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Use this agent when you need to synthesize and distill the core functions of a gene from multiple information sources (textual summaries, reviewed annotations, bioinformatics analysis) and populate the `core_functions` section of a gene review YAML file. This agent should be called after completing the gene research, annotation review, and bioinformatics analysis phases of a gene review project.
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Use this agent when you need to review UniProt annotation rules (ARBA, UniRule) for quality, biological accuracy, and GO annotation appropriateness. This agent performs comprehensive analysis of rule condition sets, evaluates literature support, assesses taxonomic scope, and recommends curation actions.
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Use this agent when you need to perform custom bioinformatics analyses on genes or proteins that go beyond standard database lookups. This includes situations where existing domain/family annotations from UniProt are insufficient, when you need to verify dubious annotations, or when you need to perform comparative analyses across gene sets. The agent creates reproducible analysis pipelines with proper dependency management and documentation.
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Query gnomAD (Genome Aggregation Database) for population allele frequencies, gene constraint scores, and variant annotations to interpret ENCODE regulatory variants. Use when the user needs allele frequencies for variants in ENCODE regulatory elements, wants to assess gene constraint (pLI, LOEUF) for ENCODE target genes, needs population-specific frequencies for GWAS variants overlapping cCREs, wants to filter variants by rarity before functional annotation, or is interpreting ENCODE CRISPR/MPRA results in the context of population genetics. Also use when the user mentions gnomAD, allele frequency, pLI, LOEUF, constraint, rare variants, population frequency, ExAC, or variant filtering.
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Guide for annotating ENCODE peaks with genomic features using ChIPseeker and GREAT. Use when users need to assign peaks to genes, determine genomic feature distribution (promoter, intron, intergenic), or perform gene ontology enrichment of peak-associated genes. Trigger on: peak annotation, ChIPseeker, GREAT, peak to gene, genomic feature, promoter enrichment, gene ontology, peak distribution, TSS distance, nearest gene.
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Query the UCSC Genome Browser REST API to retrieve regulatory tracks, DNA sequences, cCRE annotations, TF binding clusters, and track schemas for any genomic region. Use when the user wants to look up what regulatory elements exist at a genomic locus, retrieve DNA sequence under peaks, query ENCODE cCREs or TF rPeak clusters from UCSC, check what tracks are available for a genome assembly, get chromatin accessibility across cell types, or cross-reference ENCODE data with UCSC-hosted annotations. Also use when the user mentions UCSC, genome browser, cCRE lookup, SCREEN, TF binding clusters, DNA sequence retrieval, or track data extraction.
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Designing data stories and narrative-driven analytics in Salesforce: structuring CRM Analytics dashboards for executive communication, text widgets and conditional highlights for annotation, sequential narrative in Tableau Story sheets, and Einstein Discovery narrative REST endpoints. Trigger keywords: data storytelling dashboard, executive analytics narrative, CRM Analytics text widget annotation, Tableau story sheet, smart data discovery narrative API. NOT for building the Einstein Discovery story or prediction definition - use admin/einstein-discovery-setup. NOT for choosing between Reports, CRM Analytics and Tableau - use admin/einstein-analytics-basics.
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Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.
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Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
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Expert-level genomics covering whole genome sequencing, genome assembly, annotation, comparative genomics, functional genomics, single-cell genomics, and multi-omics integration.
