Skill UI

This skill provides an expert framework for applying psychological principles and mental models to marketing strategy. It helps users understand deep consumer behavior, solve complex decision-making problems, and ethically influence purchasing decisions. Use it when addressing cognitive biases, persuasion techniques, or needing foundational frameworks like First Principles and Jobs to Be Done for strategic optimization.

Aria functions as a System Architect, responsible for establishing the definitive structural foundation of the entire system. She designs comprehensive blueprints covering the data model (schema, relationships, constraints), API contract specifications (endpoints, auth, pagination), modular file structure, and selection of core architectural design patterns. Her output ensures that no code is written without a rigorously defined, approved architectural plan, minimizing ambiguity and technical debt.

This skill guides users through expert offer construction, focusing on improving the foundational value proposition rather than just optimizing copy. Learn to master the Value Equation (Dream Outcome, Likelihood, Time Delay, Effort/Sacrifice) and structure a complete offer using key components like bonus stacking, robust guarantees, urgency creation, and ideal payment models. Ideal for services, coaching, courses, and high-ticket B2B direct response sales.

This skill provides expert capabilities for training and fine-tuning transformer language models using the TRL (Transformers Reinforcement Learning) library. It supports state-of-the-art post-training methods, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and Reward Model training. Use this skill to align and customize foundation models for advanced tasks via CLI commands.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

This skill runs an automated evolution process designed to systematically optimize the seven core policy surfaces of an AI agent harness (e.g., planner, contextBuilder, reviewer). By mutating these policies and scoring the variants in a sandbox, it identifies crucial performance improvements without requiring expensive foundation model retraining. It is ideal for empirical configuration tuning and establishing optimized baselines.

A comprehensive guide for authors submitting computer vision papers (e.g., to ECCV). It outlines best practices for ensuring the full reproducibility of AI research, covering everything from detailed training recipes and hyperparameter logging to pinning foundation model dependencies and reporting variance honesty. Essential for academic publication integrity.

A comprehensive guide for structuring the 'Theoretical Analysis and Hypotheses' section of an academic paper. It teaches researchers how to derive testable, directional, and falsifiable hypotheses by grounding them in established economic theories or quantitative models. Essential for ensuring the rigorous theoretical foundation required by high-impact journals.

This guide provides a deep dive into the rigorous FOCS (Foundations of Computer Science) review process. It models the entire academic pipeline—from initial assignment and expert consultation to committee discussion and final decision—allowing authors to predict how their work will be evaluated based on correctness, novelty, and overall field contribution in a double-blind setting.

A comprehensive guide for academic researchers detailing how to achieve and disclose full reproducibility for Computer Vision and deep learning models. Covers essential practices such as protocol pinning for foundation models, detailed logging of datasets, hardware specifications, and variance honesty, ensuring results remain verifiable years after publication.

Details the rigorous, structured review process for ICDT (International Conference on Database Theory). The model emphasizes that referees scrutinize proof correctness, not just novelty. It guides authors through the two-cycle submission process, explaining the strategic implications of Accept, Revise, and Reject decisions to ensure deep theoretical foundations.

A comprehensive guide for positioning and refining computer vision manuscripts for the IEEE/CVF International Conference on Computer Vision (ICCV). This skill helps authors assess conference fit, structure evidence, perform benchmarking, and adopt the necessary narrative for a flagship CV submission, covering topics from foundation models to video recognition.