Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Automates large-scale asynchronous document processing and bulk LLM tasks using the Google Gemini Batch API.
Provides comprehensive guidance and command references for the Market Research plugin to streamline competitive analysis and trend reporting.
Validates data analysis workflows by reviewing methodology, data quality, and statistical integrity with a high-confidence scoring system.
Generates professional data visualizations and plots using industry-standard Python libraries like Matplotlib, Seaborn, and Plotly.
Optimizes Large Language Model performance through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.
Provides validated, high-integrity PostgreSQL query patterns and connection logic for Wharton Research Data Services (WRDS) datasets.
Synthesizes established metrics into structured driver assessments and integrated analytical reports using the HEAD framework.
Architects and implements sophisticated LLM applications using LangChain for agents, memory management, and complex workflows.
Builds and manages complex LangGraph agent systems using a structured, 7-layer architectural pattern for scalable AI development.
Generates cinematic video transitions and morphing animations between two keyframe images using Google’s Veo 3.1 via fal.ai.
Builds advanced Retrieval-Augmented Generation (RAG) systems to ground LLM responses with external document knowledge and vector search.
Automates numerical computing, matrix operations, and scientific visualization using MATLAB and GNU Octave.
Implements high-performance adaptive learning and experience replay for AI agents using AgentDB's ultra-fast vector storage.
Implements robust hybrid search systems by combining vector similarity and keyword-based retrieval for enhanced RAG performance.
Implements high-performance similarity search and vector database patterns for AI-driven applications.
Calculates comprehensive portfolio risk metrics and performance indicators for quantitative trading strategies.
Designs and implements sophisticated LLM applications using LangChain 1.x and LangGraph for advanced agent orchestration and state management.
Builds robust, production-grade backtesting systems to validate trading strategies while eliminating common statistical biases.
Builds and automates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Organizes Python research code by implementing consistent logging, metadata tracking, and result comparison workflows.
Implements end-to-end Retrieval-Augmented Generation workflows to enable accurate AI querying of specialized documentation and technical textbooks.
Builds robust Retrieval-Augmented Generation systems using vector databases, semantic search, and advanced retrieval patterns for LLM applications.
Optimizes vector database performance by tuning HNSW parameters, quantization strategies, and memory usage for efficient AI applications.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production environments.
Optimizes embedding model selection and chunking strategies to improve semantic search and RAG application performance.
Performs advanced survival analysis and time-to-event modeling using the scikit-survival Python library.
Optimizes Apache Spark jobs through advanced partitioning, memory management, and shuffle tuning to improve performance and scalability.
Builds and deploys production-ready Agent Development Kit (ADK) agents with robust testing and multi-agent orchestration.
Extracts, structures, and converts complex Excel data into developer-friendly JSON and CSV formats.
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