Discover Agent Skills for data science & ml. Browse 61skills for Claude, ChatGPT & Codex.
Generates visual phase portraits and vector fields for 2D dynamical systems to analyze state space behavior.
Formalizes Martin Buber's relational philosophy using category theory and homotopy type theory to enhance AI social intelligence.
Analyzes and optimizes neural network training dynamics using Stochastic Differential Equations and Fokker-Planck convergence metrics.
Implements a self-adaptive machine learning retraining framework for automated trading signals and market regime detection.
Extracts behavioral patterns and interaction sequences to train cognitive surrogate systems and predictive models.
Implements sheaf-theoretic neural network coordination for distributed consensus and complex graph-based multi-agent systems.
Exploits knowledge differentials across domains using propagator-based networks and deterministic parallel synthesis.
Optimizes interaction sequences using information theory to maximize learning efficiency and minimize surprise.
Identifies and resolves global consistency obstructions in topological AI systems using Čech cohomology and GF(3) balancing.
Models continuous performance curves and high-order musical gestures using topological category theory and Mazzola's Diamond Conjecture.
Navigates complex mathematical and ontological possibility spaces using Badiou-inspired event logic and Kripke semantics.
Performs ultra-fast portfolio backtesting and trading strategy analysis using the Polars library.
Facilitates the development, simulation, and control of 3D-printed humanoid robots for reinforcement learning research.
Evaluates research rigor and scientific claims by assessing methodology, statistical validity, and potential biases using standardized frameworks.
Orchestrates playful multi-agent exploration anchored by Leonid Levin's algorithmic complexity and optimality guarantees.
Orchestrates end-to-end MLOps pipelines from data preparation through production deployment and monitoring.
Architects sophisticated LLM applications using LangChain patterns for autonomous agents, stateful memory management, and modular chains.
Optimizes LLM performance and reliability through advanced prompting techniques like chain-of-thought and few-shot learning.
Builds Retrieval-Augmented Generation (RAG) systems to ground LLM applications with vector databases and semantic search capabilities.
Implements rigorous evaluation strategies for LLM applications using automated metrics, human-in-the-loop feedback, and advanced benchmarking.
Synthesizes Patrick Kenny's active inference framework with K-Scale's JAX/MuJoCo robotics stack for advanced predictive coding in robot locomotion.
Provides a unified framework for humanoid robot development, reinforcement learning training, and sim-to-real deployment.
Automates programmable chemical synthesis by treating chemical procedures as executable XDL code on modular robotic hardware.
Builds and simulates a cost-efficient, wobbling robot that composes nonstandard musical scales through duck-like vocalizations.
Trains humanoid locomotion and manipulation policies using JAX-accelerated MuJoCo simulations and advanced RL algorithms.
Provides hardware specifications, MuJoCo MJCF models, and deployment workflows for the K-Scale flagship humanoid robot platform.
Standardizes robotics datasets and deploys edge-optimized vision-language-action models for embodied AI applications.
Converts URDF robot descriptions into MJCF format for high-performance MuJoCo and MJX physics simulations.
Performs objective technical analysis on weekly price charts to identify trends, support levels, and probabilistic price scenarios.
Deploys trained Reinforcement Learning (RL) policies to real robots using high-performance Rust and ONNX Runtime.
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