Skill UI

Guides agentic engineering efforts where AI agents execute implementation while humans focus on risk-aware evaluation, breaking work into verifiable units, routing models by complexity, and tracking costs and regression checks.

Aws Agentic Ai refers to an AI-driven agentic capability within AWS, aiming to automate complex workflows by orchestrating services and responding to evolving goals with minimal human intervention.

This skill implements the BDI (Belief-Desire-Intention) architecture for advanced cognitive agent modeling. It allows agents to reason about context by transforming external knowledge (like RDF triples) into internal mental states (beliefs, desires, intentions) and vice versa. Key features include goal-directed planning, explainable reasoning chains, and T2B2T (Triples-to-Beliefs-to-Triples) flow, making it ideal for multi-agent systems and augmenting LLMs with formal cognitive structures.

A structured multi-agent board meeting protocol for executives that moves through six phases—context loading, isolated role contributions, critic review, synthesis, founder review, and decision logging—to drive consistent strategic outcomes when invoking /cs:board.

Provides a virtual boardroom for founders with CLI-driven C-suite agents, structured multi-role meetings and routing to deliver strategic recommendations, context capture, and decision logging for high-stakes choices.

An expert agent focused on enhancing code clarity, consistency, and maintainability. It simplifies complex code structures, eliminates redundant logic, and applies project-specific best practices, all while guaranteeing that the original functionality and behavior remain absolutely unchanged. Ideal for code reviews, cleanup tasks, or improving readability.

Defines a continuous autonomous agent loop pattern with quality gates, evaluations, and recovery actions, orchestrating CI/PR controls, RFC breakdown, and assessment cycles to keep release branches stable.

This pattern addresses the fundamental limitation of context window size by treating the filesystem as a persistent, external memory. Agents can write large outputs, plans, or findings to files, offloading them from the active context. Instead of carrying all information, agents use targeted search and retrieval tools to load only the necessary context on demand, drastically reducing token bloat and improving performance over long, complex tasks.

This infrastructure provides a robust solution for running powerful AI agents in remote, sandboxed environments. It overcomes the limitations of local execution by ensuring unlimited concurrency, consistent environments, and collaborative features. Use this to build complex background coding agents, multi-client interfaces (e.g., Slack, Web), or scale agent workloads far beyond local machine constraints.

Sets up a new AgentHub collaboration session by scaffolding the .agenthub directory, generating session IDs, configuring evaluation criteria, and optionally capturing a baseline metric so teams can immediately spawn agents and run structured evaluations.

Comprehensive suite of 42 marketing agent skills spanning content, SEO, CRO, channels, growth, intelligence, and sales, built on a shared brand context plus orchestration router and stdlib-only Python tools for campaign automation.

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.