Communitygithub.com

NCATS-ARAX

查询NCATS Translator ARAX生产API,获取有界、类型化、富含溯源的单跳和端点固定的两跳生物医学知识图谱关系。适用于基于Biolink约束的RTX-KG2查找、显式选择提供者的ARAX联合查询、实体标准化、限定符感知的图遍历以及TRAPI边绑定、出版物和知识源溯源的检查。

NCATS-ARAX 是什么?

该技能用于通过ARAX客户端工具以受控方式查询NCATS Translator ARAX生产API。它支持单跳和端点固定的两跳生物医学知识图谱查询,要求用户提供明确的CURIE标识符和Biolink类型,并支持限定符过滤。默认使用RTX-KG2知识图谱,也可显式指定2-5个联合知识提供者。查询结果以TRAPI格式返回,包含详细的边绑定、出版物和知识源溯源信息,并保存为请求和响应的JSON文件供后续离线分析。该技能强调安全边界,禁止用于临床决策、患者信息、推断或开放路径查找,所有查询均视为公开研究问题。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

NCATS ARAX

将ARAX用作受约束的知识图谱查找服务。提交经过审核的CURIE和明确的Biolink类型,保留精确的TRAPI交换,检查查询边绑定和溯源,并将每个返回的路径视为后续验证的候选者。

在构建查询之前,请阅读query-contract.md。在解释保存的工件、警告、溯源或部分结果时,请阅读output-schema.md。

安全边界

  • 仅使用公开、非敏感的研究问题。即使请求了 store=false,ARAX状态设施也可能暴露查询和调用者元数据。
  • 不要提交患者信息、机密研究问题、未发表的化合物程序或专有靶点假设。
  • 不要将返回的路径视为已验证的机制或临床建议。
  • 将零结果报告为“在此约束下未返回”,绝不能作为不存在任何关系的证据。
  • 将位置描述为无评分响应顺序,绝非排名。
  • 重要候选者需通过文献和权威数据库单独验证。

工作流程

  1. 单独标准化自由文本,然后审查并报告建议的CURIE和类别。
  2. 选择类型化的单跳查询或两个端点都固定的精确两跳查询。
  3. 除非用户明确指定两到五个提供者,否则使用默认的RTX-KG2查找。
  4. 确认生物医学查询是公开的,并选择一个新的或空的输出目录。
  5. 运行客户端一次。失败或结果为空后,不要静默更改提供者选择或扩展顺序。
  6. 检查 summary.json 以了解有界绑定和溯源,检查 response.json 以了解精确的TRAPI载荷。
  7. 在ARAX外部验证科学上重要的路径。

预检

在不进行生物医学查询的情况下检查生产OpenAPI:

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

客户端验证该服务标识为ARAX,公开 /query,并报告支持的TRAPI版本。非生产端点或未经测试的TRAPI系列需要显式覆盖;两种覆盖都不会改变固定的查询形状或操作。

标准化实体

标准化仅用于审查,从不触发图查询:

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之前,审查规范标识符、名称、类别和同义词预览。无论查询结果如何,都要报告所有CURIE和类别。类别警告或零结果是整理标识符的理由,而不是自动链接到 /query。

单跳查找

至少固定一个端点并键入两个节点:

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

查找模式是默认模式,并将扩展固定为 infores:rtx-kg2。默认返回20个结果。使用 --result-limit N 请求1-50个结果;两种模式下的硬上限均为50。

端点固定的两跳查找

使用恰好一个类型化、未固定的中间节点:

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

默认使用右优先扩展。如果空结果值得再次尝试,请使用 --expand-order left-first 显式运行新查询,并保持各次运行分离。

选定提供者联合查询

联合查询是显式的,接受两到五个命名提供者:

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

联合查询默认使用硬上限50个结果。提供者错误可能与有用结果共存;此类运行退出码为7,同时保留其工件,并标记为部分结果。

检查保存的溯源

无需网络访问即可重建有界摘要:

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

检查器仅接受与实时命令生成的相同受约束的请求形状和固定操作。使用 --format json 在标准输出上获取标准化视图。

解释结果

  • 遵循每个分析的查询边绑定;不要总结每个知识图谱边。
  • 保留ARAX返回的物理边主语、谓语、宾语和限定符值。返回的谓语或限定符方面可能比查询约束更具体。
  • 检查所有源对象,包括primary、aggregator、supporting-data、upstream-resource和source-record URL字段。
  • 将 publication_availability: not_returned 视为缺失元数据,而非不存在出版物的证据。
  • 将缺失的辅助图引用和提供者失败视为显式警告。
  • 当有界摘要遗漏细节或服务响应部分、不熟悉或科学上令人惊讶时,查阅原始响应。

有意排除的功能

客户端没有原始查询、工作流、操作、叠加、排名、推断、链接预测、Pathfinder、ARS、批处理、全提供者、三跳、缓存、守护进程、SDK、MCP或自然语言到TRAPI的接口。不要在此技能下通过直接HTTP调用绕过这些限制。

官方参考

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

>

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.

相关技能