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ncats-arax

Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships.

Install / Use

npx skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

98/100

Supported Platforms

Universal

Our assessment of ncats-arax

ncats-arax scores 98/100 on our quality scale, 2nd of 47 Healthcare & Life Sciences skills we index (top 5%).

Its SKILL.md is 7.7 KB long, well organised into 12 sections with 6 code examples: a thorough specification that gives an agent plenty to work with.

With 46,441 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated about 2 months ago, so ncats-arax is actively maintained.
  • Our last check on 2026-09-29 found the source still online.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

ncats-arax compared with similar skills

All 4 of these similar skills score higher than ncats-arax; compare them before choosing.

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ncats-arax (this skill)by K-Dense-AI9846.4k48d agoSKILL.md
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Frequently asked questions

How do I install ncats-arax?
Run npx skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax. The install tabs above show the steps for each supported agent.
Which AI agents does ncats-arax work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is ncats-arax safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is ncats-arax still maintained?
The repository was last updated about 2 months ago, so ncats-arax is actively maintained.

name: ncats-arax description: Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries. allowed-tools: Read Bash license: MIT compatibility: Requires Python 3.10+ and outbound HTTPS access to arax.transltr.io. The client uses only the Python standard library and needs no API key. Queries and caller metadata may be publicly visible; never submit sensitive or patient-specific content. metadata: version: "1.0" skill-author: neuroepithelial

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=false is 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

  1. Normalize free text separately, then review and report the proposed CURIE and category.
  2. Choose a typed one-hop query or an exactly two-hop query with both endpoints pinned.
  3. Use default RTX-KG2 lookup unless the user explicitly names two to five providers.
  4. Acknowledge that the biomedical query is public and choose a new or empty output directory.
  5. Run the client once. Do not silently change provider selection or expansion order after a failure or empty result.
  6. Inspect summary.json for bounded bindings and provenance and response.json for the exact TRAPI payload.
  7. 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.

Normalize an entity

Normalization is review-only and never triggers a graph query:

python skills/ncats-arax/scripts/arax_client.py normalize "primary myelofibrosis" \
  --expected-category biolink:Disease \
  --max-synonyms 10 \
  --acknowledge-public-query \
  --output-dir outputs/normalize-myelofibrosis

Review the canonical identifier, name, category, and synonym preview before using a CURIE. Report all CURIEs and categories regardless of query outcome. A category warning or zero result is a reason to curate the identifier, not to chain automatically to /query.

One-hop lookup

Pin at least one endpoint and type both nodes:

python skills/ncats-arax/scripts/arax_client.py one-hop \
  --subject-id CHEBI:31690 \
  --subject-category biolink:SmallMolecule \
  --predicate biolink:affects \
  --object-id NCBIGene:25 \
  --object-category biolink:Gene \
  --qualifier biolink:object_aspect_qualifier=activity_or_abundance \
  --qualifier biolink:object_direction_qualifier=decreased \
  --acknowledge-public-query \
  --output-dir outputs/imatinib-abl1

Lookup mode is the default and fixes expansion to infores:rtx-kg2. It defaults to 20 results. Use --result-limit N to request 1-50 results; 50 is the hard cap in either mode.

Endpoint-pinned two-hop lookup

Use exactly one typed, unpinned intermediate node:

python skills/ncats-arax/scripts/arax_client.py two-hop \
  --subject-id CHEBI:66901 \
  --subject-category biolink:SmallMolecule \
  --predicate-1 biolink:affects \
  --intermediate-category biolink:Gene \
  --predicate-2 biolink:associated_with \
  --object-id MONDO:0009061 \
  --object-category biolink:Disease \
  --qualifier-1 biolink:object_aspect_qualifier=activity_or_abundance \
  --qualifier-1 biolink:object_direction_qualifier=increased \
  --expand-order right-first \
  --acknowledge-public-query \
  --output-dir outputs/ivacaftor-cystic-fibrosis

Right-first expansion is the default. If an empty result merits another attempt, run a new query explicitly with --expand-order left-first and keep the runs separate.

Selected-provider federation

Federation is explicit and accepts two to five named providers:

python skills/ncats-arax/scripts/arax_client.py one-hop \
  --subject-id CHEBI:31690 \
  --subject-category biolink:SmallMolecule \
  --predicate biolink:affects \
  --object-id NCBIGene:25 \
  --object-category biolink:Gene \
  --mode federated \
  --kp infores:rtx-kg2 \
  --kp infores:molepro \
  --acknowledge-public-query \
  --output-dir outputs/federated-imatinib-abl1

Federation defaults to the hard maximum of 50 results. Provider errors may coexist with useful results; such a run exits 7 after retaining its artifacts and is marked partial.

Inspect saved provenance

Rebuild a bounded summary without network access:

python skills/ncats-arax/scripts/arax_client.py summarize \
  --request outputs/ivacaftor-cystic-fibrosis/request.json \
  --response outputs/ivacaftor-cystic-fibrosis/response.json \
  --format text

The inspector accepts only the same constrained request shapes and fixed operations that the live commands generate. Use --format json for the normalized view on standard output.

Interpret results

  • Follow each analysis's query-edge bindings; do not summarize every knowledge-graph edge.
  • Preserve the physical edge subject, predicate, object, and qualifier values returned by ARAX. Returned predicates or qualifier aspects may be more specific than the query constraint.
  • Inspect all source objects, including primary, aggregator, supporting-data, upstream-resource, and source-record URL fields.
  • Treat publication_availability: not_returned as missing metadata, not evidence that no publications exist.
  • Treat missing auxiliary-graph references and provider failures as explicit warnings.
  • Consult the raw response whenever the bounded summary omits detail or the service response is partial, unfamiliar, or scientifically surprising.

Deliberate exclusions

The client has no raw-query, workflow, operation, overlay, ranking, inference, link-prediction, Pathfinder, ARS, batch, all-provider, three-hop, cache, daemon, SDK, MCP, or natural-language-to-TRAPI surface. Do not work around those limits with direct HTTP calls under this skill.

Official references

Related Skills

View on GitHub
GitHub Stars46.4k
CategoryHealthcare
Updated1mo ago
Forks4.2k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions