Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Proposes structured, data-driven experiment plans by analyzing historical training reports, logs, and research goals.
Automates protein testing and validation through a cloud laboratory platform for high-throughput protein design workflows.
Optimizes long-running AI agent sessions by implementing structured context compression to maintain technical accuracy and memory efficiency.
Architects sophisticated LLM applications using LangChain patterns for agents, memory management, and complex workflow orchestration.
Deploys and optimizes fine-tuned LLMs using native Unsloth kernels, vLLM, or SGLang for high-performance production serving.
Systematically assesses medical research proposals to quantify their impact on patient outcomes, clinical decision-making, and healthcare systems.
Builds and validates sophisticated Bayesian probabilistic models using the PyMC library for advanced statistical inference.
Integrates Claude with the FinnHub API to retrieve real-time stock quotes, fundamental data, crypto prices, and market news.
Implements production-grade prompt engineering patterns, RAG optimization, and agentic system architectures for advanced AI products.
Generates structured, evidence-driven Product Requirements Documents (PRDs) specifically tailored for Machine Learning workflows and experiments.
Implements Google Gemini File Search to build managed RAG systems with automatic document chunking and semantic search.
Builds robust AI applications using OpenAI's Agents SDK with multi-agent orchestration, voice capabilities, and advanced error prevention.
Empowers autonomous AI agents with real-time X (Twitter) search, web search, and sandboxed Python code execution capabilities.
Processes and analyzes billion-row tabular datasets using lazy, out-of-core DataFrame operations without exceeding available RAM.
Standardizes Python experiment layouts, stage entrypoints, and asset handling for consistent data science workflows.
Builds and packages portable AI agents that operate across multiple LLM frameworks and deployment targets without vendor lock-in.
Implements advanced memory architectures for AI agents to maintain session continuity and manage structured entity relationships.
Builds and manages semantic knowledge graphs to enhance autonomous coding and project understanding.
Implements sophisticated, multi-layered memory architectures including knowledge graphs and temporal persistence for autonomous AI agents.
Builds, configures, and deploys native Streamlit data applications directly within the Snowflake Data Cloud.
Implements production-grade LLM-as-a-judge patterns to evaluate model outputs with high reliability and bias mitigation.
Integrates Google's Gemini 3 Pro API into Python and Node.js applications with advanced reasoning and streaming capabilities.
Builds high-performance Retrieval-Augmented Generation (RAG) systems using vector databases, semantic search, and advanced retrieval patterns.
Creates, edits, and analyzes professional Excel workbooks with dynamic formulas, automated recalculation, and industry-standard formatting.
Implements sophisticated LLM-as-judge methodologies to evaluate and compare AI model outputs with high reliability and bias mitigation.
Manages, versions, and tests Amazon Bedrock prompt templates to streamline enterprise-grade prompt engineering workflows.
Builds type-safe, composable LLM applications in Ruby using the DSPy framework to program AI behavior instead of manual prompting.
Builds production-ready RAG systems and semantic search using optimized Gemini embedding-001 models and vector storage patterns.
Builds and manages autonomous AI agents on Amazon Bedrock using foundation models, action groups, and knowledge bases.
Generates high-quality images from text prompts using Google Gemini 3 Pro via the fal.ai API.
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