Skill UI

A specialized Codex CLI alias designed to check the operational status of the PUA agent or team. This tool reports current active states, corresponding Process IDs (PID), Time To Live (TTL), and identifies stale or inactive records. It is primarily used for system debugging and monitoring agent health via the command line.

This CLI alias executes a comprehensive teardown protocol for PUA agents, designed for development environments. It safely releases all active agent and loop states, ensuring a clean system reset. It automatically manages configuration files like ~/.pua/config.json, preserving unknown fields while providing reliable, evidence-based cleanup.

This comprehensive skill facilitates deep integration with X (Twitter) data, supporting advanced scraping, user lookup, and data extraction. It provides multiple integration patterns for developers: dedicated SDKs (Python, TypeScript, Go, etc.), REST endpoints, hosted Apify Actors, and agent automation via MCP. Ideal for building robust data pipelines, monitoring systems, and AI-powered applications requiring real-time X data.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

Design a persistent multi-agent operating system on Claude Code with kernel routing, specialist agents, slash commands, file-based memory, and scheduled automation for long-lived projects.

Use this skill to deeply inspect and audit the entire process of local AI coding-agent sessions. It tracks critical operational metrics such as token consumption, cost spikes, tool failures, latency anomalies, and overall health scores. Ideal for debugging unreliable code runs, establishing CI/CD health gates, or comparing different attempts to ensure semantic accuracy.

This guide helps users navigate and understand the complex components of the ECC (Everything Claude Code) ecosystem, including skills, commands, agents, hooks, and rules. It provides structured paths for new user onboarding, feature discovery, installation planning, and troubleshooting, ensuring answers are derived from the current repository state.

This specialized agent allows applications to programmatically join, monitor, and leave live video conferences. It reads real-time events visible to the current identity, such as participants joining/leaving, speaking, chatting, and screen sharing. It is ideal for querying what happens during an active meeting or identifying the correct active meeting IDs. Note: It does not handle post-meeting tasks like transcriptions or recordings.

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

This guide provides a comprehensive framework for authoring new, structured AI agent skills. It details a three-phase workflow, including gathering requirements, drafting the main instructions (SKILL.md), and ensuring optimal structure. It emphasizes best practices for descriptions, scripting, and file splitting to improve agent reliability and usability.

This advanced skill manages complex, large-scale development tasks by coordinating multiple, specialized subagents in parallel. It systematically decomposes the goal into isolated missions, manages resource quotas (token usage), and handles error recovery across multiple files and components. Ideal for large feature builds, system refactoring, or complex applications requiring multiple, concurrent roles (Planner, Builder, Tester).

This skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).