2 399 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 951 tokens or less — that is what one costs your context window when the agent loads it. 545 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 355 copies of these same skills sitting in other people's repositories — counted once here, not 355 times.
2 399 unique 443 authors 1 328 updated this month 230 from vendors
Aggregate results, train ML models, and produce reports with validated references.
Build production-ready Plotly Dash dashboards with consistent theming, clear layouts, and performant callbacks.
Guide Claude through omicverse's bulk RNA-seq DEG pipeline, from gene ID mapping and DESeq2 normalization to statistical testing, visualization, and pathway enrichment. Use when a user has bulk count matrices and needs differential expression analysis in omicverse.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Run bioinformatics analyses using Lobster AI - single-cell RNA-seq, bulk RNA-seq, literature mining, dataset discovery, quality control, and visualization. Use when analyzing genomics data, searching for papers/datasets, or working with H5AD, CSV, GEO/SRA accessions, or biological data. Requires lobster-ai package installed.
Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.
Production-ready genomics and epigenomics data processing for BixBench questions. Handles methylation array analysis (CpG filtering, differential methylation, age-related CpG detection, chromosome-level density), ChIP-seq peak analysis (peak calling, motif enrichment, coverage stats), ATAC-seq chromatin accessibility, multi-omics integration (expression + methylation correlation), and genome-wide statistics. Pure Python computation (pandas, scipy, numpy, pysam, statsmodels) plus ToolUniverse annotation tools (Ensembl, ENCODE, SCREEN, JASPAR, ReMap, RegulomeDB, ChIPAtlas). Supports BED, BigWig, methylation beta-value matrices, Illumina manifest files, and multi-sample clinical data. Use when processing methylation data, ChIP-seq peaks, ATAC-seq signals, or answering questions about CpG sites, differential methylation, chromatin accessibility, histone marks, or epigenomic statistics.
Compare GWAS studies, perform meta-analyses, and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare study designs, effect sizes, ancestry diversity, and heterogeneity statistics. Use when comparing GWAS studies for a trait, performing meta-analysis of genetic loci, assessing replication across cohorts, or exploring the genetic architecture of complex diseases.
Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and neuron counting datasets. Use when analyzing microscopy measurement data, colony area/circularity, cell count statistics, swarming assays, co-culture ratio optimization, or answering questions about imaging-derived quantitative data.
Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics. Computes treeness, RCV, treeness/RCV, parsimony informative sites, evolutionary rate, DVMC, tree length, alignment gap statistics, GC content, and bootstrap support using PhyKIT, Biopython, and DendroPy. Performs NJ/UPGMA/parsimony tree construction, Robinson-Foulds distance, Mann-Whitney U tests, and batch analysis across gene families. Integrates with ToolUniverse for sequence retrieval (NCBI, UniProt, Ensembl) and tree annotation. Use when processing FASTA/PHYLIP/Nexus/Newick files, computing phylogenetic metrics, comparing taxa groups, or answering questions about alignments, trees, parsimony, or molecular evolution.
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Calculates genetic risk profiles, interprets PRS percentiles, and assesses disease predisposition across conditions including type 2 diabetes, coronary artery disease, and Alzheimer's disease. Use when asked to calculate polygenic risk scores, interpret genetic risk for complex diseases, build custom PRS from GWAS data, or answer questions like "What is my genetic predisposition to breast cancer?
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural variants, assessing CNV pathogenicity, comparing cohorts, or answering variant analysis questions.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
Integrate digital health data sources (Apple Health, Fitbit, Oura Ring) and connect to WellAlly.tech knowledge base. Import external health device data, standardize to local format, and recommend relevant WellAlly.tech knowledge base articles based on health data. Support generic CSV/JSON import, provide intelligent article recommendations, and help users better manage personal health data.
| parallel computing, and performance optimization.
| Skills for single-cell and spatial omics data analysis. Best practices, code snippets, and workflows for the scverse ecosystem.
| Skills for spatial transcriptomics analysis including single-cell to spatial mapping (MOSCOT), 3D visualization (PyVista), and related spatial workflows.
