Skill UI

A comprehensive guide providing best practices and architectural patterns for modern full-stack development using Next.js 14+ App Router. Covers advanced topics like Server Components, efficient data fetching, handling parallel routes, and implementing full-stack features with Server Actions for scalable React applications.

Plan first, then orchestrate parallel agents with adversarial verification via the local odw daemon. Use when a task spans many files or items and benefits from concurrent agents.

Open Dynamic Workflows (ODW) is an advanced, open-source engine designed to manage complex coding tasks using multiple AI agents. It operates in three stages: planning the task into parallel subtasks, orchestrating various coding agents (like Codex or Antigravity) to work concurrently, and finally, performing an adversarial verification pass to challenge and validate the combined output before merging. It supports multiple model providers and is ideal for structured, high-reliability software development.

Coordinate focused subagents on substantial work while maintaining non-overlapping ownership and integrating verified results. Ideal for large-scope tasks where parallel processing reduces elapsed time. Decompose tasks into distinct assignments, run scouts in parallel, assign distinct ownership, and synthesize outputs. Keep trivial work with the coordinator to avoid overhead.

This skill provides a systematic pipeline for safely executing large-scale code refactors across multiple files or subsystems. It first performs parallel, dependency-aware analysis to map the entire scope, synthesizing a single, actionable plan. The process then breaks the work into independent 'work packets,' which are executed sequentially or in parallel by specialized worker agents, ensuring strict dependency management and behavior parity throughout the entire process.

Coordinates multiple specialized agents via Claude Code's native Agent Tool for complex tasks requiring diverse expertise, such as comprehensive code analysis, feature reviews, and security audits. Enables sequential chains, context passing, and resumable work, with a synthesis protocol for unified recommendations.

Use Parallel's free web_search and web_fetch MCP tools to find public sources, read relevant passages, and answer with supporting links. Ideal for current facts, technical documentation, and research with citations.

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.

This skill enables developers to scaffold, write, and debug enterprise-grade Playwright E2E tests in Java. It enforces the Page Object Model, JUnit 5, and Allure reporting integration. Supports parallel execution, CI/CD pipelines, and thread-safe base classes for production-quality testing. Ideal for Java 17+ environments.

This skill reviews git diffs or specific file scopes for code reuse, quality, efficiency, clarity, and standards compliance. It optionally applies safe, behavior-preserving fixes. It uses parallel read-only sub-agents to inspect code while the main agent applies changes. Suitable for auditing changes, simplifying code, or refactoring.

Review supervisor, swarm, planner-worker and parallel agent workflows as distributed state machines: task ownership, shared state, branch joins, retries, cancellation, context handoffs, budgets, deadlocks and human escalation before rollout.

This skill enables parallel read-only multi-agent review of git diffs or specific file scopes. It identifies behavioral regressions, security risks, performance issues, and coverage gaps using four specialized sub-agents. The main agent synthesizes findings to filter and summarize critical issues without modifying code. Ideal for ensuring code quality and safety before merging changes.