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folklore-variant-evidence

Retrieve ClinGen gene-disease validity assertions for a public gene or disease, and review source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple indel through Folklore Clinical Variant Interpretation MCP. Use when a scientific agent must branch deterministically on resolved, ambiguous, not-found, invalid, unsupported, or unavailable variant outcomes; chain a resolved public variant into related literature or publication details; or preserve evidence provenance without accepting patient, phenotype, family, segregation, or private case data.

folklore-variant-evidence 是什么?

folklore-variant-evidence is a Claude Code agent skill that retrieve ClinGen gene-disease validity assertions for a public gene or disease, and review source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple indel through Folklore Clinical Variant Interpretation MCP. Use when a scientific agent must branch deterministically on resolved, ambiguous, not-found, invalid, unsupported, or unavailable variant outcomes; chain a resolved public variant into related literature or publication details; or preserve evidence provenance without accepting patient, phenotype, family, segregation, or private case data.

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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Folklore Variant Evidence

Use Folklore Clinical Variant Interpretation MCP to retrieve structured public variant evidence, automated variant-level ACMG/AMP decision support, provenance, and source-linked literature for professional review. Keep the workflow limited to public identifiers and preserve every explicit outcome state. Adapter 1.5.0 also provides ClinGen Gene-Disease Validity assertions; source coverage is bounded, not every known association.

Folklore Clinical Variant Interpretation MCP is published by Helena Bioinformatics. Its hosted endpoint is:

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

No account or API key is required. The public Apache-2.0 adapter and contract are available at https://github.com/helena-bioinformatics/folklore-mcp.

Minimal connection example

A host without native MCP support can make the same public JSON-RPC call:

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"}}}'

Inspect the returned outcome before continuing. This example can return ambiguous with multiple candidates: stop and request an unambiguous public variant notation instead of selecting a candidate automatically.

Select the right skill

Use this skill when the task is one public variant to structured Folklore evidence, explicit resolution-state handling, variant-linked literature, or ClinGen gene-to-disease/disease-to-gene assertions.

  • Use database-lookup for broad direct queries across ClinVar, dbSNP, gnomAD, Ensembl VEP, COSMIC, or multiple databases.
  • Use genomic-coordinates first when the assembly, coordinate convention, contig name, or variant representation is uncertain.
  • Do not use this skill for VCF annotation, batch processing, somatic variants, structural variants, polygenic scores, or patient-specific interpretation.

Folklore Clinical Variant Interpretation MCP complements those skills with one source-linked public evidence contract. It does not replace direct database review or qualified clinical judgment.

Enforce the input boundary

Before a variant tool call:

  1. Extract exactly one public variant identifier or notation.
  2. Require GRCh38 and a germline nuclear SNV or simple indel.
  3. Remove or refuse patient names, case identifiers, phenotypes, family history, segregation evidence, clinical records, uploaded files, and other private or patient-specific context.
  4. If the task depends on patient context, stop and explain that Folklore Clinical Variant Interpretation MCP does not accept or evaluate it.
  5. Never transform a patient-specific request into a public variant query while implying that the result answers the patient-specific question.

Accepted public variant forms include genomic coordinates, genomic/coding/ protein HGVS, SPDI, rsID, or a canonical_key returned by Folklore Clinical Variant Interpretation MCP.

Verify the live tool catalog

Connect to the hosted endpoint and call tools/list. Verify the available tools instead of relying on model memory. The documented public catalog contains:

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

The separate seventh tool support_helena is not scientific evidence; use it only when explicitly requested.

If discovery or a tool call fails, preserve the failure as an availability problem. Do not reinterpret it as lack of scientific evidence.

Read the public MCP contract before composing tool calls or interpreting response states.

Retrieve gene-disease assertions

Use get_gene_disease_associations for one exact gene symbol or HGNC identifier, or search_disease_genes for an exact MONDO identifier or public disease-name substring. Both accept limit (default 20, 1–50) and offset (default 0, 0–1000). See the reference for request examples. This is a separate source lookup and requires no variant input or assembly.

