Data Science & ML Claude 스킬

Protein Design Quality Control

Evaluates and filters protein designs using research-backed metrics for binding, expression, and structural integrity.

ESM2 Protein Analysis

Analyzes protein sequences using the ESM2 language model to generate high-quality embeddings and evaluate biological plausibility.

Foldseek Structure Search

Performs fast and sensitive 3D protein structure similarity searches across global databases like PDB and AlphaFold DB.

Protein Binding Characterization

Provides expert guidance for Surface Plasmon Resonance (SPR) and Biolayer Interferometry (BLI) experimental design and troubleshooting.

Chai-1 Structure Prediction

Predicts multi-modal molecular structures including protein complexes, ligands, and nucleic acids using the Chai-1 foundation model.

RCSB PDB Structure Manager

Fetches, analyzes, and prepares protein structures from the RCSB Protein Data Bank for computational biology workflows.

UniProt Protein Database Access

Retrieves comprehensive protein sequences, functional annotations, and structural cross-references directly from the UniProt database.

Agentic Workflow Discovery

Automates the discovery and implementation of autonomous AI agent patterns, including task decomposition, tool use, and memory management.

TensorFlow Data Pipeline Architect

Builds high-performance, scalable data pipelines using the tf.data API to maximize GPU and TPU utilization.

TensorFlow Model Deployment

Deploys, optimizes, and converts TensorFlow models for production environments including mobile, edge, and cloud.

Multimodal RAG Patterns

Implements advanced retrieval-augmented generation systems for images, text, and hybrid document content using CLIP and Voyage AI.

Golden Dataset Management

Protects and maintains high-quality AI/ML test datasets through robust backup, restoration, and data integrity validation.

TensorFlow Neural Networks

Builds, trains, and optimizes complex neural networks using TensorFlow's Keras API and custom low-level implementations.

Agent SDK Builder

Guides users through an interactive interview and research process to build custom AI agents using the OpenHands SDK.

Jupyter Notebooks

Manages, executes, and converts Jupyter notebooks to streamline data science and machine learning workflows.

Alternative Agent Frameworks

Provides implementation patterns and comparison guides for multi-agent orchestration frameworks including CrewAI, OpenAI Agents SDK, and Microsoft Agent Framework.

LLM Output Evaluation & Quality Assurance

Implements advanced LLM-as-judge patterns and RAGAS metrics to evaluate AI output quality and detect hallucinations.

Agent Loops & Reasoning

Implements autonomous agentic workflows and reasoning patterns for complex, multi-step LLM tasks.

Audio Language Models

Implements real-time voice agents, high-accuracy transcription, and expressive text-to-speech using native speech-to-speech models.

Agentic RAG Patterns

Builds self-correcting RAG systems using LangGraph for adaptive retrieval, document grading, and web search fallbacks.

LangGraph Agent Supervisor

Orchestrates multi-agent workflows using a central supervisor pattern to intelligently route tasks between specialized worker agents.

Query Decomposition

Decomposes complex, multi-concept queries into independent sub-topics to improve RAG retrieval accuracy and coverage.

LangGraph Supervisor Pattern

Coordinates complex multi-agent workflows using a centralized supervisor-worker orchestration pattern.

Agentic RAG Patterns

Implements advanced Self-RAG and Corrective-RAG architectures for self-correcting AI retrieval systems.

Prompt Engineering Suite

Optimizes AI application performance through production-ready prompt engineering patterns, versioning, and automated tuning.

Agentic Workflows & Loops

Implements autonomous reasoning patterns like ReAct and Plan-and-Execute to enable LLMs to solve complex, multi-step tasks.

Prompt Engineering Suite

Optimizes LLM performance through production-ready patterns including Chain-of-Thought, dynamic few-shot learning, and automated prompt tuning.

Ollama Local Inference

Enables high-performance local LLM execution using Ollama to eliminate API costs and enhance data privacy during development.

Prompt Caching Optimizer

Optimizes LLM API costs and performance by implementing provider-native prompt caching for Claude and OpenAI.

Semantic Redis Caching

Optimizes LLM application performance and costs using vector-based similarity caching with Redis.