Skill UI

CLI skill that lets you subscribe to Google Workspace events, pull them via Pub/Sub, and stream each payload as NDJSON with options for batching, cleanup, and file output.

Use `gws gmail +watch` to monitor Gmail inboxes and stream new messages as NDJSON so downstream Pub/Sub or file workflows can process them; supports label filters, polling control, one-off pulls, and cleanup to keep your pipeline tidy.

A comprehensive guide for deploying, configuring, and maintaining the OpenViking server in production environments. It covers service configuration management, environment setup using `uv` for virtual environments, reliable startup procedures using `nohup`, graceful shutdown methods, and critical data cleanup workflows. This resource is essential for DevOps engineers and system administrators managing OpenViking services.

Automates applying Gmail labels to matching messages and archiving them via gws commands so recurring notifications stay organized without manual inbox cleanup.

This expert system is designed to act as an Arch Linux specialist, diagnosing and resolving complex system issues using best practices for rolling-release distributions. It guides users through step-by-step triage plans, leveraging native tools like pacman, systemctl, and journalctl. It provides actionable, copy-paste-ready commands for remediation, verification, and cleanup, ensuring stable system recovery.

Converts text-based documentation into structured Markdown by following explicit instructions, documented options, or reference files, supporting header levels, patterns, platforms, and cleanup steps for polished technical documents.

Guides React 19 test migrations by outlining act import changes, Simulate-to-fireEvent replacements, full react-dom/test-utils cleanup, and StrictMode call-count verification strategies.

This skill generates a detailed, multi-phase plan for complex, multi-file refactoring projects. It ensures safety by first analyzing the codebase, identifying dependencies, and structuring changes in a safe sequence (e.g., Types first, then implementations, followed by tests and cleanup). It forces the AI to output a comprehensive plan, including rollback steps and verification points, and waits for user confirmation before making any actual code changes.

This skill guides developers through the structured completion of a feature branch. It enforces running comprehensive tests, detects the current Git environment (e.g., common repository vs. worktree), and presents clear options (merge, PR, retain, discard). It ensures safe integration, manages worktree cleanup, and enforces best practices for version control workflows.

A robust orchestration skill designed for managing large-scale, complex machine learning experiments on remote GPU clusters via SSH. It handles multi-seed sweeps, sequential wave transitions, and deep learning pipelines requiring state management. Features include OOM-aware automatic retries, cleanup of stale terminal sessions, and deterministic scheduling to prevent race conditions, making it ideal for grid computing and staged training.

This skill performs comprehensive, systematic cleanup across 11 critical dimensions of code quality, including dead code removal, security vulnerability checks, weak type elimination, and performance optimization. It analyzes code, assigns confidence scores, and verifies all suggested changes using build and test gates. Ideal for projects suffering from accumulated technical debt, or before major release cycles to ensure robust, maintainable code.

A robust framework for building complex, long-horizon AI agents using LangGraph. It addresses common pitfalls in advanced agent design, such as unbounded state growth (Virtual FS cleanup) and subagent persona leakage (explicit message overriding). Ideal for tasks requiring multi-step planning, task delegation, and self-correction through reflection loops.