Skill UI

This skill conducts a comprehensive audit to identify and prioritize automation opportunities within a user's existing workflow. It guides users through a structured intake (covering roles, repetitive tasks, and pain points) and systematically scans connected tools (like Gmail, Slack, Calendar, and CRMs) to surface actionable, ranked quick wins, minimizing implementation effort and maximizing impact.

The RunAPI CLI serves as the dedicated execution layer for interacting with RunAPI's suite of advanced AI models. It allows developers to programmatically generate multimodal content, including images, videos, and music/audio, directly from the terminal or CI/CD pipeline. Users can submit one-off jobs, manage asynchronous tasks, and pass complex JSON request bodies to various services like Suno, Claude, and Gemini, making it essential for robust AI automation workflows.

This skill utilizes the built-in macOS `caffeinate` command to prevent the Mac from entering sleep mode. It is essential for guaranteeing uninterrupted workflow during long-running tasks, such as code builds, large downloads, or supervised automation sessions. Users can specify a fixed duration or attach the function to a specific process ID for continuous wakefulness.

This skill drives an existing browser instance using the Chrome DevTools Protocol (CDP). It is essential for complex web automation tasks that require visible interaction, JavaScript execution, or handling authenticated, stateful sessions. Use it when static data fetching is insufficient, such as simulating user logins, interacting with iframes, or performing visual verification. Supports headless and remote environments.

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.

Transforms Claude Code into a fully autonomous agent with persistent memory, scheduled operations, computer use, and task queueing. Replaces standalone agent frameworks using native crons, dispatch, MCP tools, and memory. Ideal for continuous automation and self-directed agent loops.

Extract structured data from web pages using stored templates or one-shot DOM queries. Includes mandatory security gates for PII and prompt injection before data reaches the model. Supports reusable templates for recurring scraping tasks and browser automation workflows.

Automates the process of filling out web forms using a structured field map (field_name → value). This tool supports using stored templates for known forms, or falling back to analyzing the page accessibility snapshot if no template exists. It includes mandatory PII screening (aidefence_has_pii) and handles form submission, making it ideal for automating sign-ups, checkouts, and data entry tasks.

Design task-local harnesses, evaluation gates, and reusable skill extraction for adaptive agent workflows. This skill helps build structured systems for one-off work, shared skills, and observable checkpoints. It defines input boundaries, tool bindings, and safety checks to ensure reliable automation without manual intervention.

Manage LobeHub tasks through the lh CLI: view task details and instructions, update status or priority, create and sort subtasks, set dependencies, pin documents, and log progress comments. Includes JSON output and guidance for recurring automation loops.

Provides comprehensive guidance for researchers deciding the optimal conference venue for Software Engineering (SE) research. It distinguishes between contributions that focus on *automation* (techniques or tools that automate SE tasks) suitable for ASE, versus those that are broad empirical findings, theoretical results, or pure ML advances, directing them to venues like ICSE, FSE, or ISSTA.

Provides comprehensive guidelines for structuring and writing academic papers for Automated Software Engineering (ASE) conferences. It teaches authors how to present an automated task, emphasize the automation design over the deep learning model, provide evidence from real subjects, and adhere to strict formatting and argument presentation rules to maximize acceptance chances.