Skill UI

Detects system CPU, GPU, memory, and disk availability before heavy scientific tasks, writing `.claude_resources.json` with strategic recommendations (parallelism, out-of-core, GPU acceleration) so analysts choose appropriate compute approaches.

Polars is a blazing fast in-memory DataFrame library based on Apache Arrow that replaces pandas for 1-100GB workloads, offering expression-based lazy execution, parallel operators, and rich I/O for ETL pipelines.

Real-time research lookup that auto-selects between Parallel Chat API and Perplexity sonar-pro-search to gather papers, facts, market data, and cited findings.

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.

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.

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.

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 skill for orchestrating parallel AI subagents that compete on the same task using isolated git worktrees. Handles session setup, agent spawning, progress monitoring, evaluation via metrics or LLM judge, and final merge of the winning branch. Ideal for code optimization, content variation, research exploration, and any task benefiting from parallel competition.

CCPM orchestrates software delivery from PRD through epics and GitHub issues into parallel agent execution, covering planning, decomposition, syncing, workstart, and status checks with mandated script-based reporting.

The /hub:spawn command launches N subagents simultaneously, each operating in an isolated git worktree to work on the same task. It supports custom templates (like optimizer or refactorer) and manages the entire parallel execution cycle, ensuring diverse strategies and structured results for maximum value exploration.