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 agentlas-ai/agentlas-desktop --skill ontology-term-resolutiongit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/ontology-term-resolution)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/ontology-term-resolution"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/ontology-term-resolution/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/agentlas-ai/agentlas-desktop/ontology-term-resolution"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/ontology-term-resolution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00253 | $0.03015 |
| Opus 5.5 | $0.00101 | $0.01206 |
| Sonnet 5.5 | $0.00051 | $0.00603 |
| Haiku 4.5 | $0.00025 | $0.00301 |
Grade A, and why
ontology-term-resolution 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 1mo 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.
This is a copy
97% identical to ontology-term-resolution — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ontology Term Resolution
When to use
Any time an ontology identifier is about to be written down or trusted: annotating a metadata column, filling a submission template, auditing a table someone else produced, or checking whether an ID in an old file is still current.
The rule
Never write an ontology ID from memory, and never accept one without checking it.
Ontology IDs are memorable in form and arbitrary in detail. A plausible-looking UBERON:0002108
is a real term (small intestine) that is not the liver, and nothing downstream will catch the
substitution — the ID is well-formed, the ontology is right, and the metadata is silently wrong.
Reviewers cannot spot it either, which is why these errors persist into published datasets.
Every ID this skill emits comes from a live OLS lookup. Every ID it is handed gets verified. Bioregistry, Identifiers.org, ZOOMA, and Ontobee answer prefix, landing-page, and shorthand questions — they do not replace that OLS check.
Which service
| Question | Script | Authority |
|---|---|---|
| What is the term for "left ventricle"? | scripts/resolve_terms.py |
OLS |
OLS missed lab shorthand (PBMC, WT) |
scripts/map_terms.py, then validate_terms.py |
ZOOMA proposes; OLS decides |
Is EFO:0001067 real, current, correctly labelled? |
scripts/validate_terms.py |
OLS |
Is HPO a real prefix? Does HP:notanid match the pattern? |
scripts/lookup_prefix.py |
Bioregistry |
| Which landing page should this CURIE open? | scripts/lookup_prefix.py |
Identifiers.org + Ontobee URLs |
All four scripts take single values or files, emit TSV or JSON, and need no packages beyond the
standard library. Full traps for the non-OLS services are in references/companion-apis.md.
Resolve text to terms
cd skills/ontology-term-resolution/scripts
# one string, constrained to the ontology that should define it
python3 resolve_terms.py "liver" --ontology uberon
query rank curie label ontology match_type strategy defining_ontology
liver 1 UBERON:0002107 liver uberon exact_label exact true
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/companion-apis.md 7.4 KB
- references/curation-rules.md 5.4 KB
- references/ols4-api.md 6.0 KB
- references/ontology-registry.md 5.5 KB
- scripts/id_client.py 12 KB runs code
- scripts/lookup_prefix.py 6.3 KB runs code
- scripts/map_terms.py 7.2 KB runs code
- scripts/ols_client.py 12 KB runs code
- scripts/resolve_terms.py 8.0 KB runs code
- scripts/validate_terms.py 9.5 KB runs code
- scripts/zooma_client.py 4.5 KB runs code
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.
- 1mo ago First seen · 209 lines · 253 tokens per session scan A 8fbd0c71c442
ontology-term-resolution is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 253 tokens to every session and 3,015 once invoked, about $0.0010 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to ontology-term-resolution, differing in 7 lines, and is treated as a copy.
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