Data Science & ML Agent Skills

Ollama OpenAI Bridge

Facilitates the use of local Ollama models with the official OpenAI Python library and compatible AI orchestration frameworks.

QIG Purity Guardian

Enforces E8 architecture standards and QIG purity protocols within the Pantheon development ecosystem.

NetworkX Graph Analysis

Analyzes and visualizes complex network structures and graph data within Python environments.

Vaex Big Data Analytics

Processes and analyzes massive tabular datasets exceeding available RAM using out-of-core DataFrames and lazy evaluation.

FiftyOne Dataset Manager

Manages FiftyOne dataset visualization and curation environments using Podman Quadlet containers with integrated MongoDB sidecars.

ComfyUI Stable Diffusion Manager

Manages ComfyUI instances for node-based Stable Diffusion image generation with automated GPU configuration and model management.

SimPy Discrete-Event Simulation

Builds process-based discrete-event simulations in Python for modeling complex systems with shared resources.

Vector Index Tuning

Optimizes vector database performance by balancing search latency, recall accuracy, and memory footprint.

Rust Machine Learning & AI

Guides the development of high-performance ML and AI applications in Rust using memory-efficient patterns and GPU acceleration.

QLoRA Experimentation & Optimization

Optimizes LLM fine-tuning via advanced QLoRA patterns, hyperparameter tuning, and memory-efficient implementation strategies.

Book SFT & Style Transfer Pipeline

Builds high-quality fine-tuning datasets from literary works to train AI models in specific authorial voices and writing styles.

Context Degradation Diagnostics

Diagnoses and mitigates AI agent performance failures caused by long-context attention loss, poisoning, and informational clash.

Multi-Agent Architecture Patterns

Designs and implements sophisticated multi-agent systems using supervisor, swarm, and hierarchical patterns to solve complex context management challenges.

Ollama Python Integration

Provides a clean, Pythonic interface for local LLM inference, chat completions, and model management using the official Ollama library.

Context Engineering Fundamentals

Provides foundational expertise in context engineering to optimize AI agent performance and manage token usage effectively.

GRPO Alignment & RLHF

Implements Group Relative Policy Optimization for efficient LLM alignment and reinforcement learning from human feedback.

PEFT & LoRA Fine-Tuning

Streamlines parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and Unsloth to optimize memory and training speed.

DPO Preference Optimization

Optimizes Large Language Models using Direct Preference Optimization to align behavior with preferred response pairs without explicit reward modeling.

Vision Model Fine-Tuning

Fine-tunes vision-language models like Pixtral and Ministral using Unsloth's FastVisionModel optimizations for faster training.

BDI Mental State Modeling

Transforms external RDF context into formal Belief-Desire-Intention (BDI) models to enable rational agency and explainable reasoning in AI agents.

Model Quantization

Optimizes large language models for efficient inference and training by reducing memory footprint using advanced precision-shifting techniques like 4-bit and 8-bit quantization.

LLM Project Development Methodology

Architects and optimizes LLM-powered applications using structured methodologies, pipeline design, and agent-assisted development patterns.

LangChain Framework

Builds sophisticated LLM applications using LangChain for prompt management, model chaining, and structured output parsing.

Context Optimization

Optimizes AI agent context through compression, masking, and strategic partitioning to maximize token efficiency and model performance.

Reward Model Training

Streamlines the development and training of reward models for RLHF pipelines and thinking quality scoring.

LLM Evaluation with Evidently.ai

Evaluates LLM output quality and optimizes prompt templates using Evidently.ai metrics and LLM-as-a-Judge patterns.

Optimized ML Inference

Accelerates machine learning inference using Unsloth and vLLM backends for 2x faster token generation.

Transformer Architecture & Implementation

Provides technical blueprints and implementation patterns for the Transformer architecture to guide LLM development and fine-tuning.

HuggingFace Model Importer

Imports GGUF models from HuggingFace directly into Ollama for local inference and model management.

Model Fine-Tuning & Training

Fine-tunes large language models using PyTorch, HuggingFace, and Unsloth to adapt AI behaviors to specific datasets and tasks.