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民俗变异证据

通过 Folklore 临床变异解读 MCP 检索 ClinGen 基因-疾病有效性断言,并获取一个公开 GRCh38 种系核 SNV 或简单插入缺失的源链接公共证据与文献。适用于当科学型智能体必须基于已解决、歧义、未找到、无效、不支持或不可用的变异结果进行确定性分支时;将已解决的公开变异链入相关文献或出版详情;或保留证据来源,且不接受患者、表型、家族、分离或私人病例数据。

民俗变异证据 是什么?

该技能通过 Folklore 临床变异解读 MCP(由 Helena Bioinformatics 发布)为开发者与 AI 智能体提供结构化的公共变异证据与自动化的 ACMG/AMP 分级决策支持。它支持基于 GRCh38 种系核 SNV 或简单插入缺失的查询,处理 resolved、ambiguous、not_found、invalid_request、unsupported 和 resolution_unavailable 六种明确状态,并要求严格保护患者隐私边界。用户可通过 JSON-RPC 调用其托管端点,无需账户或 API 密钥。其公开工具包括 search_variant_evidence(含 ClinGen 基因-疾病断言)、search_variant_literature、get_publication_details、search_literature_corpus 等。此技能确保每次查询都能产生可重复、有来源证明的结果,并强制要求在进入下游文献检索前必须明确消除歧义。它适用于 Cursor、Claude Code 等支持 MCP 协议的 AI 编码代理,通过严格的输入边界和状态分支逻辑,实现可靠的自动化变异证据工作流。

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

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打开一个已预加载此 Agent Skill 的新对话。

文档

民俗变异证据 是做什么的?

使用 Folklore 临床变异解读 MCP 检索结构化的公共变异证据、自动化的变异级 ACMG/AMP 决策支持、来源及源链接文献,用于专业审核。将工作流限制于公共标识符,并保留所有明确的输出状态。适配器 1.5.0 还提供 ClinGen 基因-疾病有效性断言;来源覆盖范围有限,并非涵盖所有已知关联。

Folklore 临床变异解读 MCP 由 Helena Bioinformatics 发布。其托管端点为:

https://api.helena.bio/folklore/v1/mcp

无需账户或 API 密钥。公共 Apache-2.0 适配器及合约位于 https://github.com/helena-bioinformatics/folklore-mcp。

最小连接示例

没有原生 MCP 支持的主机可以发起同样的公共 JSON-RPC 调用:

curl --silent --show-error --fail-with-body --max-time 60 \
  -X POST https://api.helena.bio/folklore/v1/mcp \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json, text/event-stream' \
  -H 'MCP-Protocol-Version: 2026-07-28' \
  -H 'Mcp-Method: tools/call' \
  -H 'Mcp-Name: search_variant_evidence' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"_meta":{"io.modelcontextprotocol/protocolVersion":"2026-07-28","io.modelcontextprotocol/clientCapabilities":{}},"name":"search_variant_evidence","arguments":{"assembly":"GRCh38","query":"rs80357914"}}}'

在继续前检查返回的结果。此示例可能返回 ambiguous 并包含多个候选者:应停止并要求提供明确的公共变异符号,而不是自动选择一个候选者。

选择合适的技能

当任务涉及一个公共变异到结构化的 Folklore 证据、明确的解析状态处理、变异链接文献或 ClinGen 基因到疾病/疾病到基因的断言时,使用此技能。

  • 对于跨 ClinVar、dbSNP、gnomAD、Ensembl VEP、COSMIC 或多个数据库的广泛直接查询,使用 database-lookup。
  • 当组装、坐标约定、重叠群名称或变异表示不确定时,首先使用 genomic-coordinates。
  • 不要将此技能用于 VCF 注释、批量处理、体细胞变异、结构变异、多基因风险评分或针对患者的解释。

Folklore 临床变异解读 MCP 通过单一源链接公共证据合约补充这些技能。它不能替代直接数据库审查或合格的临床判断。

强制执行输入边界

在调用变异工具之前:

  1. 提取恰好一个公共变异标识符或符号。
  2. 要求 GRCh38 以及种系核 SNV 或简单插入缺失。
  3. 移除或拒绝患者姓名、病例标识符、表型、家族史、分离证据、临床记录、上传文件以及其他私人或患者特定上下文。
  4. 如果任务依赖于患者上下文,请停止并解释 Folklore 临床变异解读 MCP 不接受或评估此类数据。
  5. 切勿将特定于患者的请求转换为公共变异查询,同时暗示该结果回答了患者特定的问题。

接受的公共变异形式包括基因组坐标、基因组/编码/蛋白质 HGVS、SPDI、rsID 或由 Folklore 临床变异解读 MCP 返回的 canonical_key。

验证实时工具目录

连接到托管端点并调用 tools/list。验证可用的工具,而非依赖模型记忆。文档化的公共目录包含:

