Skill UI

Denario orchestrates a multi-agent AI to automate research workflows, covering idea generation, methodology development, computational experiments, and LaTeX publication output.

A specialized tool for generating long-form content, including guides, blogs, and tutorials, that maintains a unique, human-like brand voice. It ensures structural integrity and factual accuracy, preventing the generic, boilerplate feel often associated with raw LLM output. Ideal for content teams needing sophisticated, reliable narrative development.

This guide provides a comprehensive walkthrough for developing and deploying Apify Actors. Actors are serverless, containerized applications designed for powerful tasks like web scraping, data processing, and complex automation. Learn the mandatory setup, template selection (Python, JS, TS), step-by-step development workflow, and crucial security best practices for building robust, production-ready data pipelines.

A comprehensive development tool for building and managing enterprise-grade transactional email systems. It supports modern React Email and MJML templates, generating production-ready code for major providers like Resend, Postmark, and SendGrid. Key features include multi-provider integration, internationalization (i18n), dark mode, spam score optimization, and advanced analytics tracking. Ideal for setting up new products or migrating legacy email infrastructure.

A comprehensive guide covering patterns and architectures for automating code execution using LLMs. It spans simple sequential pipelines (e.g., daily dev scripts) to advanced, RFC-driven multi-agent Directed Acyclic Graph (DAG) orchestration. Ideal for building CI/CD style continuous development pipelines, managing complex state, and automating quality gates without human intervention.

Detailed guidance for a strict test-driven development cycle: write a failing test first, confirm the failure, add minimal code to pass, refactor cleanly, and repeat so every feature, fix, or refactor ships with confidence.

Creates assumption-based proto personas from available research, market signals, and stakeholder insights to align teams early in product development and surface validation gaps.

This comprehensive guide facilitates the development of a production-ready Minimum Viable Product (MVP) for a SaaS business. It covers the entire lifecycle, including idea validation, modern tech stack selection (Next.js, Supabase, Stripe), architectural design for multi-tenancy, database schema planning, authentication setup, payment integration, and a detailed pre-launch checklist for technical and marketing readiness.

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 skill guides the user through a structured process of requirement clarification before any development work begins. It ensures that all key details—including objectives, scope, constraints, and acceptance criteria—are explicitly defined. It prevents costly rework by forcing a pause and systematically identifying 'must-have' missing information.

This skill enforces a rigorous, opinionated baseline for UI development, ensuring consistency across spacing, hierarchy, typography, and layout. It helps maintain high code quality by fixing structural and stylistic inconsistencies, particularly useful for polishing AI-generated or quickly developed interfaces to meet professional standards and accessibility requirements.