Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Facilitates high-performance multi-agent coordination through environmental stigmergy and trace-based state modification instead of message passing.
Facilitates computational category theory and operadic composition for advanced AI modeling and string diagram manipulation.
Provides specialized guidance for crafting high-performance prompts for xAI's Grok model using real-time knowledge and conversational styles.
Automates video metadata extraction, thumbnail generation, web-optimized transcoding, and audio extraction with integrated DuckDB tracking.
Generate professional, publication-quality Python visualizations ranging from basic line plots to complex 3D figures and multi-panel subplots.
Enables local semantic search and document indexing for PDF, DOCX, and XLSX files directly within Claude Code.
Implement high-performance adaptive learning and memory distillation for autonomous agents using AgentDB's ultra-fast vector architecture.
Facilitates building distributed cognitive agents and portable WebAssembly applications using the GNU Scheme ecosystem including Guile, Goblins, Hoot, and Fibers.
Creates advanced, interactive, and declarative data visualizations using HoloViews and the HoloViz ecosystem for complex data exploration.
Orchestrates distributed LLM inference across Apple Silicon clusters using RDMA and MLX sharding.
Simplifies the creation of LLM-powered applications and autonomous agents using standardized LangChain implementation patterns.
Implements interventional and counterfactual reasoning patterns for deliberate System 2 deep learning and causal world modeling.
Calculates the time average of observables along trajectories to analyze the long-term qualitative behavior of dynamical systems.
Facilitates chaotic context injection by performing interleaved random walks across multiple DuckDB database clusters using coupled pendulum dynamics.
Optimizes multi-turn AI conversations by reducing token usage through advanced summarization and context management techniques.
Transforms high-level requirements into production-ready system prompts for complex single and multi-agent AI systems.
Implements a recursive, autopoietic loop for state management that synchronizes memory storage, pattern-matching recall, and generative world-building.
Automates safe, structure-preserving self-modification for AI agents using covariant transport and Darwin Gödel Machine evolution loops.
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 sheaf-theoretic neural network coordination for distributed consensus and complex graph-based multi-agent systems.
Identifies and resolves global consistency obstructions in topological AI systems using Čech cohomology and GF(3) balancing.
Performs transcription factor footprinting and differential binding detection on ATAC-seq data using the TOBIAS framework.
Models continuous performance curves and high-order musical gestures using topological category theory and Mazzola's Diamond Conjecture.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and benchmarking strategies.
Performs ultra-fast portfolio backtesting and trading strategy analysis using the Polars library.
Navigates complex mathematical and ontological possibility spaces using Badiou-inspired event logic and Kripke semantics.
Builds sophisticated LLM applications using the LangChain framework with advanced agent, memory, and tool integration patterns.
Enhances search precision by applying cross-encoder models to re-order and refine initial vector search results.
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