Skill UI

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.

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.

This skill manages comprehensive software development life cycle operations—including issue triage, PR reviews, version releases, bug fixing, and marketing content generation. It operates using a parallel "agent army" of dynamic agents (10-20 per task), requiring the user to adopt an Engineering Manager (EM) mindset. Key principles involve rigorous citation checking (anti-hallucination) and applying multiple roles (Product Owner, Open Source Contributor, Architect) simultaneously to ensure robust, safe, and user-centric outcomes across all platforms.

Master Git best practices for code management, including trunk-based development, atomic commits, and descriptive messages. Organize parallel work streams, manage branches effectively, and prepare for releases with semantic versioning. Essential for maintaining clean, reviewable, and reversible code history in modern development environments.

A comprehensive, generics-first utility library designed for Go developers who prefer declarative, functional programming patterns over manual loops. It provides 500+ type-safe functions for common data transformations (Map, Filter, Reduce, GroupBy, etc.) on slices, maps, and strings. By defaulting to immutable operations, it ensures thread safety and predictable state changes. The library also offers specialized packages (lop, lom, loi) to address performance bottlenecks, such as CPU-bound parallel processing or lazy evaluation, allowing engineers to choose the optimal approach for their c...

This mode simulates a P9 Tech Lead role, specializing in strategic project management and complex task orchestration. It does not write code itself, but focuses on generating detailed, structured Task Prompts (six elements) and managing the entire workflow delivery from multiple specialized agents (P8 teams). Ideal for coordinating large, complex projects requiring strategic decomposition and coordination of three or more parallel agents.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

Dispatch dedicated agents with isolated contexts to handle multiple independent failures or tasks in parallel. Use when you have several unrelated issues that can be investigated separately without shared state, while preserving your own context for coordination and review.

This technique is designed for advanced academic research, enabling users to search for theses by running two parallel tracks: a Topic Track (focusing on discipline, methodology, and materials) and a People Track (focusing on university, advisor, degree type, and year). By switching between these two methodologies, users can cross-validate results and significantly improve the accuracy and completeness of their literature review.

Scale pandas and NumPy workflows beyond RAM using Dask for parallel and distributed computing. Ideal for processing large multi-file datasets, out-of-core DataFrames, big arrays, and unstructured data, from laptops to clusters.

Pythonic RDKit wrapper for molecular cheminformatics: parse and standardize SMILES, compute descriptors and fingerprints, cluster compounds, generate 3D conformers, and run parallel drug-discovery workflows.