Discover Agent Skills for data science & ml. Browse 61skills for Claude, ChatGPT & Codex.
Generates and formats standardized PyTorch docstrings following Sphinx and reStructuredText conventions.
Constructs advanced financial models including DCF analysis, sensitivity testing, and Monte Carlo simulations for professional investment valuation.
Builds comprehensive 3-5 year financial models with revenue projections, burn rate calculations, and scenario planning for early-stage startups.
Automates the creation, editing, and analysis of Excel spreadsheets with professional formatting, formula integrity, and financial modeling standards.
Transforms raw data and complex analytics into persuasive business narratives and structured executive presentations.
Implements efficient similarity search and vector database patterns for semantic retrieval and RAG systems.
Builds robust, bias-aware backtesting systems to validate quantitative trading strategies and produce reliable performance estimates.
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.
Orchestrates end-to-end MLOps pipelines from data preparation and model training through to production deployment and monitoring.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and systematic benchmarking.
Optimizes vector index performance for production environments by balancing latency, recall, and memory usage.
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.
Architects sophisticated LLM applications using LangChain's modular framework for agents, memory, and complex workflows.
Combines vector similarity and keyword-based search to improve retrieval accuracy and recall in RAG systems.
Optimizes Apache Spark jobs through advanced partitioning, memory management, and shuffle tuning strategies.
Optimizes LLM performance and reliability through advanced techniques like few-shot learning, chain-of-thought reasoning, and structured prompt templates.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking to ensure performance and quality.
Build and orchestrate production-grade MLOps pipelines from data preparation through model training, validation, and deployment.
Optimizes semantic search and RAG applications through intelligent model selection, chunking strategies, and embedding quality evaluation.
Builds Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases, semantic search, and advanced retrieval strategies.
Builds sophisticated LLM applications by architecting modular chains, autonomous agents, and robust memory management systems using the LangChain framework.
Builds robust, production-grade backtesting systems for trading strategies while mitigating common pitfalls like look-ahead and survivorship biases.
Optimizes Apache Spark workloads through advanced partitioning, memory management, and shuffle tuning strategies.
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.
Builds production-ready Apache Airflow pipelines using industry best practices for orchestration, testing, and error handling.
Builds production-grade AI-powered applications, agents, and chatbots using the Vercel AI SDK for TypeScript.
Simplifies the creation of data-centric LLM applications by providing patterns for RAG, document ingestion, and vector indexing.
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