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Ontoskills

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

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

README

OntoSkills: Neuro-Symbolic Skill Compiler

OntoSkills Logo Inline OntoSkills

🇬🇧 English🇨🇳 中文

The deterministic enterprise AI agent platform.

Neuro-symbolic architecture for the Agentic Web — OntoCoreOntoMCPOntoStore

OverviewGetting StartedRoadmapPhilosophy

Python 3.10+ Node.js 18+ OWL 2 RDF/Turtle SHACL Validation MIT License


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<br/>━━━━━━━━━━<br/>SKILL.md → .ttl<br/>LLM + SHACL"] -->|"compiles"| CENTER["OntoSkills<br/>━━━━━━━━━━<br/>OWL 2 Ontologies<br/>.ttl artifacts"]
    CENTER -->|"loads"| MCP["OntoMCP<br/>━━━━━━━━━━<br/>Rust SPARQL<br/>in-memory graph"]
    MCP <-->|"queries"| AGENT["AI Agent<br/>━━━━━━━━━━<br/>Deterministic<br/>reasoning"]

    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


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


License

MIT License — see LICENSE for details.

© 2026 MareaSW

from github.com/mareasw/ontoskills

Installing Ontoskills

This server has no published package — it is built from source. Open the repository and follow its README.

▸ github.com/mareasw/ontoskills

FAQ

Is Ontoskills MCP free?

Yes, Ontoskills MCP is free — one-click install via Unyly at no cost.

Does Ontoskills need an API key?

No, Ontoskills runs without API keys or environment variables.

Is Ontoskills hosted or self-hosted?

Self-hosted: the server runs locally on your machine via the install command above.

How do I install Ontoskills in Claude Desktop, Claude Code or Cursor?

Open Ontoskills on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.

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