PDF generation toolkit. Create invoices, reports, certificates, forms, charts, tables, barcodes, QR codes, Canvas/Platypus APIs, for professional document automation.
Skills（SKILL.md）は、AIエージェント（Claude Code、Cursor、Codexなど）に特定の能力を追加するための設定ファイルです。
詳しく見る →PDF generation toolkit. Create invoices, reports, certificates, forms, charts, tables, barcodes, QR codes, Canvas/Platypus APIs, for professional document automation.
Build Shopify applications, extensions, and themes using GraphQL/REST APIs, Shopify CLI, Polaris UI components, and Liquid templating. Capabilities include app development with OAuth authentication, checkout UI extensions for customizing checkout flow, admin UI extensions for dashboard integration, POS extensions for retail, theme development with Liquid, webhook management, billing API integration, product/order/customer management. Use when building Shopify apps, implementing checkout customizations, creating admin interfaces, developing themes, integrating payment processing, managing store data via APIs, or extending Shopify functionality.
Internal ClaudeMap runtime for turning a repository into a live architecture map and driving that map during walkthroughs. Prefer the public commands in .claude/commands for normal use.
Web accessibility patterns for news sites, journalism tools, and academic platforms. Use when building accessible interfaces, auditing existing sites for WCAG compliance, writing alt text for news images, creating accessible data visualizations, or ensuring content reaches all readers including those using assistive technologies. Essential for newsroom developers and anyone publishing web content.
Legal methods for accessing paywalled and geo-blocked content. Use when researching behind paywalls, accessing academic papers, bypassing geographic restrictions, or finding open access alternatives. Covers Unpaywall, library databases, VPNs, and ethical access strategies for journalists and researchers.
Digital archiving workflows with AI enrichment, entity extraction, and knowledge graph construction. Use when building content archives, implementing AI-powered categorization, extracting entities and relationships, or integrating multiple data sources. Covers patterns from the Jay Rosen Digital Archive project.
Electron desktop application development with React, TypeScript, and Vite. Use when building desktop apps, implementing IPC communication, managing windows/tray, handling PTY terminals, integrating WebRTC/audio, or packaging with electron-builder. Covers patterns from AudioBash, Yap, and Pisscord projects.
Structured workflow for fact-checking claims in journalism. Use when verifying statements for publication, rating claims for fact-check articles, or building pre-publication verification processes. Includes claim extraction, evidence gathering, rating scales, and correction protocols.
Use this skill when creating new files that represent architectural decisions — data models, infrastructure configs, auth boundaries, API contracts, CI/CD pipelines, or event systems. Flags irreversible decisions and forces a discussion about trade-offs before committing.
Python data processing pipelines with modular architecture. Use when building content processing workflows, implementing dispatcher patterns, integrating Google Sheets/Drive APIs, or creating batch processing systems. Covers patterns from rosen-scraper, image-analyzer, and social-scraper projects.
Web scraping with anti-bot bypass, content extraction, undocumented APIs and poison pill detection. Use when extracting content from websites, handling paywalls, implementing scraping cascades or processing social media. Covers requests, trafilatura, Playwright with stealth mode, yt-dlp and instaloader patterns.
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Amazon FBA Calculator - Complete fee breakdown and profit analysis
Amazon keyword research and market opportunity analysis for sellers. Retrieve autocomplete suggestions (long-tail keywords), analyze competitor landscape, and assess market opportunity for any keyword on 12 Amazon marketplaces (US/UK/DE/FR/IT/ES/JP/CA/AU/IN/MX/BR). No API key required. Make sure to use this skill whenever the user mentions Amazon product research, finding products to sell on Amazon, Amazon keyword ideas, niche analysis, competition analysis for Amazon, market opportunity on Amazon, comparing Amazon keywords, evaluating whether a product is worth selling, Amazon autocomplete data, seasonal demand for Amazon products, or anything related to researching what to sell on Amazon — even if they don't explicitly say 'keyword research'. Also trigger when the user asks vague questions like 'is this a good product to sell?', 'what's the competition like for X on Amazon?', 'should I sell X or Y?', or 'what are people searching for on Amazon?'.
Guidelines for generating clinical decision support (CDS) documents: patient cohort analyses (biomarker-stratified outcomes) and treatment recommendation reports (GRADE-graded evidence). Covers document structure, executive summary design, evidence grading (GRADE 1A–2C), statistical reporting (HR, CI, survival), and biomarker integration. Use when creating pharmaceutical research documents, clinical guidelines, or regulatory submissions.
