Data Science & ML Agent Skills

LLM Performance Evaluation

Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.

Data Storytelling Analytics

Transforms raw business data into compelling narratives and persuasive executive presentations using proven storytelling frameworks.

Superpowers Lab

Provides a specialized laboratory environment for experimenting with and implementing advanced Claude capabilities.

Context Engineering Fundamentals

Provides foundational strategies for managing AI agent context windows, attention mechanics, and token usage to improve model performance.

Hybrid Search Implementation

Implements robust hybrid search systems by combining vector similarity and keyword-based retrieval for enhanced RAG performance.

Context Optimization

Maximizes LLM context efficiency and reduces token costs through strategic compaction, masking, and partitioning techniques.

Multi-Agent Architecture Patterns

Provides expert guidance for designing and implementing distributed multi-agent architectures to scale task complexity beyond single-context limits.

Memory Systems Design

Implements sophisticated memory architectures for AI agents to persist state, build knowledge graphs, and maintain long-term context.

Similarity Search Patterns

Implements high-performance similarity search and vector database patterns for AI-driven applications.

Quantitative Risk Metrics Calculation

Calculates comprehensive portfolio risk metrics and performance indicators for quantitative trading strategies.

LangChain & LangGraph Architecture

Designs and implements sophisticated LLM applications using LangChain 1.x and LangGraph for advanced agent orchestration and state management.

Quantitative Backtesting Frameworks

Builds robust, production-grade backtesting systems to validate trading strategies while eliminating common statistical biases.

MLOps Pipeline & Workflow Orchestration

Builds and automates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.

LLM Evaluation Framework

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.

PyTDC Therapeutics Data Commons

Facilitates drug discovery and therapeutic machine learning by providing AI-ready datasets, benchmarks, and molecular oracles.

OpenAlex Scientific Database

Queries and analyzes over 240 million scholarly works, authors, and institutions using the OpenAlex open-access database.

RAG Implementation Suite

Builds robust Retrieval-Augmented Generation systems using vector databases, semantic search, and advanced retrieval patterns for LLM applications.

Vector Index Tuning Specialist

Optimizes vector database performance by tuning HNSW parameters, quantization strategies, and memory usage for efficient AI applications.

STRING Protein Interaction Analysis

Accesses and analyzes protein-protein interaction networks and functional enrichment data using the STRING database API.

NetworkX Graph Analysis

Builds, analyzes, and visualizes complex networks and graph data structures using the Python NetworkX library.

Advanced Prompt Engineering Patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production environments.

PyLabRobot Lab Automation

Automates laboratory workflows and hardware control using a hardware-agnostic Python interface for liquid handlers and analytical equipment.

ChEMBL Bioactive Database

Queries the ChEMBL database for bioactive molecules, drug targets, and medicinal chemistry data to support drug discovery research.

GWAS Catalog Explorer

Queries the NHGRI-EBI GWAS Catalog to retrieve genetic variant associations, study metadata, and comprehensive summary statistics for genomic research.

Embedding Strategies for RAG

Optimizes embedding model selection and chunking strategies to improve semantic search and RAG application performance.

Scikit-bio Bioinformatics Toolkit

Analyzes biological data including sequences, phylogenetic trees, and microbial community diversity using the scikit-bio Python library.

ESM Protein Design & Modeling

Designs, analyzes, and generates protein sequences and structures using Evolutionary Scale Modeling (ESM3 and ESM C).

ToolUniverse Scientific Research Skill

Empowers AI agents to conduct scientific research by providing standardized access to over 600 bioinformatics, cheminformatics, and genomics tools.

AnnData Scientific Data Management

Manages annotated data matrices for single-cell genomics and large-scale biological datasets using the AnnData Python framework.

Trust RL Model Predictions

Optimizes trading strategy execution by removing redundant heuristic pattern filters that conflict with Reinforcement Learning model signals.