Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Models continuous performance curves and high-order musical gestures using topological category theory and Mazzola's Diamond Conjecture.
Performs ultra-fast portfolio backtesting and trading strategy analysis using the Polars library.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and benchmarking strategies.
Builds sophisticated LLM applications using the LangChain framework with advanced agent, memory, and tool integration patterns.
Navigates complex mathematical and ontological possibility spaces using Badiou-inspired event logic and Kripke semantics.
Enhances search precision by applying cross-encoder models to re-order and refine initial vector search results.
Orchestrates multi-agent swarms using advanced coordination patterns and dynamic topologies for complex, parallel task execution.
Implements high-performance adaptive learning and experience replay for self-improving AI agents using AgentDB.
Automates high-fidelity PDF and document conversion by selecting the optimal parsing method for academic and qualitative research data.
Ensures methodological rigor in qualitative research by enforcing isolation rules and phase-aware guidance through specialized AI agents.
Orchestrates systematic document coding, progress tracking, and audit trail generation for qualitative research projects.
Converts audio recordings and PDF documents into structured markdown for qualitative research and analysis.
Facilitates deep analytical reasoning and navigates theoretical paradoxes in qualitative research using systematic thinking and dialectical wisdom.
Orchestrates end-to-end MLOps pipelines from data preparation through production deployment and monitoring.
Converts audio recordings, PDFs, and diverse document formats into structured markdown for qualitative research and AI-assisted analysis.
Initializes qualitative research environments with structured epistemic foundations and standardized folder hierarchies.
Optimizes model selection, API cost estimation, and batch processing strategies for AI-assisted qualitative document analysis.
Architects sophisticated LLM applications using LangChain patterns for autonomous agents, stateful memory management, and modular chains.
Manages the foundational state, logging, and workflow enforcement for AI-assisted qualitative research projects.
Facilitates the development, simulation, and control of 3D-printed humanoid robots for reinforcement learning research.
Optimizes LLM performance and reliability through advanced prompting techniques like chain-of-thought and few-shot learning.
Orchestrates cost-effective qualitative research workflows using Kimi K2.5 models and advanced context caching strategies.
Composes complex 3D environments and terrains for robotic simulation and reinforcement learning training.
Bridges the gap between robotic simulations and real-world deployment using maximum entropy reinforcement learning and information-theoretic alignment.
Deploys trained Reinforcement Learning (RL) policies to real robots using high-performance Rust and ONNX Runtime.
Builds Retrieval-Augmented Generation (RAG) systems to ground LLM applications with vector databases and semantic search capabilities.
Facilitates reflexive qualitative data analysis through dialogical questioning and multi-stage visible reasoning.
Facilitates structured, step-by-step thinking for complex analytical decisions and theoretical framework construction using the Sequential Thinking MCP.
Provides hardware specifications, MuJoCo MJCF models, and deployment workflows for the K-Scale flagship humanoid robot platform.
Provides a unified framework for humanoid robot development, reinforcement learning training, and sim-to-real deployment.
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