Skill UI

Transforms complex raw product metrics into a clear, actionable health narrative. This skill analyzes key performance indicators (KPIs) across the entire user lifecycle—including acquisition, activation, engagement, and retention—against set OKRs and benchmarks. The output is a comprehensive report providing overall health status, key observations, hypothesized root causes for performance dips, and prioritized, actionable next steps with assigned owners.

This skill generates detailed, structured research protocols for various academic, clinical, or market research studies. It ensures all critical components—such as objectives, study design, participant criteria, methodology, data management, statistical analysis plan, and ethical considerations—are comprehensively documented, providing a robust framework for any scientific investigation.

A comprehensive framework for diagnosing user churn and improving product stickiness. Use this when analyzing key retention metrics (D1, D7, D30, DAU/MAU), investigating why users leave, or building targeted, actionable interventions. It guides the analysis from segmentation to identifying 'aha moments' and recommending specific product changes.

This skill analyzes comprehensive sprint delivery data, including ticket completion rates, carry-over analysis, and unplanned work. It generates a structured, data-grounded retrospective brief, transforming raw metrics into concrete discussion prompts (Start/Stop/Continue) and actionable, testable experiments for the next iteration. It helps teams focus on measurable solutions rather than subjective debates.

A comprehensive framework for creating defensible, bottom-up sales forecasts. This model guides users through building detailed pipeline stage models, conducting scenario analysis (Upside, Base, Downside), and logging key assumptions. Ideal for sales and revenue leaders who need a systematic, data-driven approach to projecting future revenue, moving beyond simple guesswork.

This skill generates a comprehensive, structured threat model for any given service or feature using the STRIDE framework. It guides users through identifying critical assets, defining trust boundaries, enumerating specific threats (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege), assessing risk, and proposing concrete mitigation controls. Ideal for security design reviews and documentation.

Conducts a comprehensive, scored audit of a brand's social media presence across multiple platforms. It assesses profile completeness, content quality, audience engagement, posting consistency, and competitive positioning. The output includes a detailed health score, platform-by-platform analysis, competitor gap analysis, and a prioritized, actionable strategy plan ready for immediate implementation by marketing teams.

This tool analyzes historical sprint velocity data to generate a comprehensive engineering team health report. It moves beyond simple charts by diagnosing underlying delivery patterns, identifying risks like chronic over-commitment, and assessing story point calibration. The report provides actionable recommendations for improving capacity planning and team predictability.

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 skill conducts deep, structured competitor analysis using Browserbase's advanced discovery and enrichment tools. It generates a comprehensive HTML report that includes market overviews, detailed per-competitor deep dives (covering marketing, social, news, and technical aspects), side-by-side feature/pricing matrices, and comparative mentions feeds. Ideal for market researchers, product managers, and strategists needing actionable competitive intelligence.

This skill implements the LLM Council concept, enabling multiple open-weight language models to collaborate on complex queries. The process follows a three-stage deliberation: 1) Parallel independent responses from selected models; 2) Peer-to-peer ranking of the responses; and 3) Final synthesis by a designated Chairman LLM. Powered by Fireworks AI, it ensures deep, multi-faceted analysis and consensus building for high-stakes decision support.

A comprehensive skill for detecting and analyzing malicious behavior in mobile applications (Android/iOS). It employs multi-layered security analysis, including static indicator checking (permissions, manifest), dynamic instrumentation (Frida), network traffic monitoring, and automated scanning (MobSF). Use this skill when investigating suspicious apps for data exfiltration, C2 communication, or unauthorized access.