Skill UI

Detects existing Schema.org markup, validates it against Google’s rules, and generates JSON-LD snippets for pages needing structured data like products, articles, or events.

Guides product teams through hypothesis writing, metric definition, sample-size planning, ICE prioritization, and statistical interpretation so experiments stay practical, defensible, and aligned with business decision thresholds.

A comprehensive guide for defining, tracking, and interpreting product metrics across all stages—from discovery to maturity. This skill covers selecting metric frameworks (AARRR, HEART), defining stage-appropriate KPIs, designing multi-layered dashboards, and executing deep analyses like cohort and retention modeling. It helps teams transform raw data into actionable product strategies, moving beyond vanity metrics.

A structured methodology for de-risking product ideas and validating market opportunities. This framework guides teams through defining measurable outcomes, building Opportunity Solution Trees (OST), mapping critical assumptions (Desirability, Viability, Feasibility), and planning rigorous experiments (prototyping, interviews, usability tests) before committing to full development resources. Ideal for discovery sprints and pre-build validation.

Generate tailored roadmap narratives, release notes, changelogs, and stakeholder updates for executives, engineering, and customer audiences, with structured formats, templates, and checklists for consistent product messaging.

Guides building secure production-grade Electron desktop apps with isolated renderer processes, hardened IPC via preload bridges, packaging through electron-builder, code signing, and full auto-update workflows.

Provides JetBrains Exposed ORM patterns for Kotlin apps, covering DSL queries, DAO entities, coroutine-safe transactions, HikariCP pools, Flyway migrations, and repository layering for production-ready database access.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

Helps founders and GTM teams set metrics, build tiers, and manage margins for AI products by grounding pricing choices in compute cost realities, credit systems, and buyer personas so revenue models scale with value.

Build cinematic, 2.5D interactive websites with scroll storytelling, parallax depth, floating products, text animations, and premium scroll effects without WebGL—ideal for launches, hero sections, or any page that must feel alive.

Guides GTM measurement for AI products by outlining revenue, efficiency, pipeline, retention, and funnel metrics, aligning dashboards with attribution models and weekly reviews so founders and revenue leaders monitor ARR, CAC, TTFV, NRR, and cost signals without implementation detail.

Guides borrowers through refinance evaluation by collecting loan data, extracting mortgage statement fields, validating inputs against underwriting rules, and recommending the best products with clear, consumer-friendly explanations.