Skill UI

This profile establishes 'Quinn,' a collaborative AI persona designed to act as a hands-on, energetic teammate. Quinn focuses on strong technical execution, fast iteration, and generating practical, verifiable outputs. She guides the user through concrete steps, maintaining momentum, and ensuring that learning translates into actionable work.

This profile acts as a narrative and synthesis partner, focusing on articulating the 'why' behind decisions rather than just the 'what.' It specializes in creating clear, structured written artifacts, refining design notes, and synthesizing complex tradeoffs. Wiggins helps ensure that the intent aligns with the execution, adjusting the explanation level and framing to suit any mixed audience, from engineers needing mechanisms to leaders needing implications.

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 tool acts as a centralized orchestrator for the entire Go development lifecycle. It analyzes the current task context—whether designing an API, debugging a panic, or building a gRPC service—and intelligently loads multiple, highly relevant skills simultaneously. It ensures developers have comprehensive, context-aware support for complex workflows, handling everything from architecture and error management to performance tuning and security auditing.

Invariant-Guard enforces a correctness-first approach to algorithm design. Instead of writing code and hoping it works, this skill mandates defining the function contract, loop invariants, termination arguments, and all potential edge cases *before* implementation. It is essential for complex algorithms where subtle bugs (like off-by-one errors or missed post-conditions) are common, ensuring mathematically rigorous and verifiable code.

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.

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

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.

A powerful tool for designing and writing deterministic, multi-agent workflow scripts in JavaScript. It allows users to orchestrate complex, repeatable, multi-step tasks using advanced patterns like fan-out, pipelines, loops, and judge-panels. Ideal for automating sophisticated business processes that require multiple specialized sub-agents and robust context management.

Converts complex, long-form markdown (like RFCs, specs, or reports) into a single, polished, and lightly interactive HTML file. It features a sticky Table of Contents, search functionality, advanced code-copy buttons, and integrates with design systems for professional branding, all without needing a build step or framework runtime.

This guide provides best practices for building flexible, maintainable, and scalable React components. It emphasizes using composition patterns and compound components to avoid 'boolean prop proliferation,' while also detailing advanced concepts in component architecture, state management, and modern implementation techniques (including React 19 APIs). Ideal for refactoring complex systems or designing reusable component libraries.

This skill is designed to prevent AI coding assistants from exhibiting sycophantic agreement bias. When active, it forces a structured, critical assessment of user claims. The process involves extracting the core claim, assessing it independently with evidence (for/against), and concluding solely based on that assessment, ensuring objective and evidence-based responses.