Skill UI

This skill orchestrates deep critical reviews of research work using external AI models like Codex, Gemini, or Claude. It gathers project context, identifies logical gaps, and provides adversarial feedback on methodologies and results. Suitable for academic papers, experimental validation, and refining research ideas through multi-round reasoning.

A comprehensive guide to hardening GitHub Actions CI/CD workflows against modern supply chain attacks, credential theft, and privilege escalation. Learn techniques such as pinning actions to immutable SHA digests, minimizing GITHUB_TOKEN permissions, preventing script injection from untrusted inputs, and implementing required review gates for secure deployment pipelines. Essential for any project handling production secrets or elevated permissions.

Vibers provides a human code review workflow for AI-generated GitHub projects; add the collaborator, GitHub Action, and commit guidance to receive spec- and security-focused feedback plus follow-up PR fixes.

This skill helps developers structure and record critical architectural decisions (ADRs) made during coding sessions. It automatically detects moments of decision, capturing the context, alternatives considered, and the rationale (trade-offs and consequences). This maintains a living log that explains 'why' the code base was built the way it is, preventing knowledge loss and improving project maintainability.

Astro is a modern web framework designed specifically for building high-performance, content-heavy websites like blogs, documentation sites, and portfolios. Its core innovation is the Islands Architecture, which ships zero JavaScript by default, drastically improving performance and Core Web Vitals. It supports multi-framework components (React, Vue, Svelte) in a single project, making it ideal for content developers prioritizing speed and flexibility.

Automatically analyzes unfamiliar codebases to generate a comprehensive, structured onboarding guide. It systematically detects package manifests, framework fingerprints, architecture maps, key entry points, and coding conventions. Ideal for new developers joining a project or setting up a repository for the first time.

Helps developers answer codebase questions by leveraging the `.understand-anything/knowledge-graph.json` graph; walks through project metadata, nodes, edges, and layers to explain dependencies and contexts.

Launches an interactive web dashboard designed to visualize the knowledge graph derived from a target codebase. This tool helps developers gain a high-level, structural understanding of complex projects by mapping out relationships, module dependencies, and the overall knowledge network of the code, making maintenance and onboarding significantly easier.

Creates a comprehensive onboarding guide for new teammates by extracting project context from the knowledge graph, layering architecture descriptions, guiding tours, and highlighting complex files so newcomers can ramp up quickly.

This skill enables AI agents to maintain persistent project planning using Markdown files on disk. It manages task_plan.md, findings.md, and progress.md to extend context window limits. Lifecycle hooks automatically inject planning content and prevent command execution in Markdown. Ideal for complex research tasks requiring over 5 tool calls, ensuring state persistence across sessions without network uploads.

A comprehensive toolset to scaffold, build, sign, and package SwiftPM-based macOS applications entirely without requiring an Xcode project. It guides users through the end-to-end process, including setting up the project, running the app, performing code signing, notarization, and generating update appcasts for robust, reproducible releases.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.