Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Generates and formats standardized PyTorch docstrings following Sphinx and reStructuredText conventions.
Analyzes ArXiv papers by fetching LaTeX source code and generating project-specific summaries for technical implementation.
Streamlines the addition of new elements, widgets, and cross-stack functionality to the Streamlit framework.
Constructs advanced financial models including DCF analysis, sensitivity testing, and Monte Carlo simulations for professional investment valuation.
Builds and orchestrates collaborative multi-agent AI teams using the CrewAI framework and Composio toolkits.
Builds comprehensive 3-5 year financial models with revenue projections, burn rate calculations, and scenario planning for early-stage startups.
Empowers developers to build, customize, and integrate autonomous AI research agents using the GPT Researcher framework.
Enables real-time, bidirectional voice AI capabilities in .NET applications using Azure AI and WebSocket communication.
Transforms complex data into actionable business intelligence through advanced analytics, KPI frameworks, and predictive modeling.
Automates the creation, editing, and analysis of Excel spreadsheets with professional formatting, formula integrity, and financial modeling standards.
Combines vector similarity and keyword-based search to improve retrieval accuracy and recall in RAG systems.
Optimizes vector index performance for production environments by balancing latency, recall, and memory usage.
Transforms raw data and complex analytics into persuasive business narratives and structured executive presentations.
Builds robust, bias-aware backtesting systems to validate quantitative trading strategies and produce reliable performance estimates.
Optimizes Apache Spark jobs through advanced partitioning, memory management, and shuffle tuning strategies.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and systematic benchmarking.
Calculates comprehensive portfolio risk metrics and performance indicators for quantitative finance and investment management.
Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning, chain-of-thought, and modular templates.
Implements efficient similarity search and vector database patterns for semantic retrieval and RAG systems.
Architects sophisticated LLM applications using LangChain's modular framework for agents, memory, and complex workflows.
Orchestrates end-to-end MLOps pipelines from data preparation and model training through to production deployment and monitoring.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in custom knowledge.
Optimizes embedding model selection and chunking strategies for semantic search and Retrieval-Augmented Generation (RAG) applications.
Optimizes semantic search and RAG applications through intelligent model selection, chunking strategies, and embedding quality evaluation.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking to ensure performance and quality.
Builds sophisticated LLM applications by architecting modular chains, autonomous agents, and robust memory management systems using the LangChain framework.
Build and orchestrate production-grade MLOps pipelines from data preparation through model training, validation, and deployment.
Implements search architectures that combine vector similarity and keyword matching to maximize retrieval precision and recall.
Implements efficient similarity search and vector database patterns for semantic search, RAG retrieval, and recommendation engines.
Optimizes Apache Spark workloads through advanced partitioning, memory management, and shuffle tuning strategies.
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