Skill UI

A comprehensive toolkit designed to professionalize LLM prompt workflows. It moves prompts from ad-hoc drafts to measurable, production-ready assets through A/B testing against structured test cases, immutable version tracking, and governance guidelines. Essential for marketing teams needing verifiable quality and safety for AI-generated content.

Analyzes protein sequences to locate canonical N-glycosylation sequons, predict O-glycosylation hotspots, and mutate or design glycosylation sites for therapeutic proteins, vaccines, and biosimilar comparisons using curated glycoengineering resources.

Helps product managers diagnose whether AI workflows rely on indiscriminate context stuffing or deliberate context engineering, then apply bounded domains, retrieval, and Research→Plan→Reset→Implement practices to improve reliability and output quality.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

Guides product managers from discovery notes to an aligned PRD by orchestrating problem framing, user context, solution definition, and success criteria so engineering and stakeholders share a clear source of truth.

Build a hierarchical user story map that lays out activities, steps, and tasks to visualize journeys, align product/design/engineering, prioritize MVP work, and surface experience gaps before backlog planning.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

This guide offers a jargon-free, practical approach to functional programming using fp-ts in TypeScript. It focuses on the 80/20 principles, teaching core patterns like Pipe, Option, and Either. Learn how to handle missing values safely, manage errors explicitly, and chain operations cleanly, improving code readability without over-engineering.

A comprehensive, cross-functional diagnostic tool that assesses overall organizational health across eight critical dimensions, including Financial, Product, People, and Engineering. It uses traffic-light scoring and industry benchmarks to provide deep, actionable insights, making it ideal for board reviews, risk assessment, and monitoring company vitality.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.

This guide outlines best practices for creating high-quality, structured pull requests (PRs) using Git and GitHub CLI. It ensures that every PR description captures not only what changed, but also the 'why' and necessary context for reviewers, significantly improving the code review process and adherence to engineering standards.