Skill UI

This API module provides comprehensive tools and abstractions for managing scheduled actions within the webiny ecosystem. It covers the entire lifecycle of asynchronous tasks, including scheduling actions for future execution, retrieving details, listing existing tasks, executing them immediately, and canceling them. Key components include dedicated Use Cases and a core SchedulerService, enabling robust background processing and reliable task management for enterprise applications.

This skill provides an end-to-end, reproducible workflow for analyzing matched tumor-normal pairs using the nf-core/pacsomatic pipeline. It handles input validation (BAM files, IDs), generates necessary samplesheets and launch artifacts, and supports execution across various environments, including local runs and cluster schedulers (Slurm, LSF). It is ideal for initiating, validating, or troubleshooting the entire analysis process.

An internal tool designed to validate candidate meta-skills (SKILL.md) before they are registered in the system. It performs comprehensive linting checks, including structural validation (G1: parsing, reference checks, XML escaping) and a scheduler dry-run (G2) using stub executors, ensuring the skill is deterministic and functional. Output diagnostics in JSON format.

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.

This tool provides a structured way to run predefined, periodic background workers (e.g., security audits, performance optimization, memory consolidation). It utilizes native scheduling mechanisms to automatically dispatch and re-schedule tasks within a session, ensuring continuous system maintenance and optimal performance monitoring.