-mcp
Skills not evolving / auto-fix not triggering
delegate-task and skill-discovery host skills are copied to your agent's skills directory.OPENSPACE_WORKSPACE points to the repo root (where openspace/skills/ lives).Cloud upload/download fails
OPENSPACE_API_KEY is set and valid (register at open-space.cloud).Dashboard frontend won't start
node --versionnpm install inside the frontend/ directory before npm run dev.LLM credentials not picked up
openspace/.env.