ncats-arax skill
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.
Is the ncats-arax skill safe?
Clean: nothing in its files matched our rules. We read 4 files in the folder on 2026-09-28.
No findings.
Install the ncats-arax skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
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
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it 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
caller metadata even when store=false is requested.
- Use only public, nonsensitive research questions. ARAX status facilities may expose query and
programs, or proprietary target hypotheses.
- Do not submit patient information, confidential research questions, unpublished compound
exists.
- 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
- Describe position as unscored response order, never rank.
- Verify important candidates with literature and authoritative databases separately.
Workflow
failure or empty result.
- 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
TRAPI payload.
- Inspect summary.json for bounded bindings and provenance and response.json for the exact
- Verify scientifically important paths outside ARAX.
Preflight
Check the production OpenAPI without making a biomedical query:
python skills/ncats-arax/scripts/arax_client.py preflightThe 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-myelofibrosisReview 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-abl1Lookup 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-fibrosisRight-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-abl1Federation 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 textThe 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
Returned predicates or qualifier aspects may be more specific than the query constraint.
- 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.
and source-record URL fields.
- Inspect all source objects, including primary, aggregator, supporting-data, upstream-resource,
publications exist.
- Treat publicationavailability: notreturned as missing metadata, not evidence that no
partial, unfamiliar, or scientifically surprising.
- 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
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
- ARAX production OpenAPI
- ARAXi operation documentation
- Translator Reasoner API
- Biolink Model
More skills from K-Dense-AI/scientific-agent-skills
- AadaptyvHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
- AaeonThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
- AalphagenomeLook up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.
- Aanalytical-method-validationPlan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
- AanndataData structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
- AarborAutonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
- AarboretoInfer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
- AastropyCore Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
- AautoskillObserve the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
- Abenchling-integrationBenchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
- Abgpt-paper-searchSearch scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
- AbidsUse this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.