Skill UI

A utility for managing and installing specialized AI skills into the Codex environment. It supports fetching curated skills from predefined sources (like openai/skills) or installing custom capabilities directly from any GitHub repository, including private ones. It handles listing available skills, installing specific modules, and provides robust fallback methods (download/git sparse checkout).

This skill designs and audits the core engineering harness required for reliable AI agent collaboration within a codebase. It establishes critical infrastructure elements such as change tracking, linting rules, CI/CD gates, and standardized documentation, ensuring the repository remains the single source of truth for all intelligent agents. Ideal for setting up complex development workflows.

This skill enables the creation of a robust 'harness' system for coding agents within a repository. It addresses the need to prevent repeated AI-generated coding mistakes by implementing durable instructions, guardrails, regression tests, and failure memory. It treats the target repository as the source of truth, ensuring consistency and maintaining high reliability across all automated contributions.

This skill scaffolds the necessary configurations for advanced engineering workflows. It guides the user through setting up the issue tracking system (GitHub, GitLab, or local markdown), defining the canonical triage label vocabulary, and establishing the domain documentation context (single or multi-context). It must be run once to initialize the repository for subsequent agent-driven development skills.

This skill acts as a comprehensive maintenance orchestrator for the brooks-lint plugin repository. It executes a rigorous, sequential subagent pipeline—including authoring, evaluation, QA, trigger auditing, and release management. Use this when adding, editing, or updating skills to ensure all related components (manifests, README, CHANGELOG) are synchronized and adhere to strict quality gates throughout the development lifecycle.

This skill provides comprehensive access to the Hugging Face Hub ecosystem via MCP server tools. Users can search for state-of-the-art models, datasets, and research papers, retrieve detailed repository information, fetch documentation, run compute jobs on GPU/CPU, and interact with Gradio Spaces as fully functional AI tools. Ideal for ML development, research, and application prototyping.

This skill is designed to accurately convert complex, Hugo-managed documentation sites and content into standard Markdown format. It goes beyond generic knowledge by inspecting the entire local Hugo repository structure, including `hugo.toml`, custom shortcodes, layouts, and content files. It preserves the original content's semantic meaning while materializing Hugo-specific constructs into clean Markdown.

This skill provides an autonomous, repository-wide pipeline for auditing and fixing complex logical inconsistencies. It systematically executes stages including health checks, deep review, locating divergences, generating fixes, and performing diff-verification until the codebase is deemed clean. Ideal for maintaining high code quality and resolving deep, systemic bugs across entire projects.

Provides championship-grade repository hygiene and health checks. It systematically audits critical areas, including dependency health, CI/CD pipeline alignment, documentation synchronization, and detecting stray artifacts. The tool generates a prioritized, actionable cleanup plan and offers auto-fix capabilities to keep codebases pristine and production-ready.

This skill automates the commit and push process for local skill changes. It should be used after a skill has been fully validated and updated, ensuring the canonical copy of the skill is published to the configured remote GitHub repository. This is essential for sharing or finalizing skill updates.

Generates a precise, evidence-based installation plan for a project's component catalog (ECC). Instead of loading a full, noisy bundle, this skill analyzes the repository's actual code structure, dependencies, and CI/CD evidence to classify skills, rules, and commands into 'DAILY' (essential) and 'LIBRARY' (referenceable) buckets. It ensures only necessary components are active, optimizing the development environment.

A comprehensive guide to implementing Clean Architecture patterns for both Android and Kotlin Multiplatform (KMP) projects. It covers robust module structuring, dependency inversion rules, defining UseCases and Repositories, and designing the data layer using technologies like Room, SQLDelight, and Ktor. Learn how to separate concerns, maintain platform independence, and manage complex data flows.