Skill UI

Runs Semgrep static analysis with auto language detection, parallel Task subagents, Semgrep Pro checks, and configurable “run all” or “important only” modes to audit codebases for vulnerabilities and bug patterns.

This comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.

This skill simulates a complete, multi-agent software development lifecycle following the principles of Agile and Scrum. It guides the project through six distinct, iterative phases: Architectural Planning, Parallel Implementation, Comprehensive Testing (Unit/E2E), Review, and Finalization. The process uses a convergent diffusion model, refining specifications and resolving conflicts across multiple specialized agents until the project converges and is deemed complete.

This skill provides opinionated and evolving constraints designed to guide AI agents effectively when building user interfaces. It offers structured patterns and guidelines, ensuring that the generated interfaces adhere to specific best practices and development rules. Use this skill when your task requires highly structured, guided output for UI development, moving beyond simple generation to guided design.

Provides durable workflow automation guidance for AI agents, covering platforms like n8n, Temporal, Inngest and patterns such as sequential, parallel, and orchestrator-worker flows to turn brittle scripts into production-grade orchestration.

A comprehensive guide for developing, optimizing, and standardizing skills for AI agents. This resource provides a decision tree for choosing the right architecture (Tier 1-3), incorporating best practices like CSO (LLM SEO) and Anti-Rationalization, and ensuring compliance through rigorous testing and checklists. Essential for robust AI system building.

Audits GitHub Actions workflows that invoke AI coding agents to surface prompt injection and sandbox risks, tracing attacker-controlled inputs through env vars, reusable actions, and workflow references before deployment or code review.

This powerful skill manages Git worktrees to enable safe, isolated parallel feature development. It standardizes branch isolation, automatically allocates unique network ports, and synchronizes environments, allowing multiple agents or terminal sessions to work on different branches simultaneously without conflicts. It also provides mechanisms for detecting and safely cleaning up stale worktrees.

A powerful tool to scaffold and ship production-ready Model Context Protocol (MCP) servers directly from OpenAPI specifications. It supports Python and TypeScript, ensuring schema validation, safe evolution, and robust tool definition generation, eliminating the need for manual wrapper coding. Use it to expose existing REST APIs to LLM agents or build shared tool integrations.

Guides execution of implementation plans by dispatching isolated subagents per task, enforcing a spec-first then quality review loop, and preserving overall coordination context for faster, high-quality iterations.

Performs static security analysis on GitHub Actions workflows that incorporate AI coding agents (e.g., Claude, Gemini, OpenAI). This skill helps identify critical data flow vulnerabilities, such as where attacker-controlled input can reach AI agent prompts or internal systems via CI/CD pipelines, going beyond simple expression checks.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.