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Ncats Arax

OrganizationPopular
K-Dense-AI
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. 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.

Overview

PublisherK-Dense-AI
Repositoryscientific-agent-skills
Skill namencats-arax
Stars
48.4K
Forks
4.4K
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by K-Dense-AI on GitHub. Read the source before you install it.

Installation

Install the Ncats Arax AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/ncats-arax
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p .claude/skills
cp -r /tmp/scientific-agent-skills/skills/ncats-arax .claude/skills/ncats-arax
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ncats Arax in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ncats Arax on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ncats Arax is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
python skills/ncats-arax/scripts/arax_client.py preflight

The client verifies that the service identifies itself as ARAX, exposes POST /query and GET /entity, and reports a supported TRAPI version. It reads info.x-trapi.version, falling back to the title for older OpenAPI documents. 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:

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

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:

bash
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:

bash
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:

bash
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. The same applies to a failed provider in lookup mode. An explicit non-success ARAX response status exits 6 with the raw response retained; it must not be reported as a successful zero-result query.

Inspect saved provenance

Rebuild a bounded summary without network access:

bash
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.
  • Trace aggregator edges to their primary/upstream sources and publications before claiming corroboration. Multiple providers can redistribute the same record; report distinct primary evidence, not provider count as confidence or independent replication.
  • 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

Reviewed on 2026-09-30 against production ARAX 1.5.4 / TRAPI 1.5.0 (the URL still contains v1.4). Live public smoke tests passed for preflight, normalization, qualified one-hop and endpoint-pinned two-hop lookups, and RTX-KG2/MolePro federation. Results and provider availability can change. The official introductory guide contains older response examples; use the deployed schema and current ARAX source for field and operation contracts. No Python SDK is used by this client.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ncats Arax AI skill do?

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.

Why use Ncats Arax on TypingMind?

Because you install it once and use it with any model. Ncats Arax is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ncats Arax in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/ncats-arax. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ncats Arax?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ncats Arax?

As many as you like. As long as a model supports skills, you can use Ncats Arax with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ncats Arax AI skill free?

Yes. It is published on GitHub by K-Dense-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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