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
Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.
Calculate immune repertoire diversity metrics, compare samples, and track clonal dynamics using VDJtools. Use when analyzing repertoire diversity, finding shared clonotypes, or comparing immune profiles between conditions.
Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.
Publication-grade figure correctness and legibility rules. Load before drawing any plot and call `apply_figure_style()` — sets a role-mapped font-size ladder, outward ticks, frameless legends, and 300-dpi output. The skill is a checklist, not a house look: data fidelity (claim-titles tested against every row, excluded data never enters summaries), label economy (floor and ceiling), colour threading, chart-choice-by-data-shape, layout, and a render-then-verify QA loop (bbox collision + per-panel perceptual check). Ships helpers: focal_palette, bar_with_points, strip_with_median, end_of_line_labels, panel_letter, set_frame, panel_crops. For multi-panel figures load `figure-composer`; for whole-paper figure arc load `paper-narrative`.
Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.
Cell segmentation in fluorescence microscopy images. Supports Cellpose/cpsam (Cellpose 4.0) with additional backends planned. Produces segmentation masks, per-cell morphology metrics (area, diameter, centroid, eccentricity), overlay figures, and a report.md.
Classify germline variants from VCF/BCF files according to the ACMG/AMP 2015 28-criteria evidence framework and generate clinical-grade interpretation reports with per-variant evidence audit trails and ACMG SF v3.2 secondary findings screening.
>- Full reimplementation of DnaSP 6 for population genetics analysis of aligned DNA sequences. Covers nucleotide diversity, haplotype statistics, neutrality tests (Tajima's D, Fu & Li's D*/F*, R2), linkage disequilibrium (D, D', R², ZnS, Za, ZZ), minimum recombination (Rm), mismatch distribution, InDel polymorphism, between-population divergence (Dxy, Da, fixed/shared sites), outgroup-based Fu & Li D/F tests (fuliout), the HKA multi-locus neutrality test (hka), the McDonald-Kreitman test (mk), Ka/Ks (dN/dS) via the Nei-Gojobori (1986) method (kaks), Fu's Fs test (fufs), the site frequency spectrum (sfs, folded and outgroup-unfolded), transition/transversion ratio (tstv), and codon usage bias - RSCU (Sharp & Li 1987) and ENC (Wright 1990) (codon). Accepts FASTA or NEXUS input; outputs DnaSP-compatible TSV and a Markdown report.
| Fetch a region of cis-eQTL summary statistics from EBI eQTL Catalogue v7+ via tabix-on-FTP. Use when an agent needs eQTL beta / SE / p-value for every variant in a window around a gene's TSS for one specific dataset
Compute HEIM diversity and equity metrics from VCF or ancestry data. Generates heterozygosity, FST, PCA plots, and a composite HEIM Equity Score with markdown reports.
| Fetch a region of GWAS summary statistics from the NHGRI-EBI GWAS Catalog harmonised collection via tabix-on-FTP. Use when an agent needs GWAS beta / SE / p-value for every variant in a window for one specific study (GCST TSV slice in canonical format.
Two-sample Mendelian Randomisation from GWAS summary statistics with IVW, MR-Egger, weighted median/mode, and full sensitivity analysis (Cochran Q, Egger intercept, Steiger, F-statistic, leave-one-out).
Aggregates QC reports from any bioinformatics tool outputs (FastQC, fastp, STAR, Picard, samtools, etc.) into a single MultiQC HTML report plus a ClawBio markdown summary with per-sample QC metrics.
Pharmacogenomic report from DTC genetic data (23andMe/AncestryDNA) — 12 genes, 31 SNPs, 51 drugs
Unified personal genomic profile report — reads a PatientProfile JSON and synthesizes all skill results into a single "Your Genomic Profile" document.
Generates professional clinical PDF reports in English from WES (Whole Exome Sequencing) data with clinical interpretation summary, pharmacogenomic alerts, and follow-up recommendations.
Generates professional clinical PDF reports in Spanish from WES (Whole Exome Sequencing) data with clinical interpretation, pharmacogenomic alerts, and follow-up recommendations.
Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP, H&P, discharge summaries). Full support with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation tools.
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.
Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large arrays, ML pipelines, graph analytics, image processing) that would benefit from GPU acceleration, even if not explicitly requested.
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Automated cell behavior analysis from microscopy or XR lab recordings. Classifies cell motion phenotypes (migration, proliferation, apoptosis, division, quiescence), computes population-level quantitative metrics (growth rate, migration velocity, directionality index), and emits structured JSON for downstream reporting, plotting, or ELN integration.
Exports any structured experimental data (JSON, tables, time series) to well-formatted Excel (.xlsx) files. Auto-names sheets (Raw Data, Growth Curves, Cell Counts, etc.), adds unit headers and annotation rows, applies consistent styling, and produces lab-ready spreadsheets for sharing, archival, or downstream analysis in R, pandas, or Excel.