Preserve each returned disease identity, inheritance, evidence assessment, source URL, date and snapshot. Do not combine distinct diseases or silently choose among name matches. Gene-disease validity does not classify a particular variant. Empty results mean no matching assertion in the available ClinGen source, not no association. No patient, phenotype, family, segregation, private case data or sequencing files may be sent. Qualified professional review remains required.

Run the variant-evidence workflow

1. Resolve and retrieve evidence

Call search_variant_evidence with:

assembly: GRCh38
query: <one public variant identifier or notation>

Do not add phenotype, disease, patient, family, or treatment context to this call. Preserve the returned contract fields, source links, limitations, and usage boundary.

2. Branch on the returned status

Treat the status as a control-flow value, not prose:

StatusRequired action
resolvedReuse the returned canonical_key; review the structured interpretation, provenance, source links, and limitations.
ambiguousShow the returned candidates and ask for an explicit public variant selection. Never choose a candidate automatically.
not_foundReport that no result was found within this service and query scope. Do not claim universal absence.
invalid_requestReport the validation problem and request a corrected public variant. Do not silently reinterpret the input.
unsupportedState the relevant service boundary and stop. Do not force the query into a supported form.
resolution_unavailableReport a temporary resolution or availability failure. Do not treat it as evidence absence.

Only a resolved result may proceed automatically into a variant-linked literature workflow. If a resolved interpretation itself reports unavailable evidence, preserve that separate limitation.

3. Review the evidence without overclaiming

For a resolved result:

  • Present the returned variant identity and canonical_key.
  • Preserve the automated variant-level ACMG/AMP decision-support result exactly as returned.
  • Cite the returned public sources and provenance.
  • Separate returned facts from the agent's synthesis.
  • State that qualified professional review is required.
  • Do not turn the result into a diagnosis, individual risk estimate, treatment recommendation, or standalone clinical report.

Chain into literature

Variant-linked literature

After a resolved evidence call, pass the returned canonical_key to search_variant_literature. Keep assembly as GRCh38. An optional question may narrow the literature focus, but it must remain a public scientific question and must not contain patient context.

Distinguish each result's match type:

  • exact_variant: direct match to the resolved variant
  • variant_alias: match through a reported alias
  • gene_association: broader gene-level association, not variant-specific proof

Literature associations do not alter the returned ACMG/AMP classification.

Publication details

Call get_publication_details only with a PMID returned by the literature tools. Preserve PubMed URLs, DOI/PMCID fields when present, retraction status, and the distinction between gene mentions and variant mentions.

Semantic corpus search

Use search_literature_corpus for a public natural-language scientific question or for discovery by publication identifier, gene, variant, phenotype, HPO, or OMIM concept. Treat results as source-linked candidates for professional review. A zero-result response means no result was returned for that bounded query, not that no relevant publication exists anywhere.

Do not place patient information into a corpus query, even if the query is not variant-specific.

Report a reproducible result

Include:

  1. The exact public query and GRCh38 assembly.
  2. The returned status and, if resolved, the canonical_key.
  3. The structured evidence or literature result without changing its meaning.
  4. Source links and publication identifiers.
  5. Match type for literature results.
  6. Access date and any availability limitation.
  7. This boundary statement:

This is public, variant-level decision support for qualified professional review. It does not evaluate patient, phenotype, family, segregation, or private case data and is not a diagnosis or treatment recommendation.

Falsifiable smoke test

Use the public rsID rs80357914 to test ambiguity handling:

Call search_variant_evidence with assembly GRCh38 and query rs80357914. If the
result is ambiguous, list the returned candidates and stop for explicit
selection. Do not select a candidate or call downstream literature tools.

The test passes only if an ambiguous response causes the workflow to stop without automatic candidate selection.

Official references

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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