Skill UI

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.

Guides authors through adapting test-driven development to agent skills documentation, covering when to create skills, required background, directory structure, and how to run pressure scenarios to ensure compliance before publishing.

Designs and optimizes AI agent execution by defining action spaces, tools, observation formats, recovery contracts, and context-budget strategies to boost completion rates and reliability.

A comprehensive protocol defining communication rules and orchestration patterns for multi-agent C-suite teams. It structures complex cross-functional analyses, simulates board meetings, and enforces hard rules—such as depth limits and loop prevention—to ensure consistent, verifiable, and non-circular business insights.

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.

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.

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.

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.