Low-level Python plotting library for full customization of scientific figures. Use for publication-quality plots (line, scatter, bar, heatmap, contour, 3D), multi-panel subplot layouts, and fine-grained control over every visual element. Export to PNG/PDF/SVG. For quick statistical plots use seaborn; for interactive plots use plotly.
PyHealth is a Python library for healthcare machine learning. Build clinical prediction models from EHR (Electronic Health Record) data: process MIMIC-III/IV, eICU, and OMOP-CDM datasets, encode medical codes (ICD, ATC, NDC), construct patient-level datasets, and train models (Transformer, RETAIN, GRASP, MedBERT) for tasks including mortality prediction, drug recommendation, readmission, and diagnosis prediction. Alternatives: FIDDLE (EHR preprocessing only), clinical-longformer (NLP on clinical notes only), ehr-ml (EHR embedding only).
Bayesian modeling with PyMC 5. 8-step workflow: define model, set priors, define likelihood, sample (NUTS/ADVI), diagnose (R-hat, ESS, divergences), interpret posteriors, compare models (LOO/WAIC), predict. Hierarchical, logistic, GP model variants. Prior/posterior predictive checks.
Classical machine learning in Python. Use for classification, regression, clustering, dimensionality reduction, model evaluation, hyperparameter tuning, and preprocessing pipelines. Covers linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. For deep learning use PyTorch/TensorFlow; for gradient boosting at scale use XGBoost/LightGBM.
Survival analysis and time-to-event modeling with scikit-survival. Cox proportional hazards (standard/elastic net), Random Survival Forests, Gradient Boosting, SVMs for censored data. C-index (Harrell/Uno), Brier score, time-dependent AUC evaluation. Kaplan-Meier, Nelson-Aalen, competing risks. scikit-learn Pipeline/GridSearchCV compatible. For frequentist regression use statsmodels; for Bayesian survival use pymc; for simpler parametric models use lifelines.
Parse and create FCS (Flow Cytometry Standard) files v2.0-3.1. Read event data as NumPy arrays, extract channel metadata, handle multi-dataset files, export to CSV/FCS. For advanced gating and compensation use FlowKit.
Query NCI Imaging Data Commons (IDC) for cancer radiology and pathology imaging datasets hosted on Google Cloud. Search DICOM collections by modality, anatomical site, cancer type, or collection name. Download images via Google Cloud Storage or IDAT tool. 50TB+ of publicly accessible DICOM images. Requires Google Cloud account for large downloads; small queries work without billing. For local DICOM processing use pydicom-medical-imaging; for whole-slide pathology use histolab.
OMERO is an open-source platform for biological image data management. Use the omero-py Python client to connect to an OMERO server, search and retrieve images as numpy arrays, annotate images with tags and key-value pairs, manage ROIs, and integrate OMERO image data into downstream analysis pipelines — all programmatically without the OMERO desktop GUI.
PathML is an open-source toolkit for computational pathology. Use it to process whole-slide images (WSIs): load slides, extract tiles, apply stain normalization and nuclear segmentation preprocessing, extract features, and train machine learning models. Supports H&E and multiplex imaging. Ideal for building end-to-end digital pathology pipelines from raw WSI files to quantitative outputs.
Pure Python DICOM library for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM files, extract pixel data as NumPy arrays, access/modify metadata tags, apply windowing (VOI LUT), anonymize PHI, build DICOM from scratch, process series into 3D volumes. For whole-slide pathology images use histolab; for NIfTI neuroimaging use nibabel.
Python library for tracking particles (fluorescent spots, colloids, vesicles, cells) in video microscopy using the Crocker-Grier algorithm. Core modules: locate particles in single frames, batch-process image sequences, link positions into trajectories, filter short-lived tracks, and compute mean squared displacement (MSD) for diffusion analysis. Supports 2D and 3D tracking with subpixel accuracy. Integrates with pims for reading TIF stacks, AVI, and image series. Use when you need quantitative single-particle tracking (SPT) from fluorescence or brightfield video and downstream diffusion coefficient extraction.
Interactive scientific visualization with Plotly. Two-layer API: plotly.express (px) for one-liner DataFrame plots and plotly.graph_objects (go) for full trace-level control. 40+ chart types with hover, zoom, pan, and animation. Exports to interactive HTML or static PNG/SVG/PDF via kaleido. Use for interactive web figures, volcano plots with gene hover info, dose-response dashboards, gene expression heatmaps, and 3D molecular visualizations. Use seaborn for statistical summaries with automatic aggregation; use matplotlib for fine-grained publication figures; use plotly for interactive or web-embedded output.
