Skill UI

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

Design a persistent multi-agent operating system on Claude Code with kernel routing, specialist agents, slash commands, file-based memory, and scheduled automation for long-lived projects.

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.

Code Health MCP provides structural maintainability feedback and design-level health scores, going beyond standard linting. It is crucial for AI-assisted coding workflows, allowing users to review code quality, verify score deltas after changes, and gate commits or pull requests to prevent codebase regression. Use it when refactoring, updating legacy modules, or ensuring the integrity of large diffs.

This comprehensive guide establishes foundational coding standards and best practices for enhancing code quality, readability, and maintainability across all projects. It covers naming conventions, immutability patterns, error handling, and core design principles like KISS, DRY, and YAGNI, applicable to TypeScript, JavaScript, and React development. Use it when starting a new module or performing code reviews.

This utility provides garbage collection for Claude Code configurations. It periodically scans various directories—including skills, memory files, hooks, permissions, and caches—to identify orphaned, stale, or redundant configuration items. Crucially, the process is designed to be non-destructive, always requiring explicit human confirmation for every deletion to ensure a safe, audited cleanup process.

This tool validates the structural integrity of a Domain-Driven Design (DDD) codebase. It automatically detects cross-context import violations, checks for broken aggregate invariant enforcement, and verifies adherence to established patterns (like repository and event naming conventions). Essential for CI/CD gates and large-scale codebase auditing to prevent architectural leakage.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.

GateGuard is a mandatory pre-tool-use hook designed to significantly enhance the reliability and safety of LLM-generated code and actions. Instead of relying on self-evaluation, it forces the model to perform concrete, verifiable investigations—such as listing all imported files, checking data schemas, or detailing rollback procedures—before any write or destructive command execution. It establishes a three-stage gate (Deny, Force, Allow) to prevent hallucination and ensure context awareness in complex coding environments.

A powerful tool designed to scaffold sophisticated, custom AI agent harnesses using the metaharness CLI. It supports various templates (e.g., coding, devops, marketing) and AI hosts (e.g., Claude, Codex). Crucially, it operates with maximum safety, defaulting to a dry-run mode and refusing to write to the calling repository root, ensuring controlled and predictable development workflows.

A comprehensive pipeline designed for Laravel projects to ensure code quality and stability before deployment. It systematically executes environment checks, linting, static analysis, full testing with coverage, security audits, migration validation, and cache preparation. Essential for robust CI/CD pipelines and pre-Pull Request validation.