ontomcp-driver

Apply structured OntoSkills MCP knowledge nodes to guide deterministic skill execution.

19|5|Updated Mar 15, 2026

Search Tags:#guidance #knowledge-management #skill-discovery #state-machine #execution-planning #ontoskills #ontomcp

Ready to run

npx skills add https://github.com/mareasw/ontoskills --skill ontomcp-driver

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ontomcp-driver
Source: https://github.com/mareasw/ontoskills/tree/main/site/public/agent-skills/ontomcp-driver
Command: npx skills add https://github.com/mareasw/ontoskills --skill ontomcp-driver

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Facilitates discovery and controlled application of structured skill knowledge within OntoSkills MCP, enabling deterministic planning and execution for AI agents.

Core Features & Use Cases

Prefetch knowledge with a query describing the task goal to surface relevant procedures, constraints, and anti-patterns.
Use search to find skills by keyword, alias, or structured filters.
Retrieve full skill context with get_skill_context to inspect procedures, constraints, and anti-patterns.
Validate multi-skill plans with evaluate_execution_plan against defined state chains.
Query epistemic rules to reason about cross-skill knowledge and safety constraints.

Quick Start

Call prefetch_knowledge with a task goal, review the returned knowledge nodes, and then use evaluate_execution_plan to validate a multi-skill plan before writing code.

Frequently Asked Questions about ontomcp-driver

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I validate multi-skill execution plans against state transitions for AI agents?▼

To validate multi-skill execution plans, use evaluate_execution_plan to check proposed state transitions against defined state chains. This ensures deterministic planning by verifying preconditions and critical rules before execution, yielding auditable outcomes.

What is structured knowledge management for deterministic skill execution in AI workflows?▼

Structured knowledge management for deterministic skill execution involves identifying and applying knowledge nodes to guide AI agents. It enforces preconditions, critical rules, and safe external tool interactions to ensure robust, auditable outcomes across multi-skill workflows.

How do I find relevant procedures and constraints by querying task goals?▼

You can find relevant procedures and constraints by calling prefetch_knowledge with a task goal description. This surfaces applicable knowledge nodes, anti-patterns, and structured procedures to guide deterministic execution planning.

Can I query epistemic rules to reason about cross-skill safety constraints?▼

Yes, you can query epistemic rules to reason about cross-skill knowledge and safety constraints. This allows you to evaluate critical rules and enforce safe interactions with external tools across complex multi-skill execution workflows.

What are the limitations of executing skills without enforcing preconditions?▼

Executing skills without enforcing preconditions leads to non-deterministic outcomes and unsafe external tool interactions. Bypassing state transition validation and critical rules removes auditability, risking unverified and failed multi-skill workflow execution.

Does ontomcp-driver support retrieving full context to inspect skill anti-patterns?▼

Yes, ontomcp-driver supports retrieving full context. Use get_skill_context to inspect complete procedures, constraints, and anti-patterns, enabling thorough review and validated execution planning before writing code.