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.

Provides patterns for migrating test files to React 19 compatibility, covering act() imports, Simulate removal, StrictMode call count changes, and cleanup of react-dom/test-utils. Helps developers update testing codebases efficiently.

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.

SSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention. Use for batch experiments, grid runs, multi-seed sweeps, or auto-chain experiments when /run-experiment cannot handle 10+ jobs that need orchestration.

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.

This guide provides a comprehensive setup for configuring CI/CD pipelines for Miro REST API v2 integrations. It uses GitHub Actions to automate unit and integration testing, validate API connectivity, and implement crucial cleanup steps to ensure test isolation and reliable results.