/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
$ npx -y skills add K-Dense-AI/scientific-agent-skills --skill ncats-arax --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/ncats-arax
Context preview
The summary Claude sees to decide when to auto-load this 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
SKILL.md
ncats-arax.SKILL.mdname: 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](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:
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
Read more
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](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:
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
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 170,000+ scientists worldwide. 158 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Other skills on scientific-agent-skills.
- /adaptyv
How 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,
Open skill - /aeon
This 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
Open skill - /analytical-method-validation
Plan, 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,
Open skill - /anndata
Data 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
Open skill - /arbor
Autonomously 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
Open skill - /arboreto
Infer 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.
Open skill

