Machine Learning Claude Skills
1 780 machine learning skills from 282 authors. They train and fine-tune models, build embeddings, run RAG and measure quality. Half of them fit into 2 252 tokens or less — that is what one costs your context window when the agent loads it. 424 ship runnable scripts rather than instructions alone. 10 of them cannot work without an MCP server, most often rube. We also found 397 copies of these same skills sitting in other people's repositories — counted once here, not 397 times.
1 780 unique 282 authors 5 updated this month 189 from vendors
In-process semantic search over text files or in-memory strings, using Gemini embeddings via the CF AI Gateway. Use when user wants fuzzy/conceptual search where exact-keyword grep would miss — "sessions discussing regulatory constraints", "code about retry logic", "notes mentioning burnout even if the word isn't there". Complements searching-codebases (regex/AST) and extracting-keywords (YAKE). Do NOT use when an exact string/regex match is what's wanted — grep/rg wins on speed and precision there.
ワークスペース内の遠い知識や活動をつないで、新しいアイデアを生成する思考支援スキル。過去記事・メモ・RAG・発信資産から企画や実装ネタを出したい時に使用。「アイデア出して」「知識をつないで」「一見関係ないものをつないで」で使用。
ワークスペース全体をベクトル検索＋構造化ファクト管理する任意スキル。ユーザーがRAGのセットアップ・検索・ファクト操作を明示的に依頼した場合に使う。「RAGをセットアップして」「RAGで探して」「ファクト登録」で使用。
Use when defining or implementing Go interfaces, designing abstractions, creating mockable boundaries for testing, or composing types through embedding. Also use when deciding whether to accept an interface or return a concrete type, or using type assertions or type switches, even if the user doesn't explicitly mention interfaces. Does not cover generics-based polymorphism (see go-generics).
Use when optimizing Go code, investigating slow performance, or writing performance-critical sections. Also use when a user mentions slow Go code, string concatenation in loops, or asks about benchmarking, even if the user doesn't explicitly mention performance patterns. Does not cover concurrent performance patterns (see go-concurrency).
Create a new evaluation dataset or add cases to an existing one for the Azure SDK QA bot evaluation. WHEN: "add eval dataset item", "add a test case", "new evaluation dataset", "create dataset", "add question to dataset", "curate eval data", "promote staging cases", "upload dataset asset", "new scenario dataset". DO NOT USE FOR: running evaluations, pipeline troubleshooting, knowledge-graph indexing.
TRIGGER THIS when researching market salaries, building compensation strategies, creating salary bands, analyzing pay equity, conducting salary reviews, preparing compensation proposals, or ensuring competitive compensation. Analyzes market data, builds equity-focused salary structures, identifies pay gaps, and creates data-driven compensation recommendations.
TRIGGER THIS when building DEI strategy, creating diversity goals, designing inclusive programs, building equity analyses, developing training, addressing inclusion issues, or tracking DEI metrics. Develops comprehensive diversity, equity, and inclusion strategies aligned to business goals with measurable outcomes, training programs, metrics frameworks, and accountability systems.
TRIGGER THIS when preparing interviews, planning hiring processes, creating interview questions, building evaluation frameworks, developing interviewer guides, or standardizing interview approaches. Builds complete interview kits with competency-based questions, scorecards, rubrics, interviewer training, and structured evaluation protocols.
Generate project status reports with RAG status, milestones, risks, blockers, resource utilization, and executive summaries
Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
Design customer onboarding playbooks with milestone tracking, training plans, health checks, and success criteria
> ESM2 protein language model for embeddings and sequence scoring. (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.
Query NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
Workflow for ligand-receptor communication inference in single-cell or spatial data with sender-receiver summaries and cautious interpretation.
End-to-end EHR predictive modeling pipeline with PyHealth, covering dataset loading, task definition, model training, evaluation, calibration, and clinical interpretation.
> ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.
Workflow for regulatory network inference, regulon scoring, perturbation-aware comparison, and network visualization.
Trim multiple sequence alignments using ClipKIT, trimAl, BMGE, Divvier, or HMMcleaner with mode selection guidance per downstream goal. Use when removing unreliable columns or contaminating residues before phylogenetic inference, HMM building, or selection analysis.
Amplicon sequence variant (ASV) inference from 16S rRNA or ITS amplicon sequencing using DADA2. Covers quality filtering, error learning, denoising, and chimera removal. Use when processing demultiplexed amplicon FASTQ files to generate an ASV table for downstream analysis.
Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.
Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
Run Bayesian phylogenetic analysis with MrBayes, BEAST2, RevBayes, and PhyloBayes including MCMC convergence diagnostics and model comparison. Use when needing posterior probability support, Bayesian model averaging, site-heterogeneous models for deep phylogenies, or formal model comparison via stepping-stone sampling.
End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.
Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when batch-mapping gene IDs across species, or when comparing the resources for consensus calls. Encodes confidence-level semantics, 1:1 vs 1:many vs many:many, HomoloGene deprecation, and when to defect to de novo computation.
Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.
Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.
Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.
Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and clustering on single-cell data.
Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.
Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.
Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.
Predict protein-coding genes in eukaryotic genomes using BRAKER3 for combined RNA-seq and protein evidence, or GALBA for protein-only evidence. Runs Augustus with trained parameters for accurate gene models. Use when annotating a newly assembled eukaryotic genome or improving existing gene predictions.
Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.
End-to-end gene regulatory network inference pipeline from processed single-cell data to regulon discovery and perturbation simulation. Supports RNA-only (pySCENIC) and multiome (SCENIC+) paths. Use when building gene regulatory networks from single-cell transcriptomic or multiome data.
Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.
Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
Polish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions. Use when improving ONT-only assemblies or calling variants from Nanopore data without short-read polishing.
Analyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions.
Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.
Implements nested cross-validation and stratified splits for unbiased model evaluation on biomedical datasets. Prevents data leakage and overfitting in biomarker discovery. Use when validating classifiers or optimizing hyperparameters on omics data.
Build maximum likelihood phylogenetic trees using IQ-TREE2 and RAxML-NG with expert model selection, branch support assessment, and topology testing. Use when inferring publication-quality ML trees, selecting substitution models, interpreting bootstrap and concordance factor support, or running partitioned phylogenomic analyses.
Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.