OWL 2 ontologies

Skills your agent can look up, not guess.

Deterministic ontological queries replace probabilistic skill discovery. Zero ambiguity, zero tokens wasted.

npx ontoskills install mcp
O(1)
Lookup
100%
Deterministic
OWL 2 DL
Reasoning
SPARQL Query
SELECT ?skill WHERE {
  ?skill oc:resolvesIntent "create_pdf" .
  ?skill oc:hasPayload ?payload .
}
→ Returns in 0.3ms
→ Always the same result
The problem

LLMs waste tokens guessing

Every time your agent uses a skill, it burns through a 4-step pipeline that's slow, expensive, and unreliable.

Read
1
Parse docs
Understand
2
Build context
Reason
3
Decide action
Execute
4
Run skill

Token waste

Large models burn thousands of tokens just parsing documentation files.

Inconsistency

Same query, different results. Every. Single. Time.

Confusion

Complex skills overwhelm smaller models with ambiguity.

No guarantees

LLMs interpret text probabilistically. Errors are inevitable.

The solution

Skip the guesswork. Query. Execute.

OntoSkills eliminates reading and understanding. An OWL 2 reasoner handles the heavy lifting — deterministically.

Read
Eliminated
Understand
Eliminated
Reason OWL 2
~
Reasoner
Execute
Deterministic
Agent "I need to create a PDF"
SPARQL SELECT ?skill WHERE { ?skill resolvesIntent "create_pdf" }
Result pdf-generator // deterministic match
Action Execute skill payload

No reading. No guessing. Just query → execute.

OntoClaw

The first natively ontological AI agent

Neuro-symbolic architecture. Ontological knowledge is native — no MCP bridge needed for OntoSkills.

Coming soon

Ready to make your agent deterministic?

Install OntoMCP
npx ontoskills install mcp