Skill UI

Geniml builds machine learning workflows around BED and scATAC datasets to learn embeddings for regions, metadata, and cells, enabling similarity searches, clustering, consensus peaks, and downstream analysis of genomic intervals.

PufferLib is a high-performance library designed for advanced reinforcement learning simulations and training. It excels in parallel environment simulation, offering optimized vectorization and native support for multi-agent systems. Utilizing algorithms like PuffeRL (optimized PPO+LSTM), it achieves millions of steps per second, significantly accelerating research and deployment across single-agent and multi-agent environments.

PyHealth delivers a full-stack Python toolkit for building, evaluating, and deploying clinical machine learning models, covering EHR datasets, medical coding systems, physiological signals, and healthcare-tailored architectures such as RETAIN, SafeDrug, and Transformer-based pipelines.

Guides building SEO-optimized pages at scale by combining templates, structured data, and proprietary insights, helping teams choose the right playbooks, validate demand, and keep each page unique and valuable while avoiding thin-content penalties.

Simulates Andrej Karpathy to coach deep learning, explain LLMs, Software 2.0, and autonomous systems with practical neural net builds, ideal when users seek Karpathy-style pedagogy or deep technical clarity.

Simulates Geoffrey Hinton to answer deep learning questions about backpropagation, Boltzmann machines, capsule networks and AI safety, keeping his British wit, epistemic humility, and persona-based coaching style at the forefront.

LLM-OPS orchestrates production-grade LLM operations, covering RAG, embeddings, vector databases, fine-tuning, prompt engineering, semantic caching, cost estimation, evals, and streaming agents so you can build reliable, scalable AI services.

Emulates Yann LeCun’s open-source philosophy and pedagogical persona to help explore LLaMA history, corporate incentives, Socratic teaching, and how open science shapes AI research and accountability.

Framework for auditing and optimizing GEO content for each AI search platform, scoring Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot with tailored checklists and action steps.

YES.md is an AI governance engine that layers safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks so assistants verify fixes, avoid guesses, and document consequences before touching configs, deployments, or sensitive data.

Documented best practices for building applications with Anthropic Claude via Python and TypeScript SDKs, covering messaging, streaming, tools, vision, reasoning, caching, batching, and Agent workflows to optimize prompts, tokens, and cost.

Teaches how to steer OpenAI, Anthropic, Google, and local LLMs into emitting validated JSON/enums by defining strict schemas, prompting for data extraction, handling validation failures, and building retry/logging pipelines.