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 ncats-araxgit 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/ncats-arax)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/ncats-arax"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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/synthetic-sciences/openscience/ncats-arax"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/ncats-arax.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 | $0.00099 | $0.01887 |
| Opus 5.5 | $0.00040 | $0.00755 |
| Sonnet 5.5 | $0.00020 | $0.00377 |
| Haiku 4.5 | $0.00010 | $0.00189 |
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 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
95% identical to ncats-arax — 37 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 — 186 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.
- 1mo ago Changed · +5 lines 78f3b4e1eb13
- 1mo ago First seen · 181 lines · 99 tokens per session scan A 11eabc8ee53b
ncats-arax is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 99 tokens to every session and 1,887 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to ncats-arax, differing in 37 lines, and is treated as a copy.
Other skills, from other repositories
songsee
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
arxiv
Search arXiv papers by keyword, author, category, or ID.
research-paper-writing
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.