Data Science & ML Claude 技能

Sheaf Laplacian Coordination

Implements sheaf-theoretic neural network coordination for distributed consensus and complex graph-based multi-agent systems.

Agent-o-rama Pattern Learner

Extracts behavioral patterns and interaction sequences to train cognitive surrogate systems and predictive models.

Academic Peer Review

Provides rigorous, PhD-level evaluations of research manuscripts and proposals to enhance academic quality and impact.

Hi-C Loop Calling

Identifies and calls chromatin loops from Hi-C data files in .mcool, .cool, or .hic formats for genomic visualization and analysis.

Times Square Integration

Integrates the Times Square notebook execution system into web applications using established patterns for data fetching, real-time updates, and URL management.

Flow Nexus Swarm Orchestrator

Deploys and manages cloud-based AI agent swarms using event-driven workflow automation and intelligent coordination.

Langevin Dynamics Learning Analysis

Analyzes and optimizes neural network training dynamics using Stochastic Differential Equations and Fokker-Planck convergence metrics.

Swarm Orchestration

Orchestrates multi-agent AI swarms for parallel task execution and dynamic coordination using the agentic-flow framework.

COSMIC Cancer Database Access

Accesses and retrieves extensive cancer genomics data from the COSMIC database for bioinformatics and precision oncology research.

Buberian Relational Logic

Formalizes Martin Buber's relational philosophy using category theory and homotopy type theory to enhance AI social intelligence.

Phase Portrait Generator

Generates visual phase portraits and vector fields for 2D dynamical systems to analyze state space behavior.

Covariant Skill Evolution

Automates safe, structure-preserving self-modification for AI agents using covariant transport and Darwin Gödel Machine evolution loops.

AgentDB Vector Search

Implements high-performance semantic vector search and intelligent document retrieval for RAG systems using AgentDB.

MQL5 Python Workspace

Automates the translation of MetaTrader 5 (MQL5) indicators into validated Python implementations for algorithmic trading.

MLflow Python Integration

Automates experiment tracking and backtest logging using the MLflow Python API and QuantStats metrics.

Research Synthesis

Synthesizes complex findings from multiple sources into coherent, actionable conclusions with uncertainty quantification.

Ihara Zeta Graph Analysis

Analyzes graph topology and connectivity using the Ihara zeta function and non-backtracking spectral analysis.

ReasoningBank Intelligence

Implements adaptive learning systems for AI agents to recognize patterns, optimize strategies, and improve autonomously over time.

Algorithmic Trading Execution Patterns

Optimizes trade execution using advanced algorithms like TWAP, VWAP, and Iceberg orders to minimize market impact and slippage.

AgentDB Memory Patterns

Implements high-performance persistent memory and reinforcement learning patterns for AI agents using AgentDB and ReasoningBank.

Autopoietic Memory & Worlding

Implements a recursive, autopoietic loop for state management that synchronizes memory storage, pattern-matching recall, and generative world-building.

Julia Gay Color System

Generates deterministic, scientifically-consistent colors and parallel-invariant fingerprints for Julia-based data visualizations.

Geospatial Visualization with GeoViews

Simplifies the creation of interactive maps and geographic data visualizations using GeoViews and GeoPandas.

Knowledge Base Builder

Creates and optimizes elizaOS knowledge bases using RAG, smart chunking strategies, and semantic search integration.

Condensed Analytic Stacks

Bridges Scholze-Clausen condensed mathematics and analytic stacks with sheaf neural networks for advanced topological computation.

Schwab Market Data Snapshot

Synchronizes and caches comprehensive Charles Schwab market data, including account status, real-time quotes, and option chains for trading agents.

Research & Professional Analysis

Provides structured methodologies and frameworks for market research, competitor analysis, and professional data synthesis.

LangGraph Python Expert

Architects and implements sophisticated, stateful multi-agent LLM applications using LangGraph and Python.

Prompt Engineering Standards

Optimizes LLM performance and context efficiency using Anthropic-inspired prompt engineering and signal-to-noise optimization principles.

Google ADK Python

Builds, evaluates, and deploys sophisticated AI agents and multi-agent workflows using Google’s open-source Agent Development Kit.