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 ncats-araxgit 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/ncats-arax)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/ncats-arax"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/ncats-arax"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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 | $0.01818 |
| Opus 5.5 | $0.00040 | $0.00727 |
| Sonnet 5.5 | $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 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
100% identical to ncats-arax — 30 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 — 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.
- 1mo ago First seen · 179 lines · 99 tokens per session scan A 87921ff0abe0
ncats-arax is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 99 tokens to every session and 1,818 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ncats-arax, differing in 30 lines, and is treated as a copy.
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