Skill UI

A powerful tool for architecting production-grade multi-agent workflows. It allows users to define complex, multi-step AI pipelines using structured patterns (sequential, parallel, hierarchical). Key features include defining explicit handoff contracts, implementing robust failure handling, and managing context/cost discipline across long-running processes, ensuring reliable and scalable AI system deployment.

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.

Dispatching parallel agents lets you split independent investigations into focused prompts, coordinate isolated fixes concurrently, and review summaries for integration once each agent finishes.

Parallel Web Research skill orchestrates Parallel Chat and Extract APIs to search the internet, perform deep intelligence work, and craft cited summaries, with model controls, JSON outputs, and citation verification for market, news, technical, and general queries.

A comprehensive guide and methodology for identifying, measuring, and fixing performance bottlenecks across the entire stack. This skill covers optimizing database queries (e.g., N+1 problems, missing indexes), improving API efficiency (caching, parallel processing), and enhancing frontend performance (memoization, code splitting, image optimization). It emphasizes the critical 'Measure First' principle to ensure all optimizations are data-driven and effective.

Coordinates a pre-task intelligence protocol that activates all relevant agents, synthesizes parallel analyses, estimates real execution time, maps probable risks, and produces a contingency-aware plan before executing the user’s request.

Provision enterprise-grade Playwright Java tests with a Page Object Model scaffold, JUnit 5 workflows, thread-safe parallel runs, Allure reporting, CI-friendly setup, and hybrid API/UI coverage to fix flakes and scale browsers.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

Performs a full GEO audit of any website by discovering business type, crawling prioritized pages, and delegating parallel subagents to evaluate AI visibility, platform readiness, technical infrastructure, EEAT content, and schema markup, then outputs a composite GEO score with prioritized actions.

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.

An interactive tool designed to discover, select, and assemble specialized teams of AI agents. It supports structured agent collections across different domains (e.g., Engineering, Marketing). Users select multiple roles for a specific task, and the skill launches them in parallel, synthesizing their independent findings into a unified report, including consensus and conflict analysis.

Spawns N parallel AI subagents that compete on the same task using isolated git worktrees. A coordinator dispatches, monitors, evaluates results by metric or LLM judge, then merges the winning branch. Ideal for parallel optimization, content variations, refactors, and research exploration.