Skill UI

Builds AI-powered paid media creative across Meta, Google, TikTok, YouTube, and LinkedIn by combining native platform tools and generative models to automate asset creation, fatigue management, and performance testing while driving ROAS.

This tool assesses creative fatigue across all active advertising channels. It scores creatives based on multiple decline signals (CTR, CPM, engagement, frequency) to predict when performance will drop. It generates actionable refresh briefs, recommends specific changes, and creates structured A/B test plans to maximize ad spend and maintain campaign performance throughout the entire creative lifecycle.

Design a comprehensive and systematic framework for optimizing ad creative performance. This tool guides users through defining testable variables (headlines, CTAs, imagery), designing A/B and multivariate tests, calculating required sample sizes, and establishing optimal iteration cadences across major advertising platforms (Meta, Google, etc.) for continuous marketing improvement.

Use this tool to pre-screen ad creatives, emails, and landing page copy against synthetic audience panels. Predict response rates, sentiment, and potential objections before launching live campaigns. By scoring variants on criteria like resonance and credibility, it helps marketing teams identify top-performing messages and avoid wasting ad spend on underperforming content.

A powerful skill designed to analyze raw ad campaign performance data from platforms like Google, Meta, and LinkedIn. It goes beyond simple metrics reporting, providing deep diagnosis by identifying underperforming ads, pinpointing budget waste, and calculating the true Customer Acquisition Cost (CAC). It generates concrete, actionable recommendations for what to cut, scale, or test next, optimizing your ad spend and maximizing ROAS.

This tool provides a statistically rigorous framework for analyzing a user's private LinkedIn post performance data. It moves beyond simple averages, utilizing median/MAD and permutation testing to determine if observed patterns are genuine or statistical noise. It guides users through three steps: descriptive analysis, pattern mining, and sizing future A/B experiments, ensuring hypotheses are testable and avoiding over-conclusion. Works only with your own exported data.