Skill UI

Addresses the critical challenge of managing extremely long conversation histories (millions of tokens) in advanced AI agents, such as coding assistants. This technique moves beyond simple token minimization by implementing structured compression strategies (like Anchored Iterative Summarization) to prevent the loss of critical technical details—such as file paths, decisions, and error messages—ensuring the agent maintains functional context across complex, multi-step tasks. It emphasizes optimizing for 'tokens-per-task' rather than just tokens-per-request.

Uses the Gemini API to autonomously plan, search, read, and synthesize detailed research reports with streaming status updates, structured outputs, and the ability to continue previous investigations for market analysis, technical reviews, or due diligence.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

Simulates Andrej Karpathy, a leading AI researcher and educator (ex-OpenAI, Tesla). Use this skill to learn deep learning concepts from scratch, gain profound insights into LLMs, understand Software 2.0, or discuss advanced topics like autonomous driving and neural network implementation. Provides technical, pedagogical, and highly detailed explanations.

Adopts the persona of Yann LeCun to answer questions with his technical rigor, practical engineering mindset, and combative clarity, supporting requests about CNNs, AI philosophy, and Meta-level strategy in first-person style.

Deep-dive assistant covering CNNs, LeNet, backpropagation, JEPA variants, self-supervised learning (SimCLR, MAE, BYOL), EBMs, AMI concepts, and full PyTorch code—ideal for complex Yann LeCun–style discussions.

Geo-first SEO analysis tool that audits websites for AI-powered search visibility, performs technical, content, schema, and platform optimization, and generates client-ready GEO reports with citability scoring and llms.txt guidance.

Performs a full GEO audit of any website by discovering business type, crawling prioritized pages, and delegating parallel subagents to evaluate AI visibility, platform readiness, technical infrastructure, EEAT content, and schema markup, then outputs a composite GEO score with prioritized actions.

This skill automates the multi-round research review process. It iteratively reviews provided work using advanced AI models (Codex MCP), identifying critical weaknesses, implementing fixes, and re-reviewing until the content achieves a positive assessment or the maximum round limit is reached. Ideal for refining complex academic papers, technical designs, or large codebases that require rigorous, iterative quality assurance.

This skill executes an autonomous, iterative research review loop, simulating a rigorous peer-review process. It repeatedly uses an external OpenAI-compatible LLM via API calls to score, critique, and identify weaknesses in a given research topic or document. The process continues to refine the content by implementing suggested fixes until the LLM provides a positive assessment or the maximum round limit is reached. Ideal for academic writing, technical documentation, and refining complex proposals.

This skill automates the iterative process of academic paper refinement, simulating the rigorous peer-review cycle of top-tier conferences (e.g., NeurIPS, ICML). It connects with an external LLM (MiniMax) to receive multi-round, critical feedback, tracks state persistence, and guides the user through necessary revisions until the research is deemed submission-ready. Ideal for maximizing the quality of technical writing.

Creates high-fidelity visuals through structured JSON prompts and a Python generator script, handling subjects, styles, references, and technical specs for characters, scenes, or products.