Skill UI

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.

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.

A guide for creating and maintaining the canonical `AGENTS.md` file. This skill enforces minimal, high-signal documentation, ensuring agents receive concise instructions regarding package management, file-scoped commands, and project conventions, while strictly omitting filler or obvious context.

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.

This comprehensive guide details how to set up and configure AWS Machine Capability Platform (MCP) servers for advanced AI agent use. It provides two options: a Full AWS MCP Server (for API calls and documentation search) and a basic AWS Documentation MCP (for documentation search only). Instructions cover prerequisites, configuration methods (via JSON settings), necessary IAM permissions, and troubleshooting tips for seamless integration of AWS services into AI workflows.

Use this command to set up a structured multi-agent collaboration environment within AgentHub. It allows you to define the core task, specify the required number of agents, and configure detailed evaluation criteria (metrics, directions, and commands) for both quantitative benchmarking and qualitative LLM judging. This is the starting point for any agent competition or complex AI workflow.

Production-grade toolkit that lets Claude Code agents scaffold Playwright configs, generate/review/fix end-to-end tests, migrate suites, sync with TestRail, and run BrowserStack reports.

Agent Analytics delivers CLI-first tracking, experimentation, and optimization so AI agents can monitor site traffic, run A/B tests, and measure funnels consistently across projects.

Validates SKILL.md files against the agentskills specification and Anthropic best practices, catching structure, semantic, naming, and quality issues in a single read-only pass before publishing or debugging skills.

Helps founders, RevOps, and GTM engineers turn manual revenue processes into reliable, observable automation by mapping tech stacks, designing workflow architectures, and orchestrating AI agents across platforms like n8n, Make, and Zapier.

This command executes the entire AgentHub lifecycle in a single, chained invocation. It automates complex development tasks by sequentially handling initialization, capturing performance baselines, spawning multiple specialized agents, evaluating the results (via metrics or LLM judging), and finally presenting a winner for merging. Use this when running an end-to-end competition or complex refactoring task.