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NCATS ARAX

查詢 NCATS Translator ARAX 生產 API,取得受邊界限制、具有型別與來源追溯的生物醫學知識圖譜單跳與端點固定的雙跳關係。適用於 Biolink 約束的 RTX-KG2 查找、明確選擇提供者的 ARAX 聯合查詢、獨立實體正規化、修飾詞感知的圖譜遍歷,以及檢視 TRAPI 邊緣繫結、文獻與知識來源追溯。不適用於推論、排序、開放式路徑探索、臨床指導或敏感性查詢。

NCATS ARAX 是什麼?

NCATS ARAX 技能提供一個受約束的知識圖譜查詢服務,用於與 NCATS Translator 專案的生產 ARAX API 進行互動。此技能支援受邊界限制的單跳查詢(subject-predicate-object)與端點固定的雙跳查詢(subject-predicate-intermediate-predicate-object),並要求明確指定 Biolink 類別與 CURIEs 以確保查詢的精確性。使用者可以選擇預設的 RTX-KG2 知識來源,或明確指定二至五個提供者進行聯合查詢,並透過 qualifier 參數進行修飾詞感知的圖譜遍歷。此技能同時提供實體正規化工具、結果摘要檢視器,以及完整的 TRAPI payload 與 provenance 細節。其安全邊界嚴格限制僅用於公開、非敏感的研究問題,禁止提交病患資料或商業機密,並要求將零結果解讀為「未在這些限制下找到」,而非「不存在關係」。此外,此技能明確排除原始查詢、工作流程、排名、推論、三跳以上路徑探索、批次處理與自然語言轉 TRAPI 等功能。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/ncats-arax

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說明文件

NCATS ARAX 是做什麼的?

將 ARAX 用作受約束的知識圖譜查詢服務。提交經過審核的 CURIEs 與明確的 Biolink 型別,保留精確的 TRAPI 交換,檢視查詢邊緣繫結與 provenance,並將每個回傳的路徑視為後續驗證的候選項目。

在構建查詢前,請先閱讀 query-contract.md。在解讀儲存的產出、警告、provenance 或部分結果時,請閱讀 output-schema.md。

安全邊界

  • 僅使用公開、非敏感的研究問題。即使要求 store=false,ARAX 狀態設施仍可能暴露查詢與呼叫者中繼資料。
  • 請勿提交病患資訊、機密研究問題、未發表的化合物計畫或專有目標假說。
  • 請勿將回傳的路徑呈現為經過驗證的機制或臨床建議。
  • 將零結果報告為「未在這些限制下回傳」,絕不能作為不存在關係的證據。
  • 將位置描述為未經分數計算的回應順序,絕不能視為排名。
  • 重要的候選項目需另外透過文獻與權威資料庫進行驗證。

工作流程

  1. 先對自由文字進行正規化,然後審閱並報告建議的 CURIE 與類別。
  2. 選擇型別的單跳查詢,或兩端點均固定的恰好雙跳查詢。
  3. 除非使用者明確指定二至五個提供者,否則使用預設的 RTX-KG2 查詢。
  4. 確認生物醫學查詢是公開的,並選擇一個新的或空的輸出目錄。
  5. 執行一次客戶端。在失敗或得到空結果後,不得暗中更改提供者選擇或擴展順序。
  6. 檢視 summary.json 以取得受邊界限制的繫結與 provenance,並檢視 response.json 以取得精確的 TRAPI payload。
  7. 在 ARAX 之外驗證科學上重要的路徑。

預檢

在不進行生物醫學查詢的情況下檢查生產 OpenAPI:

python skills/ncats-arax/scripts/arax_client.py preflight

客戶端會驗證服務是否標示為 ARAX、是否暴露 /query 端點,以及是否報告支援的 TRAPI 版本。非生產端點或未測試的 TRAPI 系列需要明確的覆寫;任何覆寫都不會改變固定的查詢形狀或操作。

實體正規化

正規化僅供審閱,絕不觸發圖譜查詢:

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

在使用 CURIE 前,請審閱規範識別碼、名稱、類別與同義詞預覽。無論查詢結果如何,均需報告所有 CURIEs 與類別。類別警告或零結果是應整理識別碼的緣故,而非自動串接至 /query。

單跳查詢

至少固定一個端點,並為兩個節點指定型別:

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

查詢模式是預設值,並將擴展固定為 infores:rtx-kg2。預設回傳 20 筆結果。使用 --result-limit N 可要求 1-50 筆結果;50 是兩種模式中的硬上限。

端點固定的雙跳查詢

使用恰好一個有型別、未固定的中間節點:

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

先右擴展是預設值。如果空結果值得再嘗試一次,請明確使用 --expand-order left-first 執行新的查詢,並保持各次執行分開。

選定提供者的聯合查詢

聯合是明確的,且接受二至五個命名提供者:

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

聯合查詢預設使用硬上限 50 筆結果。提供者錯誤可能與有用的結果共存;此類執行會以退出碼 7 結束,保留其產出並標記為部分結果。

檢視已儲存的 Provenance

在不使用網路的情況下重建受邊界限制的摘要:

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

檢視器僅接受與即時指令所產生之相同受約束的查詢形狀與固定操作。使用 --format json 可取得標準輸出的正規化檢視。

解讀結果

  • 跟隨每個分析的查詢邊緣繫結;請勿總結每個知識圖譜邊緣。
  • 保留 ARAX 回傳的物理邊緣主詞、謂詞、受詞與修飾詞值。回傳的謂詞或修飾詞面向可能比查詢約束更具體。
  • 檢視所有來源物件,包括 primary、aggregator、supporting-data、upstream-resource 與 source-record URL 欄位。
  • 將 publication_availability: not_returned 視為遺失的中繼資料,而非不存在文獻的證據。
  • 將缺少的輔助圖譜參考與提供者失敗視為明確的警告。
  • 每當受邊界限制的摘要省略細節,或服務回應是部分、不熟悉或科學上令人驚訝時,請查閱原始回應。

故意排除的功能

客戶端不具備原始查詢、工作流程、操作、疊加、排序、推論、鏈路預測、Pathfinder、ARS、批次處理、所有提供者、三跳、快取、守護程式、SDK、MCP 或自然語言轉 TRAPI 的表面。請勿透過此技能以直接 HTTP 呼叫繞過這些限制。

官方參考資料

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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, 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`.

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 requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

alphagenome

Look 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.

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, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include

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 cellxgene-census.

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 experiments without overfitting — e.g.

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. Supports distributed computation for large-scale datasets.

astropy

Core 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.

autoskill

Observe the user

benchling-integration

Benchling 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.

bgpt-paper-search

Search 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.

bids

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biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

bulk-rnaseq

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g.

cellxgene-census

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

citation-management

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

clinical-decision-support

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

clinical-reports

Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

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