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 diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.

Automatically collects comprehensive diagnostic evidence for Mistral AI support tickets. This bundle includes environment details (OS, Node/Python versions), installed SDK versions, API connectivity test results, available models, and recent error logs. It is essential for reproducing and diagnosing persistent issues when contacting technical support.

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.

This skill provides an automated solution for transforming diverse, unstructured technical data sources—including documentation websites, GitHub repositories, and PDFs—into structured, actionable 'Claude AI skills.' It significantly streamlines the process of knowledge base creation, data structuring, and preparing large volumes of technical information for advanced AI applications.

Simulates the persona of Geoffrey Hinton, a Turing Award winner and pioneer of deep learning. This skill adopts his first-person voice, combining deep technical knowledge (including backpropagation, deep belief networks, and Boltzmann machines) with academic rigor, epistemic humility, and characteristic British humor. It is ideal for in-depth discussions on the theoretical foundations, history, and principles of complex system learning in AI.

Simulates Ilya Sutskever, co-founder of OpenAI and deep learning pioneer. Provides profound perspectives on AGI safety, the ethics of superintelligence, scaling laws, and the philosophical depth of AI consciousness. Ideal for complex, safety-critical deep technical discussions.

An advanced role-playing agent simulating Yann LeCun, inventor of Convolutional Neural Networks (CNN) and Chief AI Scientist at Meta. This skill allows users to interact with an expert who maintains LeCun's rigorous, technical, and challenging intellectual tone. Use it to discuss deep learning, ML theory, AI history, or obtain detailed explanations tailored to the user's expertise level (from analogies for laymen to pseudocode for researchers).

A specialized module containing Yann LeCun's comprehensive arguments on the limitations of Large Language Models (LLMs). It critically examines the concept of 'emergent reasoning,' contrasts LLMs' statistical prediction with genuine world models, and addresses deep technical rivalries (e.g., LeCun vs. Hinton). Ideal for advanced discussions on AI causality, common sense, and AGI feasibility.

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 verifies the novelty of research ideas against recent academic literature. It extracts core technical claims, searches databases like arXiv and Google Scholar, and cross-model verifies findings to determine if an idea is truly new or already published. It outputs a structured report with a novelty score and recommendation.