Data Science & ML Claude 技能

RAG Implementation

Implements advanced Retrieval-Augmented Generation (RAG) workflows to build knowledge-grounded LLM applications and semantic search systems.

Financial Analysis & Modeling

Provides comprehensive financial frameworks for modeling, valuation, corporate finance decisions, and advanced statement analysis.

Triad Interleave Schedule Generator

Generates balanced, deterministic execution schedules by interleaving three color streams using GF(3) mathematical conservation.

Structural Rewilding (A-Life Framework)

Implements a homotopical framework for Artificial Life that treats organisms as morphisms and verifies structural changes at interaction time.

Claude Code Colab Customizer

Configures and personalizes your Claude Code Colab environment through interactive setup and automated notebook modification.

LangChain Architecture

Designs and implements sophisticated LLM applications using the LangChain framework for agents, memory management, and modular workflows.

Prompt Engineering Patterns

Implements advanced prompt engineering techniques to optimize LLM performance, reliability, and output controllability in production environments.

Geohash Coloring Skill

Implements hierarchical spatial indexing with deterministic GF(3) color derivation for geospatial analysis and visualization.

LLM Evaluation & Testing

Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and LLM-as-judge patterns.

LLM Auto-Update

Automatically synchronizes the latest LLM model specifications, pricing, and API documentation to ensure optimal architecture decisions.

Compression Progress Curiosity Engine

Implements Schmidhuber's compression progress theory to provide intrinsic curiosity rewards for autonomous AI learning and exploration.

ReasoningBank Agent Learning

Implements adaptive learning and high-speed memory patterns for self-improving Claude Code agents using AgentDB.

RAG Implementation

Builds production-ready Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases and semantic search.

SQL Data Analyst

Performs advanced data analysis and business intelligence using specialized SQL patterns for statistical and exploratory insights.

Workers AI Expert

Deploys and optimizes serverless AI models, embedding generation, and RAG architectures directly on Cloudflare’s edge network.

Demographic Predictor

Predicts age, gender, and ethnicity from person data and images to enrich datasets and customer profiles.

Marimo Development Server

Deploys and manages reactive Python notebooks with hot-reloading capabilities for interactive development.

Marimo Notebook Inspector

Inspects Marimo notebook execution results and HTML snapshots to debug errors and verify cell outputs.

Marimo Notebook Assistant

Streamlines the creation and management of reactive marimo notebooks for interactive data science and analytics workflows.

Bayesian Meta-Analysis Models

Provides comprehensive Bayesian meta-analysis templates using Stan and JAGS for advanced biostatistical evidence synthesis.

RLOO Policy Optimization

Implements Reinforcement Learning with Leave-One-Out estimation to stabilize model training and optimize policy performance.

ComfyUI Manager

Deploys and manages ComfyUI instances for node-based Stable Diffusion image generation with GPU acceleration and model lifecycle support.

Ollama Local LLM Manager

Manages local Ollama inference servers using Podman Quadlet to provide GPU-accelerated LLM capabilities.

LangChain Framework Support

Simplifies building LLM-powered applications by providing standardized abstractions for prompt engineering, model orchestration, and structured output parsing.

LLM Supervised Fine-Tuning (SFT)

Streamlines the supervised fine-tuning of Large Language Models using Unsloth for optimized performance and reasoning model development.

Quantization & Model Optimization

Optimizes large language models for efficient inference and training using various precision types and memory estimation techniques.

Direct Preference Optimization (DPO)

Aligns AI models with human preferences using Direct Preference Optimization to improve reasoning and response quality without explicit reward models.

Ollama Python Library

Provides a clean, Pythonic interface for interacting with Ollama to handle text generation, chat completions, and model management.

Parameter-Efficient Fine-Tuning (PEFT)

Optimizes LLM fine-tuning using LoRA, QLoRA, and Unsloth to drastically reduce memory requirements and accelerate training cycles.

LLM Evaluation & Optimization

Evaluates LLM outputs and optimizes prompts using Evidently.ai metrics and LLM-as-a-judge patterns.