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NLM PubTator3 实体级关系挖掘原子 skill — 在 PubMed 文献里检索"疾病-药物-基因-化学品-突变-物种-细胞系"的标注与关系三元组。当用户问"BTK 与肺曲霉病的关联文献"、"BTK 抑制剂(MeSH D000077180)在哪些研究被讨论"、"标注这些 PMID 中提到的所有疾病/药物/基因实体"、"ibrutinib 的 MeSH/DrugBank ID"、"voriconazole 与 CYP2C19 药物-基因相互作用"、"BRAF V600E 突变 / rs113488022 检索"、"实体共现 / co-mention / 关系挖掘 / 实体规范化 / NER / annotation / entity normalization / disease-gene association / drug-gene interaction / chemical-disease relation" 时使用。无需 API key,跨平台稳定。**实体级 + 关系级**定位 — 不做综合文献检索(走 pubmed-eutils / europepmc-search)、不做全文段落抽取(走 bioc-fulltext-fetch)、不做证据等级排序(走 medical-evidence-grading)。
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NLM PubTator3 实体级关系挖掘 — 检索"疾病-药物-基因-化学品-突变"在生物医学文献中的标注与关系。当用户问"哪些研究讨论了 BTK 抑制剂与肺曲霉病"、"基因 X 与疾病 Y 的关联"、"药物-基因相互作用"、"找含某突变的文献"、"PubTator / 实体标注 / NER / annotation / biomedical entity / 疾病基因关联 / 药物-基因 / 化学品-疾病 / 突变检索"时使用。无需 API key。**实体级 · 关系挖掘**定位 — 不做文献综合检索(用 pubmed-eutils / europepmc-search)、不做全文(用 BioC)、不做试验(用 ct-v2)。
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METAINFORMANT rules for directory src/metainformant/pharmacogenomics/annotations. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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METAINFORMANT rules for directory src/metainformant/ontology/annotation. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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METAINFORMANT rules for directory src/metainformant/dna/annotation. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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Implements type-safe Flutter routing using AutoRoute v11 with code generation and @RoutePage annotations. Activates when setting up AutoRoute navigation, configuring AutoRouterConfig or RootStackRouter, generating route files with build_runner, using @RoutePage, @PathParam, or @QueryParam annotations, implementing AutoTabsRouter for bottom navigation tabs, adding navigation guards with AutoRouteGuard, setting up nested or child routes, handling type-safe route parameters, or migrating from string-based Navigator routing to compile-time-verified routes. Ideal for large apps requiring refactoring confidence and zero string-based routing errors.
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Generates immutable Dart data classes, sealed union types, and deep copyWith using Freezed (v3.2.x) with optional json_serializable integration. Use when creating DTOs, API response models, app state classes, or any model requiring immutability, pattern matching, or JSON serialization. Activates on: @freezed annotation, freezed_annotation import, immutable data class request, copyWith deep nesting, union/sealed types for loading/data/error states, generic response wrappers, or build_runner code generation for models.
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Data Labeling Specialist IA — Expert en annotation de données (outils, contrôle qualité, agreement inter-annotateurs, active learning)
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This skill should be used when the user asks how to "use VibeTags", "add VibeTags annotations", "set up AI guardrails", "protect code from AI", "configure AI platforms", asks about @AILocked, @AIContext, @AIDraft, @AIAudit, @AIIgnore, @AIPrivacy, @AICore, @AIPerformance, @AIContract, @AITestDriven, @AIThreadSafe, @AIImmutable, @AIDeprecated, @AIObservability, @AIRegulation, @AIArchitecture, @AILegacyBridge, @AIStrictClasspath, @AIInternationalized, @AIPublicAPI, @AISchemaSafe, @AIStrictExceptions, @AIStrictTypes, @AIParallelTests, @AIIdempotent, @AIFeatureFlag, @AISecure, @AICallersOnly, @AISandboxOnly, @AIMemoryBudget, @AIPure, @AIDomainModel, @AIExtensible, @AIInputSanitized, @AISecureLogging, @AIExplain, @AIPrototype, @AISunset, @AITemporary annotations, or wants to control how AI tools interact with Java code.
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Adds accessibility annotations (ARIA labels, roles, alt text) to make web content accessible. Use when user asks to "add accessibility", "make accessible", "add aria labels", "wcag compliance", or "screen reader support".
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Maps query single-cell data onto reference atlases and transfers cell-type labels using scArches surgery (scVI/scANVI), Symphony, Azimuth, CellTypist, scPoli, popV, and foundation models, with explicit out-of-distribution and label-transfer uncertainty. Use when annotating new single-cell datasets against a pre-trained reference, deciding which mapping method fits, or judging whether transferred labels are trustworthy. For de novo clustering and manual annotation see single-cell/cell-annotation; for batch integration without a reference see single-cell/batch-integration.
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End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3 (or HOMER), and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.
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Maps metabolomics results to biological pathways via over-representation (ORA), metabolite-set enrichment (MSEA/QEA), mummichog/PSEA on raw m/z peaks, and network-diffusion enrichment (FELLA), with correct background-set construction and honest interpretive ceilings. Use when interpreting differential metabolites or an untargeted LC-MS feature table in pathway context, choosing ORA vs MSEA vs mummichog vs topology, or setting the reference/background set. For annotation confidence levels feeding ORA see metabolomics/metabolite-annotation; for gene-set concepts see pathway-analysis/go-enrichment and pathway-analysis/gsea; for joint gene+metabolite pathways see multi-omics-integration/mofa-integration.
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Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.
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