Skill UI

This skill analyzes an existing codebase using domain context and architectural decisions to identify architectural friction and "deepening opportunities." It proposes refactored improvements to increase module depth, enhancing testability, improving maintainer locality, and making the codebase more navigable for AI tools. It focuses on transforming shallow modules into robust, deeply leveraged components.

This skill serves as a comprehensive entry point for initiating new development work. It systematically analyzes project metadata (specs, roadmaps) to determine the optimal work decomposition path (new spec, existing extension, or simple fix). By conducting deep context loading and guiding structured dialogue, it refines vague ideas into clearly defined, actionable scope boundaries. Finally, it proposes multiple concrete technical approaches, complete with trade-offs and estimates, ensuring the resulting project scope is well-defined and ready for implementation.

This skill orchestrates the entire development lifecycle for approved software tasks. It operates in autonomous or manual modes, rigorously applying Test-Driven Development (TDD). It automatically gathers all necessary context (specs, requirements, design, and historical learnings), validates the repository state, and dispatches specialized sub-agents to implement tasks one by one, ensuring high code quality, adherence to design, and process traceability.

This comprehensive tool analyzes a detailed specification by synthesizing data from multiple source files (JSON, Markdown, etc.). It tracks phase completion, identifies task progress, maps boundary contexts (upstream/downstream dependencies), and proactively highlights necessary revalidation points or blockers, providing a single, unified report of the spec's completeness and current development state.

This skill provides comprehensive guidance on correctly configuring Model Context Protocol (MCP) servers within Claude Code. It details the critical concepts of configuration scope (user vs. project) and outlines best practices to prevent context pollution across different coding sessions. Users learn how to safely add, remove, and clean up MCP settings using specific JSON files, ensuring a stable and efficient development environment.

A tool designed for persistent context management within AI coding sessions. Memory Kit solves the problem of context loss that occurs during compaction or session restarts by saving, loading, updating, and sharing critical session state to a dedicated `MEMORY.md` file. Key functions include creating snapshots of goals and decisions, syncing context across team members via Git, and auditing/pruning stale entries, ensuring continuous workflow and knowledge retention.

Performs rigorous, zero-context verification of all quantitative claims, comparisons, and scope statements reported in a research paper against the raw evidence files. It is designed to detect confirmation bias, such as rounding errors, data cherry-picking, or misreported deltas, ensuring strict fidelity between published claims and underlying raw results. Ideal for pre-submission quality control.

This skill provides comprehensive integration with the Podium API, a robust platform for managing business messaging, customer reviews, and payment processing. It utilizes OAuth2 authentication and REST API calls to automate complex business workflows, allowing users to retrieve location data, manage customer communications, and ensure reliable transaction handling. Ideal for automating customer service and core operational tasks within a SaaS business context.

This assistant functions as a professional prompt engineer, specializing in generating highly optimized, production-ready prompts for diverse AI platforms. It analyzes user intent and technical requirements across nine dimensions (Task, Context, Format, etc.), then tailors the prompt structure, constraints, and tone specifically for the target model (e.g., GPT-5.x, Claude, Gemini, Midjourney). Use it when you need to convert a vague idea into a structured, high-performing command for any large language model or generative AI tool.

This skill facilitates an interactive Quality Assurance (QA) session. Users can conversationally describe encountered bugs or issues. The agent clarifies the problem, explores the codebase for context and domain language, and then meticulously files durable GitHub issues. It supports both single-issue reports and breaking down complex problems into multiple, independently verifiable tickets, ensuring developers receive actionable, high-quality reports written from the user's perspective.

This skill automates the creation of professional, animated walkthrough videos from static app designs. It retrieves screen assets from Stitch projects and utilizes Remotion's advanced video composition capabilities to generate polished product demonstrations. Features include smooth transitions, contextual zooming, and animated text overlays, making it ideal for UX reviews, product marketing, and design portfolios.

A comprehensive tool for searching the Model Context Protocol (MCP) across various GitHub locations, including PRs, issues, and discussions. It allows users to track the full lifecycle of protocol specifications, from initial proposals to final merged changes. Furthermore, it facilitates deep dives into pull request reviews and comments, enabling the extraction of key maintainer rationales and design intent for accurate technical understanding.