Search Skills

Comprehensive AB test analysis tool providing statistical significance testing, user segmentation, and visual reporting for product, marketing, and feature optimization decisions.

Predicts customer lifetime value (LTV) using RFM feature engineering and regression modeling on transaction data for e-commerce and retail.

Performs multi-touch attribution analysis using Markov chains and Shapley values to optimize marketing channel effectiveness, budget allocation, and customer journey insights with Chinese language support.

Analyzes user conversion funnels to calculate step-by-step rates, visualize user journeys, and identify optimization opportunities for improved conversion.

Automates RFM customer segmentation analysis with K-means clustering and visualization for e-commerce data.

Analyzes text content using NLP and LLM to extract sentiment, topics, keywords, and insights from social media, articles, and reviews.

Analyzes user retention and churn using survival analysis, cohort analysis, and machine learning to predict churn risk and optimize retention strategies.

Provides ML-driven growth analysis with uplift modeling, user segmentation, and ROI optimization for marketing and product teams.

Automates data exploration, visualization, and report generation with intelligent diagnostics for medical, financial, and e-commerce analysis.

Tool for implementing and evaluating recommendation algorithms including collaborative filtering and matrix factorization, with visualization and report generation for personalized recommendations.

Performs regression analysis and predictive modeling using linear regression, decision trees, and random forests for continuous target variables with automated feature engineering and visualization.

Fetches up-to-date LangGraph documentation to assist developers with accurate guidance for building graph-based applications.

Provides testing patterns for PHPUnit and Playwright E2E tests to validate ActivityPub features and debug test failures.

Guides users through personalized setup of COG by creating profile, interests, and watchlist files to tailor the tool to their workflow.

Automates accessibility and visual design reviews for UI components to ensure WCAG compliance and design quality.

Removes redundant comments, defensive checks, and inconsistent style from AI-generated code in the current branch to streamline development.

Refactors recently modified code to enhance readability and consistency while preserving original functionality.

Automates web browser interactions including navigation, form filling, screenshots, and data extraction for testing and information gathering.

Generates a complete favicon set from an image and automatically updates HTML to include them for web projects.

Automates identification and removal of dead code, unused dependencies, and exports to maintain clean, efficient codebases.

Refactors CLAUDE.md files to apply progressive disclosure, enhancing documentation readability and organization.

Deploys optimized AI/ML inference workloads on GKE using Google's Inference Quickstart for model discovery and manifest-based deployment.

Provides workflows for scaling GKE workloads using HPA and VPA with configuration best practices.

Guides users in creating Google Kubernetes Engine (GKE) clusters with predefined templates for Standard, Autopilot, and GPU/AI workloads.