Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting 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 K-Dense-AI/scientific-agent-skills --skill ontology-term-resolutiongit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/ontology-term-resolution)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/ontology-term-resolution"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/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/k-dense-ai/scientific-agent-skills/ontology-term-resolution"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/ontology-term-resolution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 22 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00201 | $0.02133 |
| Opus 5 | $0.00101 | $0.01066 |
| Sonnet 5 | $0.00040 | $0.00427 |
| Haiku 4.5 | $0.00020 | $0.00213 |
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 9d 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 — 165 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.
Two directions
| Direction | Script | Question answered |
|---|---|---|
| text → ID | scripts/resolve_terms.py |
What is the term for "left ventricle"? |
| ID → verdict | scripts/validate_terms.py |
Is EFO:0001067 real, current, and labelled what this file claims? |
Both take single values or files, emit TSV or JSON, and need no packages beyond the standard library.
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
# a column of tissue names; anything not an exact hit is reported, not guessed
python3 resolve_terms.py --input tissues.txt --ontology uberon \
--exact-only --format tsv -o resolved.tsv
# accept fuzzy fallbacks, then review the partial hits by hand
python3 resolve_terms.py "left ventrical of heart" --ontology uberon --top 3
The search escalates exact (label and synonym) → token → fulltext and stops at the first
strategy that returns anything, reporting which one fired. --exact-only disables the ladder.
--branch UBERON:0000465 restricts candidates to descendants of a term.
What ships with it
6 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.
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.
- 9d ago First seen · 165 lines · 201 tokens per session scan A 25e59f0c8fe4
ontology-term-resolution is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed yesterday), licensed MIT. It adds 201 tokens to every session and 2,133 once invoked, about $0.0010 per session on Opus 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 2026-09-03.
Other skills, from other repositories
discovery-toolbox
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discovery-director
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bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.