Skill UI

This skill processes raw, unstructured materials (e.g., conversations, reports, controversies) into highly structured, reusable casebook entries. It focuses on identifying core facts, defining mechanism chains, and mapping responsibility links while ensuring rigorous data anonymization. It is designed for deep knowledge management and pattern recognition, helping users build a reliable, reusable knowledge base for future professional analysis, rather than generating immediate advice.

This skill guides the design of statistically rigorous A/B tests for optimizing product features, UI/UX changes, onboarding flows, and pricing models. It generates a complete test plan, including formulating hypotheses, calculating required sample sizes, estimating test duration, defining primary and guardrail metrics, and providing a detailed guide on interpreting results (ship, iterate, or reject).

This skill generates a comprehensive budget variance analysis report. It takes actual and budgeted figures, identifies variances exceeding a materiality threshold, and provides structured commentary. The output includes variance summaries, detailed root cause analysis (distinguishing between permanent and timing issues), recommended management actions, and revised full-year financial forecasts, suitable for C-suite presentations or board packs.

This skill extracts precise, pixel-level data from various chart types (bar, line, scatter, pie) and graphs presented in images. It generates a comprehensive structured data table, including confidence levels, axis details, and assumptions, making the raw data ready for spreadsheets or further analysis. Ideal for digitizing charts, transcribing graph values, or turning visual data into actionable formats.

This skill generates a comprehensive, structured churn analysis, moving far beyond simple headline rates. It meticulously separates avoidable vs. unavoidable customer losses, identifies high-risk segments (by tier, cohort, etc.), calculates critical metrics like Net Revenue Retention (NRR), and pinpoints actionable early warning signals. The output culminates in prioritized, data-driven intervention recommendations to improve customer retention.

A structured framework for performing in-depth cohort analysis. It helps measure user retention rates, estimate Lifetime Value (LTV), and segment users based on behavior over time. Use this when analyzing growth metrics, identifying churn risks, or presenting product health to leadership.

This skill converts vague business questions or monitoring needs into a complete, structured, and implementation-ready dashboard specification. It guides users to define key metrics (KPIs), chart types, axes, filters, and optimal layout, providing data engineers and BI developers with all necessary details to build the dashboard without ambiguity.

This skill systematically researches real-time public sentiment and user opinions on any topic by querying Reddit, X/Twitter, and the broader web. It bypasses curated SEO content to provide a structured report detailing consensus points, recurring pain points, genuine praise, and signal confidence, offering actionable insights into current market sentiment.

This skill enables querying Delta Lake tables and raw files stored in OneLake using DuckDB. It supports two primary modes: local execution via CLI/Python for ad-hoc analysis, and in-notebook execution within Fabric, combining DuckDB's speed with notebook context. Ideal for data exploration, schema discovery, and data quality validation across the Fabric ecosystem.

This comprehensive tool suite provides high-fidelity parsers for key Windows artifacts, including MFT, Registry hives, Prefetch files, and ShellBags. Designed for digital forensics and incident response, it converts raw system evidence into structured CSV/JSON formats. The output is optimized for feeding into Timeline Explorer, enabling analysts to build comprehensive, correlated timelines and establish a clear chain of events.

KAPE is a powerful, Windows-native triage tool used for Digital Forensics and Incident Response (DFIR). It executes in two phases: 'Targets' collect raw forensic artifacts (like registry hives, event logs, and prefetch files) from live systems, while 'Modules' parse this collected data into normalized, analyst-ready CSV/JSON formats. It enables fast, defensible, and scalable artifact collection, especially when full disk imaging is impractical.

A spectral vector search tool that enhances nearest-neighbor search with graph Laplacian features, capturing both semantic similarity and structural roles in embedding spaces.