Skill UI

This skill transforms vague, unclear, or half-formed business opportunities and brief descriptions into highly structured, actionable problem statements. It guides the user through a three-stage process: reframing the core questions, rigorously defining in-scope and out-of-scope boundaries, and designing a minimum viable research plan. It ensures that ambiguous inputs are converted into clear, executable steps, allowing the user to respond with confidence rather than asking for more clarification meetings.

This skill generates a comprehensive discovery call brief designed for sales professionals. Users input prospect details and product info, and the AI outputs a structured guide including market research summaries, hypotheses about pain points, deep-dive questioning frameworks, and clear success criteria. It ensures every sales conversation is strategic, context-driven, and leads to a defined next step.

A comprehensive framework for structuring user interviews, customer discovery, or Jobs-to-be-Done (JTBD) research. This guide provides structured steps for screener creation, deep behavioral exploration (focusing on past actions, not future predictions), and systematic synthesis/pattern detection, ensuring the gathering of genuine, actionable insights.

This skill guides the planning and structuring of Figma prototypes specifically for usability testing and user research. It helps define the precise scope (in/out scope), specifies every interaction detail (trigger, elements, animations), builds comprehensive flow diagrams, and generates detailed test task scripts. It ensures the prototype is the minimum viable version needed to answer the research questions, preventing scope creep and over-engineering.

This skill guides product teams beyond listing features by applying the Jobs-to-be-Done (JTBD) framework. It maps customer needs across functional, emotional, and social dimensions, allowing teams to write precise job stories. Use this to conduct deep user research, identify core pain points, and define product opportunities based on user outcomes rather than mere outputs.

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 synthesizes user signals gathered from diverse sources—such as interviews, support tickets, NPS verbatims, app reviews, and sales calls—into a single, weighted, and comprehensive insight brief. It moves beyond surface-level feature requests to surface the core underlying user needs, identifies contradictions (divergence), and assigns confidence ratings based on source diversity and weight. Ideal for product managers and UX researchers needing to reconcile complex, mixed data sets.

This skill automates the NotebookLM AI research tool using browser automation via the Claude Chrome extension. It allows users to programmatically create new notebooks, add multiple sources (URLs, documents, text), and trigger complex output generations, such as mindmaps, audio overviews, and briefing documents, without manual clicking. Ideal for researchers and builders managing high volumes of source material.

This skill synthesizes raw user interview transcripts into structured research findings. It identifies core themes, pain points, workflow insights, and actionable product implications. Use this when analyzing qualitative research notes to derive evidence-based product recommendations and next steps for development teams.

This skill analyzes raw user research data—including transcripts, survey results, and feedback—and synthesizes it into a structured, actionable report. It identifies key themes, quantifies prevalence, pinpoints critical pain points, prioritizes feature requests, and provides strategic, evidence-backed recommendations for product improvement and development.

This skill provides comprehensive access to the Hugging Face Hub ecosystem via MCP server tools. Users can search for state-of-the-art models, datasets, and research papers, retrieve detailed repository information, fetch documentation, run compute jobs on GPU/CPU, and interact with Gradio Spaces as fully functional AI tools. Ideal for ML development, research, and application prototyping.

This skill provides deep, cross-border financial analysis and valuation metrics for stocks across multiple markets (e.g., US, HK). Users can access detailed income statements, balance sheets, cash flow data, analyze business segments, compare valuation multiples (PE, PB), run sophisticated DCF models, and perform value screening. It is an essential tool for fundamental research, investment modeling, and corporate analysis.