Interactive visualization with Plotly. 40+ chart types (scatter, line, bar, heatmap, 3D, statistical, geographic) with hover, zoom, and pan. Use for exploratory analysis, dashboards, and presentations. Two APIs: Plotly Express (quick, DataFrame-oriented) and Graph Objects (fine-grained control). For static publication figures use matplotlib; for statistical grammar use seaborn.
Statistical visualization library built on matplotlib with native pandas DataFrame support. Automatic aggregation, confidence intervals, and grouping for distribution plots (histplot, kdeplot), categorical comparisons (boxplot, violinplot, stripplot), relational plots (scatterplot, lineplot), regression plots (regplot, lmplot), matrix plots (heatmap, clustermap), and multi-variable grids (pairplot, jointplot, FacetGrid). Use seaborn for statistical summaries with minimal code; use matplotlib for fine-grained figure control; use plotly for interactive HTML output.
Statistical visualization built on matplotlib with pandas integration. Distribution plots (histplot, kdeplot, violinplot, boxplot), relational plots (scatterplot, lineplot), categorical comparisons, regression, correlation heatmaps. Automatic aggregation and CI. For interactive plots use plotly; for low-level control use matplotlib.
Annotated data matrices for single-cell genomics. AnnData stores expression data (X) with observation metadata (obs), variable metadata (var), layers, embeddings (obsm/varm), graphs (obsp/varp), and unstructured data (uns). Use for .h5ad/.zarr file handling, dataset concatenation, and scverse ecosystem integration. For analysis workflows use scanpy; for probabilistic models use scvi-tools.
GATK Best Practices pipeline for germline SNP and indel variant calling from WGS/WES BAM files. Runs HaplotypeCaller in GVCF mode per sample, consolidates with GenomicsDBImport, joint-genotypes with GenotypeGVCFs, and applies VQSR or hard filters. Requires BWA-MEM2-aligned, markdup, and BQSR-processed BAMs. Use DeepVariant instead for a faster deep-learning alternative; GATK is the ENCODE/NIH standard for research and clinical genomics.
Geniml is a Python library for genomic interval machine learning. Train and apply region2vec embeddings to convert BED file regions into numeric vectors, load and index genomic interval datasets for ML pipelines, search embedding spaces with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, cross-sample region comparison, and building ML models on genomic intervals.
NHGRI-EBI GWAS Catalog REST API for SNP-trait associations from published genome-wide association studies. Query studies, associations, variants, traits, genes, and summary statistics. Build polygenic risk score candidates, analyze variant pleiotropy, download summary statistics for Manhattan plots. No authentication required.
Ultra-fast RNA-seq transcript and gene-level quantification using quasi-mapping (no BAM required). Builds a k-mer index from a transcriptome FASTA, then quantifies reads in minutes. Outputs transcript-level TPM/count tables (quant.sf) with optional GC-bias and sequence-bias correction. Integrates directly with tximeta/tximport for DESeq2 or edgeR. Use STAR instead when a genome-aligned BAM is required for variant calling or visualization.
Python API v2 for programming Opentrons OT-2 and Flex liquid handling robots. Write protocols as Python files with metadata and a run() function; control pipettes, labware, and hardware modules (thermocycler, heater-shaker, magnetic, temperature). Simulate locally with opentrons_simulate, then upload to the robot app. Use PyLabRobot instead for hardware-agnostic scripts that run on Hamilton, Tecan, or other vendors.
Guide to quantitative Western blot analysis covering band detection, two-step normalization, fold change calculation, statistical aggregation across biological replicates, and publication-ready visualization. Consult when analyzing blot images with multiple conditions and repetitions, choosing normalization strategies, or preparing densitometry figures for publication.
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Pipeline for analyzing Neuropixels extracellular electrophysiology recordings. Covers probe geometry loading (ProbeInterface), spike sorting with Kilosort via SpikeInterface, quality metrics computation, unit curation (ISI violations, firing rate, signal-to-noise), and post-sort analysis (PSTH, tuning curves, population decoding) using pandas and matplotlib. Designed for acute and chronic Neuropixels 1.0/2.0/Ultra recordings from rodent and primate experiments.
PyTorch Geometric (PyG) for graph neural networks. Node classification, graph classification, link prediction with GCN, GAT, GraphSAGE, GIN layers. Message passing framework, mini-batch processing, heterogeneous graphs, neighbor sampling for large-scale learning, model explainability. Supports molecular property prediction (QM9, MoleculeNet), social networks, knowledge graphs, 3D point clouds. For non-graph deep learning use PyTorch directly; for traditional graph algorithms use NetworkX.
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