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 ncats-araxgit 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/ncats-arax)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/ncats-arax"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/ncats-arax/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/ncats-arax"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/ncats-arax.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00099 | $0.01818 |
| Opus 5 | $0.00049 | $0.00909 |
| Sonnet 5 | $0.00020 | $0.00364 |
| Haiku 4.5 | $0.00010 | $0.00182 |
Grade A, and why
ncats-arax 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ncats-arax — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NCATS ARAX
Use ARAX as a constrained knowledge-graph lookup service. Submit reviewed CURIEs and explicit Biolink types, preserve the exact TRAPI exchange, inspect query-edge bindings and provenance, and treat every returned path as a candidate for subsequent verification.
Read query-contract.md before constructing a query. Read output-schema.md when interpreting saved artifacts, warnings, provenance, or partial results.
Safety boundary
- Use only public, nonsensitive research questions. ARAX status facilities may expose query and
caller metadata even when
store=falseis requested. - Do not submit patient information, confidential research questions, unpublished compound programs, or proprietary target hypotheses.
- Do not present a returned path as a validated mechanism or clinical recommendation.
- Report a zero as "not returned under these constraints," never as evidence that no relationship exists.
- Describe position as unscored response order, never rank.
- Verify important candidates with literature and authoritative databases separately.
Workflow
- Normalize free text separately, then review and report the proposed CURIE and category.
- Choose a typed one-hop query or an exactly two-hop query with both endpoints pinned.
- Use default RTX-KG2 lookup unless the user explicitly names two to five providers.
- Acknowledge that the biomedical query is public and choose a new or empty output directory.
- Run the client once. Do not silently change provider selection or expansion order after a failure or empty result.
- Inspect
summary.jsonfor bounded bindings and provenance andresponse.jsonfor the exact TRAPI payload. - Verify scientifically important paths outside ARAX.
Preflight
Check the production OpenAPI without making a biomedical query:
python skills/ncats-arax/scripts/arax_client.py preflight
The client verifies that the service identifies itself as ARAX, exposes /query, and reports a
supported TRAPI version. A nonproduction endpoint or untested TRAPI series requires an explicit
override; neither override changes the fixed query shapes or operations.
What ships with it
3 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 · 179 lines · 99 tokens per session scan A 87921ff0abe0
ncats-arax is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 1,818 once invoked, about $0.0005 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
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
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.