Skill UI

An open-source automation framework for conducting multi-turn, adversarial red-teaming against conversational LLMs. PyRIT simulates real-world attacker behavior using advanced orchestrators (Crescendo, TAP) and scorer feedback loops. It is designed for comprehensive security assessments of AI agents and chatbots, going far beyond single-shot prompt testing to evaluate robustness under gradual or adaptive attacks.

A comprehensive guide for managing AWS infrastructure as code using the SST v4 (Ion) framework, built on top of Pulumi. This skill covers the entire lifecycle, including writing and editing sst.config.ts, defining high-level components (Function, Bucket, Dynamo), wiring resource links, writing unit tests for infrastructure, and troubleshooting deployments across complex, multi-stack AWS environments.

A portable, framework-agnostic system designed for robust field-side observability in React or React Native apps. It establishes a strongly typed event taxonomy, ensures best-effort, non-blocking data fan-out across multiple analytics providers, and enforces consent gates for all telemetry data. This system complements synthetic testing by capturing real user vitals (LCP, INP, CLS) and behavioral events in production.

A robust protocol designed for multi-agent collaboration on long-running projects. It simulates a self-optimizing city where diverse AI agents (citizens) interact using decentralized markdown files (The Register, Contracts, Chronicle). This framework enables cross-vendor optimization, dynamic work routing, and constitutional evolution without requiring a centralized server or specific runtime environment.

This skill generates comprehensive visual regression test configurations using SmartUI for accurate screenshot comparison on cloud platforms. It is framework-agnostic, supporting major tools like Playwright, Selenium, Cypress, and Puppeteer. Use it when detecting UI changes, ensuring visual consistency, or running automated visual tests across multiple viewports and browsers.

This skill generates comprehensive end-to-end (E2E) automation tests using WebdriverIO (WDIO) in JavaScript or TypeScript. It supports various frameworks (Mocha, Jasmine, Cucumber) and includes advanced patterns like the Page Object Model (POM) and robust selector strategies. Users can configure tests for local execution or integrate with cloud services like LambdaTest, making it ideal for simulating complex user interactions and ensuring application quality across multiple browsers and environments.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

A comprehensive, multi-session framework designed to systematically uncover and articulate a brand’s core identity. Using advanced techniques like Laddering, 5 Whys, and Projective methods, the process covers purpose, positioning, audience, personality, and voice. The final output is a reusable Master Brandbook perfect for briefing designers and marketing teams.

A robust framework for orchestrating complex, multi-step coding tasks using multiple AI agents. It functions by first planning the entire project into a mission dependency graph (DAG), then dispatching specialized agents to isolated Git worktrees for parallel development. The system automatically monitors progress, handles merges, and generates structured, comprehensive reports detailing changes, completed tasks, and next steps.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.

A comprehensive framework for creating and executing intelligent, goal-oriented action plans (GOAP). This system analyzes preconditions, optimizes costs, and provides adaptive replanning capabilities. It is designed for complex objectives that require multiple, dependent steps, ensuring robust task completion even when unexpected state changes occur.

This skill executes the metaharness score to provide a comprehensive, multi-dimensional readiness scorecard for a codebase. It assesses critical dimensions like compilation confidence, task coverage, tool safety, and memory usefulness. Use it to evaluate if a repository is prepared for harness adoption before initiating development tools or framework setup.