General-purpose experimental data extractor from lab video streams. Ingests footage from XR headsets or fixed cameras and extracts typed, timestamped measurements — liquid volume levels, color/turbidity shifts, cell and colony counts, pipette readouts, instrument display values, gel band intensities — emitting a time-series JSON or CSV table ready for downstream analysis, charting, or ELN attachment.
Domain-specialized chart generator for cell biology video analysis outputs. Consumes structured JSON from analyze_lab_video_cell_behavior or compatible sources and produces publication-ready figures — growth curves, cell trajectory maps, phenotype distribution charts, MSD plots, wound-closure timeseries, dose-response curves, and 96-well heatmaps — using matplotlib and seaborn. Exports PNG/PDF at configurable DPI for papers, ELN entries, or XR dashboards.
Facebook Research Hand Tracking Challenge Toolkit - evaluation and visualization tools for 3D hand tracking. Supports loading HOT3D data, computing metrics (PA-MPJPE, AUC, etc.), visualizing 3D pose projections, and generating tracking evaluation reports. Essential for benchmarking hand tracking algorithms.
High-quality 3D hand pose estimation for egocentric videos from ECCV 2024 (ap229997/hands). Provides 3D joint keypoints and skeleton visualization projected to 2D. Optimized for daily egocentric activities with state-of-the-art accuracy. Outputs hand skeleton overlays on video frames.
HOT3D (Hand-Object 3D Dataset) by Meta Facebook - multi-view egocentric hand and object 3D tracking for Aria/Quest smart glasses. State-of-the-art multi-view 3D hand pose, object pose, and hand-object interaction tracking. Supports visualization with 3D joint projections, meshes, and skeletal overlays on video frames.
> Dispatch biomedical research and data analysis tasks to Claude Code with K-Dense Scientific Skills. Use this skill when the user asks to run any bioinformatics, genomics, drug discovery, clinical data analysis, proteomics, multi-omics, medical imaging, or scientific computation task. Also use for literature search (PubMed, bioRxiv), pathway analysis, protein structure prediction, or scientific writing tasks.
> Send rich interactive cards with embedded images in Feishu group chats. Use when reporting progress, sharing analysis results, or presenting any content that benefits from mixed text+image layout in Feishu. Combines SVG UI templates (or matplotlib/PIL charts) with Feishu Card Kit API.
Use this skill whenever the user wants to visualize neuroimaging analysis results, including 3D brain connectivity networks, atlas-based regional activation summaries, or FreeSurfer cortical surface meshes with anatomical colors. Triggers include: 'brain visualization', 'visualize connectome', '3D brain network', 'zALFF visualization', 'brain activation map', 'FreeSurfer PLY export', 'surface mesh rendering', or any request to turn neuroimaging outputs into interpretable figures or 3D models.
Use this skill whenever any NeuroClaw diffusion MRI / DWI modality skill needs to execute concrete DIPY operations: load DWI (NIfTI+bvals+bvecs), optional masking, DTI fitting, compute FA/MD/AD/RD, and extract ROI statistics. This is the dedicated base/tool skill that contains all specific DIPY code and usage patterns. Never called directly by the user.
Use this skill whenever the user wants to preprocess diffusion MRI / DWI data, compute diffusion metrics (FA/MD/AD/RD, etc.), extract ROI-wise diffusion features, or run tractography/connectome-related workflows. Triggers include: 'DWI', 'DTI', 'diffusion MRI', 'FA', 'MD', 'AD', 'RD', 'eddy', 'topup', 'QSIPrep', 'tractography', 'connectome', 'TBSS', 'white matter microstructure'. This is the NeuroClaw modality-layer interface: it plans WHAT to do and delegates execution to tool skills.
Use this skill whenever the user wants to process structural MRI data (T1w, T2w, FLAIR, etc.) with FreeSurfer, especially for cortical/subcortical segmentation, surface reconstruction, parcellation, cortical thickness, volume statistics, or full recon-all pipeline. Triggers include: 'freesurfer', 'recon-all', 'segment MRI', 'FreeSurfer processing', 'cortical segmentation', 'subcortical segmentation', 'run recon-all', 'freesurfer T1', 'process brain MRI with freesurfer', 'aseg aparc', or any request to run FreeSurfer on NIfTI MRI data for research analysis.
Use this model doc whenever the user wants to run a classical General Linear Model (GLM) for task-evoked fMRI activation analysis. This is a non-deep-learning model route focused on design matrices, first-level/second-level statistics, and statistical maps.
Use this skill whenever the user wants to analyze already available UK Biobank data for brain-related research, including neurological outcomes, cognitive phenotypes, brain MRI derived phenotypes, survival analysis, subgroup analysis, propensity score analysis, mediation analysis, sensitivity analysis, machine learning, visualization, or manuscript-ready summaries. This skill only covers post-extraction analysis and explicitly excludes RAP access and data download guidance.
Create publication-quality matplotlib/seaborn charts with readable axes, tight layout, and curated palettes.