SciAgent-Skills

In the author's own words, from each skill's SKILL.md — showing the first 30 of 208 files.

• opentrons-integration

  Opentrons Protocol API v2 for OT-2/Flex: Python protocols for pipetting, serial dilutions, PCR, plate replication; control thermocycler, heater-shaker, magnetic, temperature modules. Use pylabrobot for multi-vendor.

  legacy/opentrons-integration/SKILL.md on GitHub (opens in a new tab)

• plotly-interactive-visualization

  Interactive visualization with Plotly. 40+ chart types (scatter, line, heatmap, 3D, geographic) with hover, zoom, pan. Two APIs: Plotly Express (DataFrame) and Graph Objects (fine control). For static publication figures use matplotlib; for statistical grammar use seaborn.

  legacy/plotly-interactive-visualization/SKILL.md on GitHub (opens in a new tab)

• seaborn-statistical-visualization

  Statistical visualization on matplotlib + pandas. Distributions (histplot, kdeplot, violin, box), relational (scatter, line), categorical, regression, correlation heatmaps. Auto aggregation/CIs. Use plotly for interactive; matplotlib for low-level.

  legacy/seaborn-statistical-visualization/SKILL.md on GitHub (opens in a new tab)

• single-cell-annotation

  Best practices for single-cell RNA-seq cell type annotation including marker-based, reference-based, and automated classification approaches.

  legacy/single-cell-annotation/SKILL.md on GitHub (opens in a new tab)

• sciagent-skill-creator

  Scaffold a new SciAgent-Skills entry. Picks pipeline/toolkit/database/guide template, creates skills/{category}/{name}/SKILL.md with valid frontmatter, appends the registry.yaml entry, runs validation. Enforces name uniqueness, kebab-case, description keyword rules, schema rules from AGENTS.md. TRIGGER when user says (any language): "add a SciAgent skill", "add a skill for <X>", "create new skill…

  .claude/skills/sciagent-skill-creator/SKILL.md on GitHub (opens in a new tab)

• pymc-bayesian-modeling

  Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.

  skills/biostatistics/pymc-bayesian-modeling/SKILL.md on GitHub (opens in a new tab)

• scikit-survival-analysis

  Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.

  skills/biostatistics/scikit-survival-analysis/SKILL.md on GitHub (opens in a new tab)

• statistical-analysis

  Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for publication. Covers frequentist (t-test, ANOVA, chi-square, regression, correlation, survival, count, reliability) and Bayesian. Use statsmodels or pymc-bayesian-modeling to fit.

  skills/biostatistics/statistical-analysis/SKILL.md on GitHub (opens in a new tab)

• statsmodels-statistical-modeling

  Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.

  skills/biostatistics/statsmodels-statistical-modeling/SKILL.md on GitHub (opens in a new tab)

• cellpose-cell-segmentation

  DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm segment cells without retraining. Outputs label masks for morphology and tracking. Use scikit-image watershed for rule-based; Cellpose when DL generalization across staining is needed.

  skills/cell-biology/cellpose-cell-segmentation/SKILL.md on GitHub (opens in a new tab)

• flowio-flow-cytometry

  Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.

  skills/cell-biology/flowio-flow-cytometry/SKILL.md on GitHub (opens in a new tab)

• napari-image-viewer

  Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review.

  skills/cell-biology/napari-image-viewer/SKILL.md on GitHub (opens in a new tab)

• opencv-bioimage-analysis

  Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.

  skills/cell-biology/opencv-bioimage-analysis/SKILL.md on GitHub (opens in a new tab)

• pyimagej-fiji-bridge

  Python bridge to ImageJ2/Fiji for macros, plugins (Bio-Formats, TrackMate, Analyze Particles), NumPy↔ImagePlus/ImgLib2 exchange, and ImageJ Ops. Automates Fiji headlessly from Python. Use scikit-image for pure Python without Fiji plugins; napari for visualization.

  skills/cell-biology/pyimagej-fiji-bridge/SKILL.md on GitHub (opens in a new tab)

• scikit-image-processing

  Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.

  skills/cell-biology/scikit-image-processing/SKILL.md on GitHub (opens in a new tab)

• trackpy-particle-tracking

  Python library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extractio…

  skills/cell-biology/trackpy-particle-tracking/SKILL.md on GitHub (opens in a new tab)

• matplotlib-scientific-plotting

  Low-level Python plotting for scientific figures: publication-quality line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control of every element. PNG/PDF/SVG export. Use seaborn for quick stats, plotly for interactive.

  skills/data-visualization/matplotlib-scientific-plotting/SKILL.md on GitHub (opens in a new tab)

• molecular-visualization-3dmol

  3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate trajectories with a frame-delay (interval, ms) control; and animate vibrational normal modes via vibrate() from per-atom dx/dy/dz displacements or from precomputed frames. Output standalone HTML that loads 3Dmol from a CDN,…

  skills/data-visualization/molecular-visualization-3dmol/SKILL.md on GitHub (opens in a new tab)

