synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill ontology-term-resolutiongit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/ontology-term-resolution)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/ontology-term-resolution"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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/synthetic-sciences/openscience/ontology-term-resolution"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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 | $0.03084 |
| Opus 5.5 | $0.00101 | $0.01234 |
| Sonnet 5.5 | $0.00051 | $0.00617 |
| Haiku 4.5 | $0.00025 | $0.00308 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ontology-term-resolution — 97% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 216 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 Changed · +5 lines 4cdf84a9f8d2
- 1mo ago First seen · 211 lines · 253 tokens per session scan A eabbe78bc612
ontology-term-resolution is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 253 tokens to every session and 3,084 once invoked, about $0.0010 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 2026-09-14.
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Write ML papers for NeurIPS/ICML/ICLR: design→submit.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
knowledge-extension-ecosystem-lsp-code-intelligence
A codebase guide to LSP, the standard protocol behind editor features such as symbol queries, file diagnostics, and language-aware code information.