Skill UI

WorkorAI is a comprehensive talent marketplace skill designed for agents. It supports candidates in searching jobs, managing applications, and tracking job status. For employers, it manages the entire job lifecycle and provides advanced candidate discovery, ranking candidates with detailed "white-box" match explanations (fit score, proven skills, gaps) instead of simple black-box scores. It facilitates the entire hiring workflow.

This skill provides the central management layer for defining an AI agent's capabilities. It uses the `capabilities.yaml` file as the single source of truth to configure everything from integrated skills, tools, hooks, and sub-agents. Use this when running any `capa` command (install, clean, add, etc.), integrating a new service, or troubleshooting the project's entire operational environment.

Context Kit is an external framework designed to organize and manage personal, durable context data (like identity details, memories, and domain knowledge) into secure Markdown artifacts. It helps users set up robust knowledge bases for coding agents (e.g., Claude Code), guiding them on best practices for data privacy, local storage, and safe integration, while preventing sensitive data leaks.

A specialized presentation deck template adopting a cyberpunk, CRT terminal aesthetic. It is ideal for conducting deep technical reviews, such as benchmarking CLI agents, demonstrating code diffs, or presenting complex development tool analyses. Features a dark background, grid lines, command prompt indicators, and structured tagging for maximum professional impact.

The Agent Harness provides a robust, verifiable loop structure for driving complex goals across specialized domains. It converts a high-level objective into a verifiable task plan, executes tasks sequentially using domain-specific tools, and rigorously verifies every step with machine-run checks. The system includes mechanisms for retries, budget management, and mandatory human escalation, ensuring no goal is closed until all progress is verifiably complete or explicitly waived.

Provides comprehensive guidance on scheduling AI agent runs, covering external systems like cron and systemd, and built-in schedulers (e.g., Hermes). It details best practices for setting up recurring jobs, implementing heartbeat patterns, and avoiding common pitfalls like tight timers or state loss, ensuring reliable, scheduled automation.

A comprehensive utility for managing complex cmux topologies, including workspaces, panes, and surfaces. This tool is essential for inspecting, controlling, and monitoring multiple AI coding agents running in parallel within a macOS terminal environment. It supports advanced functionalities such as browser automation and real-time status notifications, optimizing developer workflows.

This guide details how to effectively delegate complex, multi-step tasks to various specialized AI agents (Codex, Pi, Claude, Hermes). It covers best practices for maintaining conversation context, handling TUI prompts (e.g., avoiding newlines, using quotes), techniques for reliable polling, and choosing the optimal agent for coding, design, or general purpose work. Essential for advanced AI workflow automation.

This guide details the comprehensive workflow for distributing a single AI skill across various local agent skill directories (Codex, Claude, Pi, Hermes). It addresses canonical locations, symlink management, and specific synchronization commands to ensure that all agents detect the latest skill version while correctly navigating common development pitfalls.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.