3. Data, Science & AI

Merges and deduplicates Banksy notebooks with associated source code for efficient data science project management.

Provides a complete mirror of RAPIDS Single-Cell GPU documentation for data scientists analyzing single-cell genomics data on GPU-accelerated platforms.

Enables semantic search implementation with vector embeddings using model2vec-rs and sqlite-vec for embedding generation, vector queries, and hybrid search.

Manages enterprise context and session state with token budget optimization for efficient AI application performance.

Generates professional images using Google's Gemini 3 Pro AI model for creative visual content.

Provides tools for SQLite database operations including full-text search (FTS5), vector indexing (sqlite-vec), and schema management for data querying and debugging.

Toolkit for spatial transcriptomics data analysis featuring precise file name-based categorization and comprehensive documentation.

Automates embedding model setup for semantic search using model2vec-rs, resolving build and download failures.

Comprehensive tool for scvi-tools, enabling deep probabilistic modeling for single-cell omics data analysis including integration, batch correction, and differential expression.

Automates weekly stock analysis by fetching end-of-week prices, calculating returns and momentum, and merging with fundamental data from Rice Data Portal.

Comprehensive collection of best practices for single-cell data analysis, including 407 HTML documentation files and 3 project guides.

Applies functional equivalents of meditation techniques (Vipassana, Samatha, TM, Zen) to AI models with interpretability logging to reduce confabulation and enhance coherence.

Automates monthly stock analysis by fetching end-of-month prices, calculating returns and momentum, and merging with fundamental data from Rice Data Portal.

Comprehensive multimodal data analysis toolkit with full documentation covering APIs, tutorials, IO guides, and core functionality.

Strict marker-based JSON annotation tool with color highlighting, overlapping span merging, and anti-hallucination features using Supabase.

Expert guidance on building machine learning models for fantasy football player projections, including feature engineering and model selection.

Extracts articles from theatlantic.com via web scraping based on user requests for data collection.

Automates RAG system design patterns including chunking, vector database usage, embedding management, and retrieval optimization.

Analyzes single-cell RNA-seq data using Seurat v5 on R data files (.rds/.rdata).

Enhances marker application via SFT/RL fine-tuning with LeanDeep 4.0, processing markers from Supabase/ZIP for context-aware bottom-up logic.

Generates Jupyter notebooks for fantasy football analytics adhering to project standards for structure, DuckDB connections, and visualization.

Integrates production-ready RAG chatbots into documentation sites using OpenAI, Qdrant, and Neon Postgres, covering full-stack backend and frontend implementation.

Defines token-efficient AI agent and workflow structures via YAML-based TOON format, inspired by BMAD Method.

Toolkit for cell-cell communication analysis in biological datasets, featuring comprehensive documentation and file-based categorization.