# Ncats Arax > Consulta la API de producción NCATS Translator ARAX para relaciones acotadas y con procedencia en el grafo de conocimiento biomédico: lookup de uno y dos saltos, federación explícita de proveedores y normalización de entidades. Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/ncats-arax Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/ncats-arax.md Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills Autor: K-Dense-AI Licencia: MIT Actualizado: hace 21 días Coste de contexto: 125 tok instalada, 2k tok al activarse, 26.2k tok con todos los archivos del bundle Bundle: 4 archivos, 102 KB Permisos que pide: read bash ## Instalación Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina. ```bash # Claude Code npx -y skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax --agent claude-code # Cursor npx -y skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax --agent cursor # Codex npx -y skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax --agent codex # Gemini CLI npx -y skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax --agent gemini # Windsurf npx -y skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax --agent windsurf # Cline npx -y skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax --agent cline ``` ## Qué hace - Consulta la API de producción ARAX para relaciones de un salto y de dos saltos con extremos fijados en el grafo biomédico - Normaliza texto libre a CURIEs y categorías Biolink antes de consultar, como paso de revisión separado - Usa por defecto el lookup RTX-KG2 y permite federación explícita con entre 2 y 5 proveedores nombrados - Reconstruye un resumen acotado a partir de artefactos guardados sin volver a llamar a la red - Preserva los bindings de aristas TRAPI, publicaciones y procedencia de cada fuente de conocimiento ## Cuándo usarla - Se necesita un lookup acotado por Biolink en RTX-KG2 de una relación biomédica de un salto - Se necesita un lookup de dos saltos con ambos extremos fijados y un nodo intermedio tipado - Se quiere federación explícita entre 2 y 5 proveedores ARAX seleccionados - Se necesita inspeccionar bindings de aristas TRAPI, publicaciones y procedencia de una consulta guardada ## Cuándo no - Inferencia, ranking o búsqueda de caminos abierta (open-ended pathfinding) - Orientación clínica, o preguntas sensibles o con información de pacientes - Operaciones fuera de las formas fijas del cliente (raw-query, workflow, overlay, batch, etc.) ## Qué la activa - "Consulta en ARAX qué genes afecta este compuesto según RTX-KG2" - "Normaliza esta enfermedad a un CURIE Biolink antes de consultar ARAX" - "Haz una consulta federada en ARAX con molepro y rtx-kg2" ## Antes de instalar - Necesita Python 3.10+ y acceso HTTPS saliente a arax.transltr.io; no requiere API key, pero las consultas pueden ser públicamente visibles. - Necesita en el PATH: python - makes network requests ## Archivos - SKILL.md — 8 KB - references/output-schema.md — 7 KB - references/query-contract.md — 5 KB - scripts/arax_client.py — 82 KB ## SKILL.md Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT. Esta sección es el documento original y está en inglés. # 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](references/query-contract.md) before constructing a query. Read [output-schema.md](references/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 `/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: ```bash 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: ```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. ## 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. - 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 - [ARAX documentation](https://ncatstranslator.github.io/TranslatorTechnicalDocumentation/architecture/ara/arax/) - [ARAX production OpenAPI](https://arax.transltr.io/api/arax/v1.4/openapi.json) - [ARAXi operation documentation](https://github.com/RTXteam/RTX/blob/master/code/ARAX/Documentation/DSL_Documentation.md) - [Translator Reasoner API](https://github.com/NCATSTranslator/ReasonerAPI) - [Biolink Model](https://biolink.github.io/biolink-model/) ## Dónde encaja - Categoría: [Investigación](https://skillsagentes.com/categorias/investigacion.md) — Investigación estructurada, búsqueda de fuentes y síntesis. - Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio - [Todas las skills](https://skillsagentes.com/skills.md) - [Ranking de instalaciones](https://skillsagentes.com/ranking.md) ## Otras skills del mismo repositorio - [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas. - [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual. - [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver). - [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo. - [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras. --- Skills Agentes · [Índice de páginas en markdown](https://skillsagentes.com/sitemap.md) · [Inicio](https://skillsagentes.com/index.md)