본문으로 건너뛰기
cccskills
무료GitHub에 공개

ncats-arax

Look up biomedical relationships in ARAX using explicit entity types and tightly bounded paths. Inspect direct links or paths through one intermediate entity, along with returned publications and source records, to identify candidates for further research.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

설치 방법 보기

이럴 때 유용합니다

  • Checking recorded drug–gene relationships
  • Exploring paths between a drug and a disease
  • Inspecting publications and original data sources
  • Comparing results from explicitly selected providers

포함된 파일(4)

  • SKILL.md8.9 KB
  • references/output-schema.md8.2 KB
  • references/query-contract.md7.7 KB
  • scripts/arax_client.py83.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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  • Do not submit patient information, confidential research questions, unpublished compound programs, or proprietary target hypotheses.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:

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:

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:

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. 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:

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.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Estimate metabolic fluxes from carbon-13 labeling
  • Validate atom mappings and measurement inputs
  • Simulate labeling for proposed reaction rates
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

adaptyv

무료

Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Plan binding screens for candidate protein sequences
  • Estimate experiment costs before submission
  • Track laboratory progress and retrieve results
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

aeon

무료

Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Classify time series and predict continuous outcomes
  • Group time series with similar patterns
  • Forecast future values
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

Investigate how DNA variants may affect gene activity using precomputed AlphaGenome Atlas results and model predictions. Rank candidate variants, examine tissue-specific effects, and inspect the features behind scores to develop research hypotheses about biological mechanisms.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Ranking candidate variants from a VCF
  • Examining tissue-specific effects on gene activity
  • Comparing reference and altered DNA predictions
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Planning analytical validation studies and acceptance criteria
  • Evaluating calibration, accuracy, and precision
  • Estimating and confirming detection and quantitation limits
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

anndata

무료

Organize single-cell measurements with cell and gene metadata, read and write h5ad or Zarr files, and prepare subsets or combined datasets. Includes guidance on handling large annotated matrices without loading all measurement values into memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Read and write h5ad or Zarr datasets
  • Combine experimental batches with source labels
  • Select cells or genes using metadata
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

K-Dense-AI의 스킬 모두 보기

이 스킬의 문제 신고