> update, stakeholder update, project update, status report, QBR, or executive communication. Calibrates for audience and cadence.
Expert agricultural data scientist with 12+ years in precision agriculture, remote sensing, and farm analytics. Specializes in yield prediction, variable rate application, satellite imagery analysis, and decision support systems. Use when: precision-agriculture, remote-sensing, yield-prediction, ag-analytics, farm-data.
Elite Data Scientist skill with expertise in statistical analysis, predictive modeling, experimental design (A/B testing), feature engineering, and data visualization. Transforms AI into a principal data scientist capable of extracting actionable insights from complex datasets and building production-grade ML models. Use when: data-science, statistics, machine-learning, predictive-modeling,
Elite bioinformatics scientist specializing in genomic data analysis, NGS pipeline development, variant calling, transcriptomics, and precision medicine. Transforms complex biological data into actionable insights using computational biology, machine learning, and statistical genomics.
Expert-level Business Analyst skill covering requirements analysis, process modeling, data analysis, stakeholder management, and solution assessment. Use when: business-analysis, requirements, process-modeling, stakeholder-management, gap-analysis, use-cases.
Expert-level Data Analyst skill covering SQL analysis, Python/pandas data manipulation, statistical analysis, A/B test design and interpretation, business intelligence, dashboard design, and data storytelling
Expert Test Prep Instructor specializing in SAT, ACT, GRE, GMAT, LSAT, and professional certification exam preparation. Expert in test-taking strategies, content review, performance analytics, and score improvement methodologies. Use when: test-prep, sat, act, gre, gmat, lsat, exam-prep, test-strategies, score-improvement.
Create and manipulate Excel XLSX files programmatically. Use when the user needs to generate spreadsheets, modify XLSX templates, extract spreadsheet content, or automate Excel workflows. Supports both template-based generation (for branding compliance) and from-scratch creation. Keywords: Excel, XLSX, spreadsheet, workbook, worksheet, data, report, template, financial, analysis.
Maintain and debug the link-curator web dashboard (port 8090). Separate process from the official Hermes dashboard.
Analyzes CSV files and automatically generates comprehensive summaries with statistical insights, data quality checks, and visualizations using Python and pandas. No questions asked — just upload a CSV and get a full analysis immediately.
Create interactive, custom data visualizations using d3.js — including charts, graphs, network diagrams, and geographic maps. Use when you need fine-grained control over visual elements, transitions, or interactions beyond what standard charting libraries offer, in any JavaScript environment (vanilla JS, React, Vue, Svelte, etc.).
Generate candlestick price charts for any asset from existing OHLC data, without handling data fetching.
Transform data between JSON, CSV, and other formats with filtering, mapping, and flattening. Use when: (1) Converting API responses to CSV, (2) Processing data pipelines, (3) Extracting specific fields, or (4) Flattening nested structures.
Generate a clean white Tailwind CDN report page from user content, optionally password-gate viewing via client-side decryption, and deploy to Originless/IPFS.
Analyze Google Maps review data from CSV exports. Sentiment analysis, trend detection, competitor review comparison, and reputation insights — no API key needed.
Analyze local competitors from a Google Maps CSV export. Compare ratings, reviews, digital presence, and positioning. Find market gaps and opportunities without any API calls.
Export Google Maps business data to CSV, JSON, or CRM format (HubSpot, Pipedrive, Salesforce). Bulk export with custom field mapping and filtering.
>- Research and analyze urban design precedents systematically. Generates structured case study reports with quantitative metrics, design principles, lessons learned, and transferability assessment. Use when the user asks for precedents, case studies, reference projects, comparable developments, benchmarks from other cities, examples of similar projects, or best practice examples. Also use when the user names a specific urban project and wants it analyzed as a precedent.
>- Python computational tools for urban design metric calculations including density, FAR, walkability scoring, parking requirements, green space analysis, and block optimization. Use when the user asks to calculate density, compute FAR, score walkability, determine parking requirements, analyze green space provision, optimize block dimensions, run urban metrics, or perform any quantitative urban design calculation. Also use when precise numbers are needed for any urban design metric rather than rules of thumb.