Data Science & ML Agent Skills

ReasoningBank Intelligence

Implements adaptive learning and pattern recognition systems to enable AI agents to optimize strategies and improve through experience.

ReasoningBank Intelligence

Implements adaptive learning systems to enable AI agents to recognize patterns, optimize strategies, and continuously improve through experience.

Pairwise Meta-Analysis Methodology

Provides rigorous methodological guidance for conducting and reviewing pairwise meta-analyses following Cochrane and PRISMA standards.

Swarm Orchestration

Orchestrates multi-agent swarms using dynamic topologies and automated task distribution for complex, parallel AI workflows.

Flow Nexus Swarm

Deploys and orchestrates cloud-based AI agent swarms with event-driven workflow automation and intelligent coordination.

PocketFlow

Orchestrates complex LLM applications using graph-based abstractions, agentic design patterns, and modular task decomposition.

AgentDB Memory Patterns

Implements persistent, high-performance memory and learning patterns for AI agents to maintain context across sessions.

Polars ML Feature Buffers

Manages high-performance data buffers for real-time feature engineering and time-series analysis using Polars.

Scientific Schematics

Generates publication-quality scientific diagrams and architectural schematics using AI-driven iterative refinement.

Gioia Methodology

Constructs and validates hierarchical data structures for qualitative research, transforming grounded participant language into abstract theoretical dimensions.

System Resource Detector & Advisor

Detects hardware resources and provides strategic architectural recommendations for computationally intensive scientific tasks.

Tidy ITC Workflow

Implements tidy modeling principles for reproducible and standardized indirect treatment comparison (ITC) analyses in R.

Fantasy Football Analytics & ML

Develops predictive player projection models using specialized feature engineering and sports-specific machine learning validation techniques.

Analysis Orchestration

Optimizes AI-assisted qualitative research through model selection guidance, cost estimation, and document processing strategies.

AI Multimodal

Processes and generates multimedia content including audio, video, images, and documents using the Google Gemini API.

PyTorch Lightning Assistant

Streamlines deep learning development by organizing PyTorch code into scalable, boilerplate-free Lightning modules and automated training workflows.

MAIC Methodology Guide

Provides expert methodological guidance and best practices for conducting Matching-Adjusted Indirect Comparisons (MAIC) in health economics and outcomes research.

RAG Systems

Implements robust Retrieval-Augmented Generation (RAG) pipelines including document ingestion, hybrid search, reranking, and intelligent query routing.

Google ADK & CopilotKit Agent Builder

Builds full-stack AI agents by integrating Google ADK backends with CopilotKit-powered React frontends and Gemini models.

Innovation Game Miner

Develops, optimizes, and submits high-performance competitive algorithms for Boolean Satisfiability, Vehicle Routing, and Knapsack challenges on The Innovation Game platform.

PyMC Bayesian Modeling

Builds, fits, and validates sophisticated Bayesian models using PyMC's probabilistic programming interface.

Workers AI

Provides expert guidance on implementing AI models, generating embeddings, and building Retrieval Augmented Generation (RAG) systems using Cloudflare’s serverless AI platform.

Network Meta-Analysis (NMA) Methodology

Provides comprehensive methodological guidance and code implementations for conducting rigorous network meta-analyses following international standards.

TRL Training on Hugging Face

Trains and fine-tunes language models using Transformer Reinforcement Learning methods on fully managed Hugging Face cloud GPU infrastructure.

MetaNova NOVA Miner

Facilitates the setup, optimization, and management of molecular mining operations on Bittensor Subnet 68 for decentralized drug discovery.

Mean-Variance Optimization

Calculates optimal portfolio weights and identifies the tangency portfolio to maximize the Sharpe ratio based on user-provided asset data.

Model Coordination

Coordinates multiple AI models in parallel to perform comparative analysis, aggregate decision-making, and track performance across diverse LLM providers.

Polars Data Processing

Provides expert guidance for high-performance data manipulation and ETL pipeline optimization using the Polars library in Python.

Medallion Schema Reference

Automates schema validation and business logic extraction to ensure accuracy when generating PySpark ETL transformation code.

Prompt Engineering

Designs, optimizes, and troubleshoots large language model prompts using research-backed techniques and structured design patterns.