Skill UI

This skill assesses the AI readiness of a codebase by running a comprehensive audit against multiple technical and governance pillars. It generates a self-contained, detailed HTML dashboard that provides an overall maturity score, identifies strengths, and, most importantly, delivers a prioritized remediation plan (Fix First / Fix Next). Ideal for auditing repositories before major AI feature development.

This skill generates comprehensive, tailored instruction files for AI coding assistants like GitHub Copilot and Claude. It supports multiple strategies (flat or nested) and is specifically designed for managing complex monorepos by creating area-scoped instructions using `applyTo` globs. Use it to standardize AI tooling usage across large codebases.

Performs a comprehensive external audit of a deployed repository's state, going beyond simple code review. It checks for critical production gaps, including RLS misconfigurations, webhook idempotency issues, secret exposure, deployment health, and prompt injection vulnerabilities. Ideal for pre-launch gates and ensuring code stability before going live.

Guides assessment of KRACK and FragAttacks against WPA2 clients, including official test scripts, likely vulnerable embedded and legacy devices, practical impact, detection, and reporting for modern penetration testing.

A professional methodology for writing penetration test and red team reports, covering executive summaries, technical findings, CVSS scoring, evidence handling, remediation tracking, retest notes, and delivery formats for clients and internal teams.

This tool searches a comprehensive database of over 2500 curated open-source repositories for tools related to Large Language Models (LLMs), ChatGPT, RAG, agents, and NLP. It interprets natural language queries, converts non-English inputs to core English keywords, and systematically filters results across multiple specialized categories (e.g., LangChain, Chatbots, CLIs). Use it whenever the user needs to find specific AI development libraries or open-source AI tooling.

This catalog provides 11 core abstractions for implementing robust mailing functionalities within the Webiny API architecture. It includes Use Cases, Repositories, and various Event Handlers (e.g., before/after send, save). Developers should use these structured patterns to ensure their mailer logic is modular, highly testable, and follows industry best practices.

This utility serves as a command-line alias for invoking the Claude Code `/pua:kpi` function. It loads necessary configurations (pua-pro) to generate comprehensive Key Performance Indicator (KPI) or performance report cards. It manages configuration files by updating `~/.pua/config.json` while preserving unknown fields and ensuring the directory structure exists.

A specialized Codex CLI alias designed to check the operational status of the PUA agent or team. This tool reports current active states, corresponding Process IDs (PID), Time To Live (TTL), and identifies stale or inactive records. It is primarily used for system debugging and monitoring agent health via the command line.

This skill guides the entire chaotic engineering lifecycle, enabling teams to design safe, controlled experiments that surface weaknesses in production systems without causing actual outages. It provides tools for defining hypotheses, calculating blast radius (risk assessment), and generating structured post-mortem reports. Essential for SRE teams, Game Days, and validating system resilience.

A comprehensive pre-production audit tool that scans a codebase for critical issues across eight dimensions: security, database, deployment, code quality, AI/LLM usage, dependencies, frontend, and observability. It intercepts deployment commands, forcing users to resolve critical failures before shipping code. The audit is stack-agnostic and provides detailed pass/fail/manual reports.

This guide helps users navigate and understand the complex components of the ECC (Everything Claude Code) ecosystem, including skills, commands, agents, hooks, and rules. It provides structured paths for new user onboarding, feature discovery, installation planning, and troubleshooting, ensuring answers are derived from the current repository state.