3. Data, Science & AI

Provides compact patterns for single-cell RNA-seq analysis using AnnData and Scanpy, including QC, preprocessing, clustering, and visualization.

Provides single-cell RNA-seq analysis tools for AnnData and Scanpy, including quality control, normalization, clustering, and visualization.

Detects AI hallucinations by tracing claims to source materials and validating if fetched information supports conclusions.

Comprehensive guide for Cloudflare Workers AI, covering model deployment, Vectorize integration, AI Gateway configuration, and RAG implementation for LLMs, image generation, and embeddings.

Guides implementation of visual predictive checks for Bayesian model validation following Säilynoja et al. recommendations using ArviZ.

Enables Bayesian statistical analysis including probabilistic modeling, prior specification, posterior inference, and predictive checks.

Enables scientific visualization of 3D volume data, meshes, and vector fields with support for VTK and EXODUS formats.

Extracts Shiller PE financial data from web sources or databases for market analysis and research purposes.

Generates AI agent test evaluations including questions, answer verification, and XML formatting for MCP server usability testing.

Creates interactive statistical and scientific visualizations including scatter, box, violin, heatmaps, and network graphs using Plotly's Express and Graph Objects APIs.

Provides compact Plotly visualization patterns for quick plots with Express and detailed control via Graph Objects.

Configures and optimizes AI assistance tools (Copilot, custom agents) for AROG development, enabling efficient AI integration and agent management.

Ingests RFP opportunities from SAM.gov, eMMA, and RFPMart, enabling data source management and debugging.

Executes MCMC convergence diagnostics for Bayesian models using CmdStanPy and ArviZ analysis tools.

Provides guidelines for constructing Stan models incorporating ordinary differential equations (ODEs) for dynamic system modeling using modern Stan interfaces.

Provides session and context metrics to analyze knowledge injection effectiveness and optimize the knowledge system.

Analyzes CSV files to generate summary statistics and visual plots using Python's pandas library.

Provides YouTube video transcripts for text-based processing, including summarization and Q&A applications.

Provides fallback Markdown extraction from webpages via Crawl4AI when standard parsing fails.

Manages session lifecycles, extracts learnings from workflows, and queries knowledge graphs for context metrics.

Creates interactive data dashboards by analyzing raw data, generating visualizations, and saving reusable configurations for collaborative development.

Trims large AI tool outputs to free context space and maintain model efficiency within token limits.

Delivers executive-grade data analysis with pandas/polars, creating McKinsey-quality visualizations for dashboards, investor presentations, and SaaS metrics.

Processes PDFs and image-heavy documents using Gemini AI to extract text, tables, diagrams, and handwritten content from complex layouts.