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.
Automates advanced natural language processing tasks including text moderation, sentiment analysis, and language detection via the Tisane API.
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.
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.
Calculates comprehensive portfolio risk metrics and performance indicators for quantitative finance and investment management.
Architects sophisticated LLM applications using LangChain's modular framework for agents, memory, and complex workflows.
Builds robust, bias-aware backtesting systems to validate quantitative trading strategies and produce reliable performance estimates.
Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning, chain-of-thought, and modular templates.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and systematic benchmarking.
Transforms raw data and complex analytics into persuasive business narratives and structured executive presentations.
Optimizes Apache Spark jobs through advanced partitioning, memory management, and shuffle tuning strategies.
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