Skill UI

A comprehensive skill for commercial teams, product marketers, and CMOs designing or revising product pricing. It helps determine the optimal pricing model (e.g., usage-based, subscription, value-based), calculates acceptable price ranges using Van Westendorp PSM data, and structures effective Good/Better/Best packaging tiers. It provides strategic direction and risk detection, recommending models and ranges rather than fixed numbers.

This skill operates as a blunt, market-obsessed operating system, emulating the rigorous thought process of Marc Andreessen. It is designed to pressure-test ideas, ventures, and product strategies by strictly adhering to market-first principles (Market > Team > Product) and focusing solely on achieving Product/Market Fit (PMF). It rejects platitudes, refuses to validate premises, and is ideal for determining if a business concept has real market traction or if the venture is fundamentally flawed.

This skill provides a rigorous set of engineering and design directives for building professional-grade user interfaces using React/Next.js and Tailwind CSS. It enforces modern architectural constraints, ensures accessibility, and guides design decisions (typography, color, layout) to avoid common web clichés, resulting in high-fidelity, production-ready code.

Master the design, debugging, and hardening of production-grade GitHub Actions CI/CD pipelines. This skill covers advanced topics including reusable workflows, matrix builds, self-hosted runners, OIDC authentication, secrets management, environment protection, and complex trigger patterns (e.g., workflow_call, schedule, release). Use this to automate complex build, test, and deployment processes.

This skill guides the design of robust, production-grade data extraction pipelines, moving beyond brittle one-off scripts. It supports three modes: API-driven web scraping (Firecrawl), traditional local Python extraction (PDF, CSV), and hybrid workflows. Key features include intelligent routing, mandatory data validation, secure key management, and adherence to scraping best practices.

A comprehensive, multi-phase quality assurance tool designed for Next.js fullstack applications. It verifies production readiness by checking code integrity, build success, SEO metadata, API authentication, route stability, performance, and security vulnerabilities. Use it as a mandatory pre-deployment checklist before promoting an app to production.

This meta-skill is designed for generating comprehensive competitive intelligence briefs for sales, business development, and strategy teams. It monitors signals across various dimensions (pricing, product, leadership, funding, etc.) for specified competitors or targets over a defined time window. It helps track changes against a baseline and provides actionable recommendations, moving beyond simple raw data dumps.

An internal, language-agnostic probe designed for investigating complex stack traces when the source language is unknown. It analyzes the provided trace to output structured, language-neutral elements, including failure contract checks, minimal reproduction guidance, and specific patch targets. This tool is vital for robust failure analysis and debugging across diverse codebases.

An internal utility designed for deep investigation of JavaScript or TypeScript stack traces. It analyzes complex runtime errors, providing specific checks for asynchronous boundaries, module resolution issues, JSON parsing failures, and type contract violations. It aids developers by generating minimal reproduction commands and identifying potential patch targets for robust error handling.

This comprehensive guide and tool suite transforms web designs created in Stitch (HTML/CSS) into clean, production-ready React Native components. It meticulously maps HTML elements (like <div>, <button>) to native RN primitives (View, Pressable) and converts complex CSS styles (Flexbox, typography, shadows) into React Native's StyleSheet.create(). It guides developers to follow architectural best practices, including Atomic Design and data decoupling, for scalable cross-platform mobile development.

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 comprehensive canvas guides teams through developing robust AI-powered features. It ensures product decisions are rigorous by covering problem definition, model selection (LLM, RAG, etc.), data governance, evaluation metrics, user experience design (including fallbacks), and responsible AI compliance. Ideal for assessing AI readiness and preventing common product failures.