Skill UI

A comprehensive guide for auditing and polishing original long-form manuscripts intended for submission to high-level Pattern Recognition and AI journals. It details mandatory components, formatting standards, anti-plagiarism checks, and ethical guidelines, ensuring the paper meets rigorous peer-review standards for publication.

A skill that models AI educator Baoyu's approach to creating accessible AI content, systematic prompt engineering teaching (Level 1-10), tool reviews, open-source sharing, and daily knowledge updates. Ideal for writing tutorials, translating tech articles, and building AI knowledge bases without hype or anxiety.

This skill helps classify AI systems under the EU AI Act (Regulation (EU) 2024/1689) to determine compliance obligations. It guides users through the decision tree from prohibited practices to high-risk categories, GPAI models, and role-specific responsibilities. Useful for assessing risk levels, mapping provider/deployer duties, and building compliance roadmaps. Includes DACH-specific regulatory considerations.

Classifies whether a technology qualifies as an AI system under the EU AI Act Art. 3(1) and determines its risk tier: prohibited, high-risk, GPAI with systemic risk, limited, or minimal risk. Guides users through the scope exclusions, AI definition test, and risk classification workflow.

A comprehensive companion tool for the foundational Deep Learning textbook. It indexes 20 chapters and provides a crucial delta layer, updating core concepts from 2016 to 2026 (e.g., Transformers, Diffusion Models). The tool features four deterministic utilities: a reading-path planner, a training-failure diagnostic, a capacity planner, and a parameter/FLOP calculator. Ideal for students and practitioners who need to study the theory, understand its modern relevance, and translate academic knowledge into actionable engineering decisions.

Provides a deep, structured assessment of whether an AI system meets the criteria for 'high-risk' classification under Article 6 of the EU AI Act. Grounded in the Commission's draft guidelines (Annex I & III), this tool guides users through complex compliance checks, including product safety routes and quasi-provider traps. Essential for making consequential risk verdicts and ensuring legal compliance.

A comprehensive guide for generating and manipulating images using Gemini models within a Flutter/Dart environment powered by Firebase AI. This skill covers best practices for multi-modal API calls, interpreting interleaved text and image responses, troubleshooting common setup issues (billing, App Check), and optimizing user inputs by handling downscaling and aspect ratios.

A comprehensive jurisdictional reference file designed for AI assistants, ensuring accurate legal, regulatory, and compliance research specific to Gibraltar. It corrects common errors made by AI models applying English law to Gibraltar’s distinct legal framework, covering constitutional law, court structure, financial services (FSC), and public record OSINT sources.

Full reference guide for Ouroboros commands, skills, agents, and workflows. Covers Socratic interviews, seed generation, evaluation, evolutionary loops, and troubleshooting for power users.

A systematic post-hoc audit skill for verifying factual, legal, and interpretive claims generated by AI. Designed for professional reliance—such as legal briefs or published advice—it classifies claims by type (empirical, legal, interpretive) and salience, searches for corroborating evidence using tiered, domain-appropriate sources (e.g., NICE guidelines, primary legislation), and scores the trustworthiness of the output before professional use.

This comprehensive guide outlines the principles, architecture, and best practices for developing specialized, modular skills that extend large language model capabilities. Learn how to integrate domain expertise, multi-step workflows, API tool integrations, and structured resources while managing the context window efficiently.

This tool guides the analysis of work sessions by detecting user feedback (corrections, successes, edge cases). It evaluates these signals against quality criteria (Complete, Precise, Atomic, Stable) to propose structured improvements, update skill documentation (SKILL.md), and maintain a changelog.