MCP verified MIT Self-run

Ontoskills

Name: Ontoskills
Availability: InStock
Author: mareasw

mcp-mareasw-ontoskills · by mareasw

OWL 2 skill compiler for deterministic AI agents — transforms SKILL.md into validated RDF/ Turtle ontologies with SHACL gatekeeper

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$ agentstack add mcp-mareasw-ontoskills

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

✓ Prompt-injection patterns
✓ Secret / credential exfiltration
✓ Dangerous shell & filesystem operations
✓ Untrusted network calls
✓ Known-malicious package signatures

What it can access

✓ Network access No
✓ Filesystem access No
✓ Shell / process execution No
✓ Environment & secrets No
✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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Declared compatibility

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Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

OntoSkills

🇬🇧 English • 🇨🇳 中文

The deterministic enterprise AI agent platform.

Neuro-symbolic architecture for the Agentic Web — OntoCore • OntoMCP • OntoStore

Overview • Getting Started • Roadmap • Philosophy

What is OntoSkills?

OntoSkills transforms natural language skill definitions into validated OWL 2 ontologies — queryable knowledge graphs that enable deterministic reasoning for AI agents.

The problem: LLMs read skills probabilistically. Same query, different results. Long skill files burn tokens and confuse smaller models.

The solution: Compile skills to ontologies. Query with SPARQL. Get exact answers, every time.

flowchart LR
    CORE["OntoCore━━━━━━━━━━SKILL.md → .ttlLLM + SHACL"] -->|"compiles"| CENTER["OntoSkills━━━━━━━━━━OWL 2 Ontologies.ttl artifacts"]
    CENTER -->|"loads"| MCP["OntoMCP━━━━━━━━━━Rust SPARQLin-memory graph"]
    MCP |"queries"| AGENT["AI Agent━━━━━━━━━━Deterministicreasoning"]

    style CORE fill:#e91e63,stroke:#2a2a3e,color:#f0f0f5
    style CENTER fill:#abf9cc,stroke:#2a2a3e,color:#0d0d14
    style MCP fill:#92eff4,stroke:#2a2a3e,color:#0d0d14
    style AGENT fill:#6dc9ee,stroke:#2a2a3e,color:#0d0d14

Why OntoSkills?

| Problem | Solution | |---------|----------| | LLMs interpret text differently each time | SPARQL returns exact answers | | 50+ skill files = context overflow | Query only what's needed | | No verifiable structure for relationships | OWL 2 formal semantics | | Small models can't read complex skills | Democratized intelligence via graph queries |

For 100 skills: ~500KB text scan → ~1KB query

[→ Read the full philosophy](PHILOSOPHY.md)

Quick Start

# Install the MCP runtime and bootstrap your client
npx ontoskills install mcp --claude

# Or install the Python compiler separately
pip install ontocore
ontocore compile

→ Full installation guide

Components

| Component | Language | Description | |-----------|----------|-------------| | OntoCore | Python | Neuro-symbolic compiler: SKILL.md → OWL 2 ontology | | OntoMCP | Rust | MCP server with sub-ms SPARQL queries | | OntoStore | GitHub | Versioned skill registry | | CLI | Node.js | One-command installer (npx ontoskills) |

Documentation

Overview — What is OntoSkills and why it matters
Getting Started — Installation and first steps
Architecture — How the system works
Knowledge Extraction — Extracting value from skills
OntoStore — Browse and install skills
Roadmap — Development phases

License

MIT License — see [LICENSE](LICENSE) for details.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Author: mareasw
Source: mareasw/ontoskills
License: MIT
Homepage: https://ontoskills.sh

Install and usage instructions live in the source repository linked above.

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Versions

v0.1.0 Imported from the upstream source.