Skill UI

This tool simulates market demand using synthetic audience panels grounded in real CRM data to estimate customers' willingness-to-pay (WTP). It calculates optimal price points, acceptable price ranges, and analyzes price sensitivity across different buyer segments. Use this command to guide pricing strategies before product launch, tier adjustments, or competitive positioning evaluation, providing actionable intelligence without expensive formal research.

A structured tool for running A/B tests on marketing content variations. It allows users to compare different approaches—such as headline styles, CTA phrasing, or overall content strategies—by logging variants and evaluating them against technical quality scores (e.g., hallucination, readability). The system provides a comprehensive analysis, identifying a statistically grounded winner and offering actionable recommendations for content improvement.

Generates comprehensive quality intelligence reports by analyzing historical evaluation scores. This tool tracks content performance over time, identifies improving or declining content types, detects significant quality regressions against baselines, and provides prioritized, actionable recommendations for improving brand quality across various content dimensions.

This skill facilitates the persistent storage of critical brand knowledge, including campaign learnings, brand guidelines, and competitive intelligence, into a specialized memory layer (such as a vector database). It automatically indexes content, deduplicates entries via hashing, and applies rich metadata tagging, ensuring that valuable institutional knowledge is preserved and retrievable through natural language search across different sessions.

This tool batches syncs all accumulated session learnings, campaign insights, and performance data to a persistent memory layer. It ensures valuable knowledge is automatically retained across multiple work sessions without manual saving. It supports incremental syncing based on checkpoints, making it fast and safe for continuous knowledge capture.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

This guide provides comprehensive instructions for packaging multiagent system (MARL) and game theory evidence for academic submissions, particularly for conferences like AAMAS. It emphasizes that the artifact must be inspectable, containing not just a trained model, but the game definition, environment code, opponent sets, and interaction protocols, ensuring rigorous reproducibility and reviewability.

This guide provides a detailed walkthrough of the AAMAS peer review process. It covers everything from initial submissions and technical evaluation (correctness, significance, rigor) to the rebuttal phase, area-chair synthesis, and final decisions. Use this when planning academic submissions or understanding the rigorous criteria for publishing multi-agent systems research.

Provides comprehensive guidelines for authors submitting papers to Acta Automatica Sinica (AAS). It details how to organize and present anonymous, reproducible code, data, and experimental artifacts to enhance the credibility and rigor of research in control, automation, and pattern recognition, supporting the double-blind review process.

A comprehensive guide for packaging and submitting evidence packages (artifacts) for NLP research papers, such as code, datasets, prompts, and model outputs. It details requirements for reproducibility, adhering to the Responsible NLP checklist, ensuring proper anonymization, and preparing for post-acceptance public release in academic settings like ACL.

A comprehensive guide for researchers designing and auditing NLP experiments for top-tier conferences. It covers establishing defensible baselines, ensuring evaluation breadth, performing rigorous statistical tests (significance, variance), controlling for data contamination, and structuring detailed functional error analysis. Essential reading for publishing reliable AI research.

A comprehensive guide for authors targeting the ACM Conference on Intelligent User Interfaces (IUI). This skill helps evaluate manuscript fit, refine scope, understand evidence requirements, and strategically position papers related to AI-infused interaction, adaptive systems, and user modeling for top-tier HCI venues.