• multipanel

  Assemble multiple plots into ONE publication-ready multi-panel journal figure (e.g. Figure 1 with panels A, B, C). Use whenever the user asks to combine, compose, or lay out several plots as a single composite figure — newly plotted from data or from already-rendered panels the user supplies (PNG/PDF). Ask the user to pick one of two approaches: (1) redraw every panel into one unified figure usin…

  skills/data-visualization/multipanel/SKILL.md on GitHub (opens in a new tab)

• omics-plotting

  omics-plotting: publication-style figure authoring for omics / bioinformatics results with matplotlib / seaborn. Read this before writing any plotting or figure code in any omics analysis — RNA-seq, proteomics, single-cell, variant, or database results — not only when a plot is explicitly requested: whenever an analysis will produce a figure, load this first and follow its recipes. Covers volcano…

  skills/data-visualization/omics-plotting/SKILL.md on GitHub (opens in a new tab)

• plotly-interactive-plots

  Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graph_objects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido. Use for volcano plots with gene hover, dose-response dashboards, expression heatmaps, 3D molecular views. Use seaborn for stats; matplotlib…

  skills/data-visualization/plotly-interactive-plots/SKILL.md on GitHub (opens in a new tab)

• scientific-visualization

  Guide for choosing and creating scientific visualizations for publications and talks. Covers chart-type selection by data structure, color theory for accessibility/print, figure composition, journal formatting (Nature, Cell, ACS), and common pitfalls. Consult when visualizing data or preparing submission figures.

  skills/data-visualization/scientific-visualization/SKILL.md on GitHub (opens in a new tab)

• seaborn-statistical-plots

  Statistical visualization on matplotlib with native pandas support. Auto aggregation, CIs, grouping for distributions (histplot, kdeplot), categorical (boxplot, violinplot), relational (scatterplot, lineplot), regression (regplot, lmplot), matrix (heatmap, clustermap), grids (pairplot, FacetGrid). Use for quick statistical summaries; matplotlib for fine control; plotly for interactive HTML.

  skills/data-visualization/seaborn-statistical-plots/SKILL.md on GitHub (opens in a new tab)

• statistical-significance-annotation

  Guide for annotating statistical significance (p-value asterisks) on comparison plots. Covers standard notation (ns, *, **, ***, ****), matplotlib bracket+asterisk implementation, and use with seaborn box/violin/bar plots. Use when preparing publication-ready figures with significance markers.

  skills/data-visualization/statistical-significance-annotation/SKILL.md on GitHub (opens in a new tab)

• arboreto-grn-inference

  GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.

  skills/genomics-bioinformatics/arboreto-grn-inference/SKILL.md on GitHub (opens in a new tab)

• biopython-molecular-biology

  Molecular biology toolkit: sequence manipulation, FASTA/GenBank/PDB I/O, NCBI Entrez, BLAST automation, pairwise/MSA alignment, Bio.PDB, phylogenetic trees. Use for batch processing, custom pipelines, format conversion, PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.

  skills/genomics-bioinformatics/biopython-molecular-biology/SKILL.md on GitHub (opens in a new tab)

• biopython-sequence-analysis

  Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.

  skills/genomics-bioinformatics/biopython-sequence-analysis/SKILL.md on GitHub (opens in a new tab)

• etetoolkit

  ETE Toolkit (ETE3): Python phylogenetic tree analysis and visualization. Parse Newick/NHX/PhyloXML, traverse/annotate nodes, render figures with TreeStyle/NodeStyle, integrate NCBI taxonomy, run PhyloTree comparative genomics. Use for species trees, gene family evolution, annotated tree figures.

  skills/genomics-bioinformatics/etetoolkit/SKILL.md on GitHub (opens in a new tab)

• homer-motif-analysis

  De novo and known TF motif enrichment in ChIP-seq/ATAC-seq peaks via HOMER. findMotifsGenome.pl finds over-represented patterns vs background; annotatePeaks.pl assigns context (TSS distance, gene, repeat). Use after MACS3 to identify enriched TFs, annotate peaks with nearest genes, and validate ChIP-seq via the target motif.

  skills/genomics-bioinformatics/homer-motif-analysis/SKILL.md on GitHub (opens in a new tab)

• macs3-peak-calling

  Poisson-model peak caller for ChIP-seq/ATAC-seq BAMs. MACS3 callpeak finds enriched regions (TF sites or histone marks) vs input/IgG; outputs BED narrowPeak/broadPeak for motif analysis, annotation, and differential binding. Use narrow peaks for TF ChIP-seq and ATAC-seq; broad for H3K27me3, H3K9me3, and other broad marks.

  skills/genomics-bioinformatics/macs3-peak-calling/SKILL.md on GitHub (opens in a new tab)

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