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Agent skill

Ncats Arax

by K-Dense-AI in K-Dense-AI/scientific-agent-skills

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

MITAuto-check: notesKnowledge Management

Install Ncats Arax

skills CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install K-Dense-AI/scientific-agent-skills ncats-arax --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ncats-arax .claude/skills/ncats-arax && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ncats-arax
GitHub stars
48k
Used in
1 other repo
Token cost
~2.3k tokens
SKILL.md length
794 words
Files
4 (incl. scripts, references)
Skills in repo
153
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 7 steps: Normalize free text separately, then… → Choose a typed one-hop query or an… → Use default RTX-KG2 lookup unless the… → …
  • Biolink-constrained RTX-KG2 lookup
  • SKILL.md covers Safety boundary, Workflow, Preflight and Normalize an entity, plus 7 more sections
  • Runs Python scripts from its folder; calls python

What it does

Ncats Arax is an agent skill from K-Dense-AI/scientific-agent-skills. 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.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/output-schema.md`, `references/query-contract.md` and `scripts/arax_client.py`). Compatibility notes: 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…

It sits in Knowledge Management, covering Knowledge graphs, Database schema design and Translation. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.

When your agent uses it

  • Biolink-constrained RTX-KG2 lookup
  • Explicit selected-provider ARAX federation
  • Separate entity normalization
  • Qualifier-aware graph traversal

Example prompts

  • “Use the ncats-arax skill to query the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop…”
  • “/ncats-arax”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): 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.
  • Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  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
  6. Inspect summary.json for bounded bindings and provenance and response.json for the exact
  7. Verify scientifically important paths outside ARAX.

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com
    • ncatstranslator.github.io
    • arax.transltr.io
    • biolink.github.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • 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.

    From compatibility in the SKILL.md frontmatter.

Context cost

Ncats Arax loads about 2.3k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 128 tokens; SKILL.md has 794 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~128
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 794 words, ~2,284 tokens.

Download SKILL.mdSave it as .claude/skills/ncats-arax/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
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
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.
license
MIT
metadata.version
1.2
metadata.last-reviewed
2026-09-30
metadata.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:

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.

Show full SKILL.md (287 more words)Show less

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.

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/ncats-arax of K-Dense-AI/scientific-agent-skills.

  • SKILL.md
  • references/output-schema.md
  • references/query-contract.md
  • scripts/arax_client.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Categories

Questions about Ncats Arax

What does Ncats Arax do?

Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Ncats Arax is an agent skill from K-Dense-AI/scientific-agent-skills. Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships.

When should I use Ncats Arax?

Ncats Arax fits situations like: biolink-constrained RTX-KG2 lookup; explicit selected-provider ARAX federation; separate entity normalization; qualifier-aware graph traversal.

How do I install Ncats Arax in Claude Code?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax -a claude-code`. Or copy the skill folder (skills/ncats-arax in K-Dense-AI/scientific-agent-skills) into .claude/skills/ncats-arax in your project. Claude Code loads it when a task matches its description.

How do I install Ncats Arax in Codex?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax -a codex`. Or copy the skill folder (skills/ncats-arax in K-Dense-AI/scientific-agent-skills) into .agents/skills/ncats-arax in your project. Codex loads it when a task matches its description.

Can I use Ncats Arax in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ncats-arax, .gemini/skills/ncats-arax, .github/skills/ncats-arax and .opencode/skills/ncats-arax in your project.

What does Ncats Arax need to run?

Going by SKILL.md and its folder, Ncats Arax needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Bash. Compatibility (from SKILL.md): 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..

Does Ncats Arax access the network?

SKILL.md names 4 domains. As links in the text: github.com, ncatstranslator.github.io, arax.transltr.io and biolink.github.io. This is read from the text; nothing was executed.

Is Ncats Arax safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Ncats Arax use?

Ncats Arax is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ncats Arax use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.1k tokens, read only when the agent opens those files.

What are the alternatives to Ncats Arax?

Skills that share tags, products or a category with Ncats Arax: Memory Literary Analysis (FirefoxCSS-Store/FirefoxCSS-Store.github.io, 1k stars), Geo Knowledge (yaojingang/GEOHub, 165 stars), Memory Literary Analysis (basicmachines-co/basic-memory, 4.1k stars) and Behive Research (qa10devteam/behive, 146 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ncats Arax?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,215 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.

Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.