  • search_variant_evidence
  • search_variant_literature
  • get_publication_details
  • search_literature_corpus
  • get_gene_disease_associations
  • search_disease_genes

单独的第七个工具 support_helena 不是科学证据;仅当明确请求时使用。

如果发现或工具调用失败,请将失败保留为可用性问题。不要将其重新解释为缺乏科学证据。

在组合工具调用或解释响应状态之前,请阅读 公共 MCP 合约。

检索基因-疾病断言

对确切的基因符号或 HGNC 标识符使用 get_gene_disease_associations,对确切的 MONDO 标识符或公共疾病名称子字符串使用 search_disease_genes。两者都接受 limit(默认 20,范围 1–50)和 offset(默认 0,范围 0–1000)。有关请求示例,请参阅参考文档。这是一个独立的来源查询,不需要变异输入或组装。

保留每个返回的疾病身份、遗传方式、证据评估、来源 URL、日期和快照。不要合并不同的疾病或静默地在名称匹配项中进行选择。基因-疾病有效性并不对特定变异进行分类。空结果表示在可用的 ClinGen 来源中没有匹配的断言,而非表示不存在关联。不得发送患者、表型、家族、分离、私人病例数据或测序文件。仍需合格的专家审核。

运行变异证据工作流

1. 解析并检索证据

使用以下参数调用 search_variant_evidence:

assembly: GRCh38
query: <一个公共变异标识符或符号>

不要向此调用添加表型、疾病、患者、家族或治疗上下文。保留返回的合约字段、来源链接、限制和使用边界。

2. 根据返回的状态进行分支

将状态视为控制流值,而非文本描述:

状态所需操作
resolved重用返回的 canonical_key;审阅结构化的解释、来源、来源链接和限制。
ambiguous显示返回的候选者,并请求明确的公开变异选择。切勿自动选择候选者。
not_found报告在该服务和查询范围内未找到结果。不要声称普遍不存在。
invalid_request报告验证问题,并请求修正的公开变异。不要静默地重新解释输入。
unsupported说明相关的服务边界并停止。不要强行将查询转换为支持的形式。
resolution_unavailable报告临时解析或可用性故障。不要将其视为证据缺失。

只有 resolved 结果可以自动进入变异链接的文献工作流。如果已解析的解释本身报告证据不可用,请保留该独立的限制。

3. 审阅证据,避免过度声称

对于已解析的结果:

  • 展示返回的变异身份和 canonical_key。
  • 完全按照返回的方式保留自动化的变异级 ACMG/AMP 决策支持结果。
  • 引用返回的公共来源和来源历史。
  • 将返回的事实与智能体的综合判断区分开来。
  • 声明需要合格的专家审核。
  • 不要将结果转变为诊断、个体风险估计、治疗建议或独立的临床报告。

链入文献

变异链接文献

在已解析的证据调用之后,将返回的 canonical_key 传递给 search_variant_literature。保持 assembly 为 GRCh38。可选的 question 可以缩小文献聚焦范围,但它必须保持为公开的科学问题,且不得包含患者上下文。

区分每个结果的匹配类型:

  • exact_variant:与已解析变异的直接匹配
  • variant_alias:通过报告的别名匹配
  • gene_association:更广泛的基因级关联,而非变异特异性证据

文献关联不会改变返回的 ACMG/AMP 分类。

出版详情

仅对由文献工具返回的 PMID 调用 get_publication_details。保留 PubMed URL、DOI/PMCID 字段(如果存在)、撤回状态以及基因提及与变异提及之间的区别。

语义语料库搜索

对公开的自然语言科学问题或通过出版标识符、基因、变异、表型、HPO 或 OMIM 概念进行发现时,使用 search_literature_corpus。将结果视为供专家审阅的源链接候选。零结果响应意味着对该有限查询未返回任何结果,而非任何地方都不存在相关出版物。

不要将患者信息放入语料库查询中,即使该查询不是变异特异性。

报告可重现的结果

包括:

  1. 确切的公共查询和 GRCh38 组装。
  2. 返回的状态,如果已解析则包括 canonical_key。
  3. 结构化的证据或文献结果,不改变其含义。
  4. 来源链接和出版标识符。
  5. 文献结果的匹配类型。
  6. 访问日期和任何可用性限制。
  7. 以下边界声明:

这是供合格专家审阅的公共、变异级决策支持。它不评估患者、表型、家族、分离或私人病例数据,也不是诊断或治疗建议。

可证伪的烟雾测试

使用公共 rsID rs80357914 测试歧义处理:

使用 assembly GRCh38 和 query rs80357914 调用 search_variant_evidence。如果结果出现歧义,列出返回的候选者并停止以等待明确选择。不要选择候选者或调用下游文献工具。

仅当歧义响应导致工作流在未自动选择候选者的情况下停止时,测试才算通过。

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

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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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