| name | openspace |
| description | Self-evolving skill system - skills that auto-fix, auto-improve, and auto-learn from usage. Provides collective intelligence where agents share improvements, 46% token reduction through skill evolution, and quality monitoring that tracks performance. Use for complex multi-step tasks, skill evolution, or accessing cloud skill community. |
OpenSpace: Self-Evolving Skill System
What it does: OpenSpace is a self-evolving engine that makes AI agents smarter and more cost-efficient. Skills automatically learn, fix themselves, and improve over time. One agent's improvement becomes every agent's upgrade through cloud skill sharing.
Core Capabilities
🧬 Self-Evolution
- AUTO-FIX — When a skill breaks, it fixes itself instantly
- AUTO-IMPROVE — Successful patterns become better skill versions
- AUTO-LEARN — Captures winning workflows from actual usage
- Quality monitoring — Tracks skill performance, error rates, execution success
🌐 Collective Intelligence
- Shared evolution — One agent's improvement becomes every agent's upgrade
- Network effects — More agents → richer data → faster evolution
- Easy sharing — Upload and download evolved skills with one command
- Access control — Public, private, or team-only access per skill
💰 Token Efficiency
- 46% fewer tokens through skill reuse and evolution
- 4.2× better performance on real-world professional tasks
- Stop repeating work → Reuse successful solutions
- Small updates only → Fix what's broken, don't rebuild everything
CLI vs MCP: Token-Saving Priority
ALWAYS try CLI first. MCP tools consume tokens through protocol overhead; CLI calls via Bash are token-free beyond the command itself.
Priority Order:
1. OpenSpace CLI → Direct Bash execution (token-free)
2. OpenSpace MCP → Protocol overhead (use when CLI insufficient)
When to use CLI (Primary):
- ✅ Task execution:
openspace --query "task description"
- ✅ Skill download:
openspace-download-skill <skill_id>
- ✅ Skill upload:
openspace-upload-skill /path/to/skill/dir
- ✅ Quick task execution without needing structured results back
When to escalate to MCP (Backup):
- ⬆️ Need structured output parsed into conversation context
- ⬆️ Multi-step workflows where MCP state persistence helps
- ⬆️ Integration with other MCP tools in a single flow
- ⬆️ Automatic skill evolution tracking across tool calls
- ⬆️ Need result objects for conditional logic
CLI saves ~200-500 tokens per call compared to MCP. Over 20 OpenSpace calls, that's 4K-10K tokens saved.
When to Use OpenSpace
Use OpenSpace for:
- ✅ Complex multi-step tasks that would benefit from evolved, battle-tested workflows
- ✅ Repeated similar tasks where skill evolution can save tokens over time
- ✅ Tasks with unclear approaches — OpenSpace can search and apply community skills
- ✅ Skill evolution — automatically fix broken skills or derive improved versions
- ✅ Knowledge sharing — upload successful patterns or download community skills
Don't use OpenSpace for:
- ❌ Simple one-off tasks with clear solutions
- ❌ Tasks that already have well-defined local skills
- ❌ Quick reads, simple calculations, or trivial operations
CLI Commands (Use These First)
OpenSpace provides a standalone CLI for token-free execution:
openspace --query "Create a monitoring dashboard for Docker containers"
openspace-download-skill <skill_id>
openspace-upload-skill /path/to/skill/dir
openspace-dashboard --port 7788
CLI examples:
openspace --query "Generate a Python script to process CSV files"
openspace-download-skill sk_abc123xyz
Search is MCP-only — use mcp__openspace__search_skills tool:
mcp__openspace__search_skills(
query="csv processing",
source="all",
limit=10
)
Available MCP Tools (Use as Backup)
OpenSpace exposes 4 MCP tools via the openspace MCP server when CLI is insufficient:
1. mcp__openspace__execute_task
Execute complex tasks through OpenSpace's grounding agent with automatic skill evolution.
