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ontomcp-driver

@mareasw⭐ 22 stars

Use OntoSkills MCP tools to discover and apply structured skill knowledge.

Choose how to use this skill

You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.

1. Native installer

This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.

Do not guess an installer command or replace an existing version without reviewing the diff.

2. Complete package recommended

Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.

No complete ProSkills package is published for this listing yet.

3. Prompt-only

Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.

Need only the instruction file?

Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.

No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.

—/10

// RATINGS

⭐GitHub Stars
⭐⭐ 22 on GitHubGitHub ↗

Growing

🟢ProSkills Score
—
📍

Not yet listed on ClawHub or SkillsMP

// README


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?

ProblemSolution
LLMs interpret text differently each timeSPARQL returns exact answers
50+ skill files = context overflowQuery only what's needed
No verifiable structure for relationshipsOWL 2 formal semantics
Small models can't read complex skillsDemocratized 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

ComponentLanguageDescription
OntoCorePythonNeuro-symbolic compiler: SKILL.md → OWL 2 ontology
OntoMCPRustMCP server with sub-ms SPARQL queries
OntoStoreGitHubVersioned skill registry
CLINode.jsOne-command installer (npx ontoskills)

Documentation


License

MIT License — see LICENSE for details.

© 2026 MareaSW

// HOW IT'S BUILT

KEY FILES

site/public/agent-skills/ontomcp-driver/SKILL.mdREADME.md

// REPO STATS

22 stars