Skill UI

This API abstraction catalog provides formalized interfaces for core business logic within the webiny API. It defines standard architectural patterns—including Use Cases, Repositories, and Event Handlers—for managing the complete folder lifecycle. It covers CRUD operations, permission control (FLP), hierarchy resolution, and complex business rules, ensuring a clean separation of concerns and robust code implementation.

A robust Go library for managing complex configuration values from multiple sources. Viper resolves configuration based on a fixed precedence pipeline (flags > environment variables > config files > defaults), ensuring reliable key resolution. It supports binding flags, reading various file types (YAML, JSON, TOML), and handling hot reloads. Essential for building professional, highly configurable Go applications.

An expert playbook detailing DNS rebinding techniques to bypass the Same-Origin Policy (SOP). This method is used in penetration testing to exploit client-side applications that rely on DNS resolution for origin checks, allowing attackers to trick the browser into sending requests to internal, non-publicly accessible services (like cloud metadata endpoints) while maintaining a seemingly trusted origin.

This meta-skill automates the process of handling a GitHub issue by fetching context, delegating the fix to a sub-agent, summarizing the resolution, and finally opening a pull request (PR). It is best suited for small, well-scoped issues with clear acceptance criteria. This pipeline simulates a complete development workflow.

This meta-skill acts as a watchdog for long-running processes, such as CI image builds or overnight model fine-tunes. It utilizes a sub-agent to continuously monitor the output via tmux, automatically diagnose failures, propose corrective fixes, and persist the entire resolution process to memory. It is designed for robust, automated debugging of complex system tasks.

An internal, non-user-invocable tool designed to validate candidate meta-skills. It runs critical G3 (positive) and G4 (negative) smoke tests by simulating meta-resolution using LLM-generated fixtures. This ensures the structural integrity and robustness of the skill in a cross-vendor environment.

This powerful utility concatenates multiple numbered MP4 segments (e.g., 1_*.mp4, 2_*.mp4) into a single, cohesive video file. It supports optional fade transitions and normalizes video parameters, including resolution, FPS, and codec, ensuring a professional and unified output. It is ideal for stitching together short clips produced by automated workflows into a final reel or presentation.

This skill guides the process of routing a complex goal through an AI's scored-memory loop using the `acc_act(runtime="solve")` function. It provides a structured workflow for deliberation, handling intermediate `brain_frame` results, and submitting continuous proposals. It emphasizes systematic reasoning and requires careful resolution of all generated frames to ensure a robust and verifiable solution.

A structured skill designed to generate a comprehensive and highly actionable escalation brief for at-risk customer accounts. It provides internal stakeholders, from Customer Success Managers to C-level executives, with a clear overview of the situation, covering the timeline, root cause, business impact, and proposing a concrete resolution plan to prevent churn and save the account.

This advanced skill downloads Instagram posts, carousels, and Reels at their original, high resolution directly from the CDN. It supports batch processing of multiple URLs, automatically creates organized folders for each post, and stitches multi-slide carousels into a single PDF file, making content archiving and aggregation seamless.

This skill generates a comprehensive, actionable runbook for any service, designed specifically for on-call engineers. It structures critical incident response procedures, including alert definitions, escalation matrices, diagnostic commands, and rollback steps, significantly reducing Mean Time To Resolution (MTTR). It provides a single source of truth for responding confidently during high-stress incidents.

This skill provides utilities to manage and drain the deliberation queue (brain_frames) of the ACC system. It allows users to list waiting frames, retrieve necessary context, perform deliberation on open tasks, and submit continuations using `acc_act(runtime="continue")`. It is essential for checkpointed work saved by headless runs, ensuring continuous task resolution and calibration against predicted outcomes in complex AI workflows.