Parameters:
task (required) — Task description in natural languagemax_iterations (optional, default: 20) — Maximum reasoning loopssearch_scope (optional, default: "all") — "local", "cloud", or "all"
When to use:
- Multi-step tasks requiring tool orchestration
- Tasks that might benefit from evolved skills
- Complex workflows that Claude Code struggles with
Returns:
response — Task result/summaryevolved_skills — List of skills that evolved during execution (FIX/DERIVED/CAPTURED)status — "completed" or "failed"
2. mcp__openspace__search_skills
Search for skills in local registry and cloud community.
Parameters:
query (required) — Search query (natural language or keywords)scope (optional, default: "all") — "local", "cloud", or "all"top_k (optional, default: 5) — Number of results to return
When to use:
- Before starting a complex task — check if community skills exist
- Discovering reusable patterns for common workflows
- Finding specialized skills for specific domains
Returns:
- List of matching skills with:
name — Skill namedescription — What it doessource — "local" or "cloud"skill_id — Unique identifier (for cloud skills)
3. mcp__openspace__fix_skill
Repair a broken or outdated skill.
Parameters:
skill_name (required) — Name of the skill to fixerror_context (optional) — Error message or failure contextsuggestion (optional) — Specific fix suggestion
When to use:
- A skill failed due to API changes or outdated instructions
- Tool behavior changed and skill needs updating
- You notice a skill producing incorrect results
Returns:
success — Boolean indicating if fix succeedednew_version — Version number of the fixed skilldiff — Changes made to the skill
4. mcp__openspace__upload_skill
Upload a skill to the cloud community.
Parameters:
skill_dir (required) — Path to skill directoryvisibility (optional, default: "private") — "public", "private", or "group"group_id (optional) — Group ID for group-only sharing
When to use:
- You've created or evolved a valuable skill worth sharing
- You want to back up a skill to the cloud
- Sharing skills within a team/group
Requires: OPENSPACE_API_KEY in environment
Returns:
skill_id — Cloud skill identifierurl — Skill URL on open-space.cloud
Host Skills Integration
Two host skills are automatically installed to .claude/skills/:
1. delegate-task
Teaches Claude Code when and how to delegate complex tasks to OpenSpace.
Teaches:
- When to use
execute_task vs handling directly
- How to interpret OpenSpace results
- When to trigger skill evolution (
fix_skill)
- How to upload successful patterns (
upload_skill)
2. skill-discovery
Teaches Claude Code how to search and discover skills before starting work.
Teaches:
- When to search for skills (before complex tasks)
- How to evaluate search results (local vs cloud)
- Decision: follow skill yourself, delegate to OpenSpace, or skip
- How to import cloud skills to local registry
Skill Evolution Modes
OpenSpace evolves skills in three ways:
🔧 FIX
Repairs broken or outdated instructions in-place. Same skill, new version.
Triggers:
- Skill execution failure
- Tool degradation (success rate drops)
- API/tool behavior changes
🚀 DERIVED
Creates enhanced or specialized versions from parent skills. New skill directory.
Triggers:
- Successful pattern improvement opportunity
- Specialization for specific use case
- Combination of multiple complementary skills
✨ CAPTURED
Extracts novel reusable patterns from successful executions. Brand new skill.
