Open Ontologies is a Rust tool for planning, checking, applying, and rolling back changes to production ontologies, which are formal descriptions of concepts and their relationships. It helps ontology maintainers and reviewers inspect the consequences of edits and verify the resulting evidence with a Lean 4 checker. The catalogue includes add-ons such as skills, an instruction, a plugin, and an MCP integration for working with it.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fabio-rovai/open-ontologies --skill ontology-engineeringgit clone --depth 1 https://github.com/fabio-rovai/open-ontologiesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fabio-rovai/open-ontologies/ontology-engineering)<a href="https://agentmods.dev/skills/fabio-rovai/open-ontologies/ontology-engineering"><img src="https://agentmods.dev/badge/skills/fabio-rovai/open-ontologies/ontology-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fabio-rovai/open-ontologies/ontology-engineering"><img src="https://agentmods.dev/badge/skills/fabio-rovai/open-ontologies/ontology-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.02587 |
| Opus 5.5 | $0.00019 | $0.01035 |
| Sonnet 5.5 | $0.00009 | $0.00517 |
| Haiku 4.5 | $0.00005 | $0.00259 |
Grade A, and why
ontology-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ontology Engineering Workflow
You have access to the Open Ontologies MCP server, which provides 122 tools for AI-native ontology engineering backed by an in-memory Oxigraph triple store. 114 of them are advertised in a default build, and the other 8 require an optional Cargo feature and return an error without it: four need embeddings, two need plugins, and two need postgres or duckdb.
Core Workflow
When building or modifying ontologies, follow this workflow. Decide which tools to call and in what order based on results -- this is not a fixed pipeline.
1. Generate
- Understand the domain requirements (natural language, competency questions, methodology constraints)
- Generate Turtle/OWL directly -- you know OWL, RDF, BORO, 4D modeling natively
2. Validate and Load
- Call
onto_validateon the generated Turtle -- if it fails, fix syntax errors and re-validate - Call
onto_loadto load into the Oxigraph triple store. For repos that mount a folder of.ttlfiles via[general] ontology_dirs, preferonto_repo_loadso the same compile-cache / TTL-eviction path is exercised. Useonto_repo_listto discover candidate files. - Call
onto_statsto verify class count, property count, triple count match expectations
3. Verify
- Call
onto_lintto check for missing labels, comments, domains, ranges -- fix any issues found - Call
onto_querywith SPARQL to verify structure (expected classes, subclass hierarchies, competency questions) - If a reference ontology exists, call
onto_diffto compare
4. Iterate
- If any step reveals problems, fix the Turtle and restart from step 2
- Continue until validation passes, stats match, lint is clean, and SPARQL queries return expected results
5. Persist
- Call
onto_saveto write the final ontology to a .ttl file - Call
onto_versionto save a named snapshot for rollback
Cache and Multi-Ontology Loading
The server keeps a single active ontology in memory plus an on-disk N-Triples compile cache for everything it has parsed. Switch between several ontologies without paying re-parse costs:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago Changed 9db9b34c21fb
- 28d ago Changed 22ed28a76205
- 1mo ago Changed 1bd5e159ee62
- 1mo ago Changed 9ec3ad961560
- 1mo ago Changed c51f55ba6d84
- 1mo ago Changed 487545d1ac16
- 1mo ago First seen · 153 lines · 47 tokens per session scan A ddb6f26ce463
ontology-engineering is a skill published in the GitHub repository fabio-rovai/open-ontologies (1,502 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 2,587 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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