Triggers:
- Novel workflow emerges from multi-step execution
- Repeatable pattern identified across tasks
- Successful recovery strategy worth preserving
Environment Configuration
Required:
- Python 3.12+
pip install -e tools/openspace (auto-handled by setup.sh)
Optional:
OPENSPACE_API_KEY — Cloud skill access (register at https://open-space.cloud)OPENSPACE_WORKSPACE — Custom workspace path (default: tools/openspace)OPENSPACE_DEBUG — Enable debug logging
LLM credentials auto-detected from:
- Provider-native env vars (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)
- OPENSPACE_LLM_* overrides (if needed)
Workflow Example
Before OpenSpace:
User: "Create a monitoring dashboard for Docker containers"
Claude: [Reasons from scratch, writes code, debugs, 50K tokens]
With OpenSpace:
User: "Create a monitoring dashboard for Docker containers"
Claude: [Searches skills via skill-discovery]
→ Found: docker-monitoring, dashboard-layout-v3, data-polling
→ Delegates to execute_task
OpenSpace: [Applies evolved skills, auto-fixes issues, 27K tokens]
→ Returns: Working dashboard
→ CAPTURED: docker-stats-api-fallback (new skill)
Next similar task:
User: "Create a monitoring dashboard for system processes"
Claude: [Searches skills]
→ Found: docker-stats-api-fallback (from previous task)
→ DERIVED: process-stats-api (specialized version)
OpenSpace: [Applies derived skill, 15K tokens]
Quality Monitoring
OpenSpace tracks:
- Skill metrics — Applied rate, completion rate, effective rate, fallback rate
- Tool metrics — Success rate, latency, flagged issues
- Code execution — Status, error patterns
Auto-triggers evolution when:
- Tool success rates drop below threshold
- Skill completion rates decline
- Error patterns emerge across multiple executions
- Manual evolution requested via
fix_skill
Cloud Skill Community
Browse: https://open-space.cloud (no account needed)
Register: Get OPENSPACE_API_KEY to:
- Upload evolved skills (public/private/group)
- Access private/group skills
- Track skill lineage and evolution
- View performance metrics
CLI tools:
openspace-download-skill <skill_id>
openspace-upload-skill /path/to/skill/dir
Dashboard (Optional)
Local web UI to browse skills, track lineage, compare diffs.
Requirements: Node.js ≥ 20
Start:
openspace-dashboard --port 7788
cd tools/openspace/frontend
npm install
npm run dev
Features:
- Browse local and cloud skills
- View evolution lineage graphs
- Compare skill diffs between versions
- Track workflow session history
- Monitor skill performance metrics
Troubleshooting
"Missing environment variables"
- Ensure LLM API key is set (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)
- For cloud features, add OPENSPACE_API_KEY
"Skill not found"
- Check
search_scope parameter (local/cloud/all)
- Verify skill was registered to local registry
- For cloud skills, ensure OPENSPACE_API_KEY is valid
"Evolution failed"
- Check execution logs in
tools/openspace/logs/
- Verify skill directory structure (must have SKILL.md)
- Ensure diff-based patches are valid
"MCP server timeout"
- Increase
toolTimeout in .mcp.json (default: 600s)
- Long evolutions may need 1200s+
- Check if evolution confirmation is hanging (requires user input)
Performance Benchmarks
From GDPVal benchmark (50 professional tasks, 6 industries):
| Metric | Result |
|---|
| Income vs Baseline | 4.2× higher (same LLM) |
| Token Reduction | 46% fewer tokens (Phase 2 vs Phase 1) |
| Quality Improvement | +30pp above best baseline |
| Value Capture | 72.8% ($11,484 / $15,764) |
Task categories improved:
- Documents & Correspondence: +3.3pp quality, −56% tokens
- Compliance & Forms: +18.5pp quality, −51% tokens
- Media Production: +5.8pp quality, −46% tokens
- Engineering: +8.7pp quality, −43% tokens
- Spreadsheets: +7.3pp quality, −37% tokens
- Strategy & Analysis: +1.0pp quality, −32% tokens
165 skills evolved across benchmark, with most focusing on:
- File format I/O (44 skills)
- Execution recovery (29 skills)
- Document generation (26 skills)
- Quality assurance (23 skills)
Source & Documentation
Bottom Line
OpenSpace turns individual agent learning into collective intelligence. Every task makes every agent smarter and more cost-efficient. Skills evolve automatically, agents share improvements, and token costs drop over time.
Use it when the task is complex enough that evolved, battle-tested workflows will save time and tokens compared to reasoning from scratch.
