Skill UI

An internal, non-user-invocable agent used by the meta-skill creator process. It executes positive (G3) and negative (G4) smoke tests against a candidate meta-skill definition (SKILL.md). It simulates meta-resolution using LLM-generated fixtures and reports the results in a structured JSON format.

A utility for managing and installing specialized AI skills into the Codex environment. It supports fetching curated skills from predefined sources (like openai/skills) or installing custom capabilities directly from any GitHub repository, including private ones. It handles listing available skills, installing specific modules, and provides robust fallback methods (download/git sparse checkout).

An internal diagnostic tool designed for analyzing Go language panics and complex stack traces. It provides structured guidance, including Go-specific nil/error checks, minimal reproducible `go test` commands, and precise patch targets, enabling highly efficient root cause analysis and debugging for Go projects.

A specialized internal helper for deep meta-stack trace investigation in Python environments. This tool guides users through generating Python-specific root-cause checks, creating minimal reproducible test cases using pytest, and identifying precise targets for defensive patches (e.g., validation or guard clauses). It ensures robust debugging and testing practices.

An internal helper designed for analyzing Rust panics and backtraces. It guides users to perform Rust-specific checks for proper handling of Option and Result types, generates minimal reproducible cargo test commands, and pinpoints exact patch targets for safe code improvements.

This comprehensive guide and tool suite transforms web designs created in Stitch (HTML/CSS) into clean, production-ready React Native components. It meticulously maps HTML elements (like <div>, <button>) to native RN primitives (View, Pressable) and converts complex CSS styles (Flexbox, typography, shadows) into React Native's StyleSheet.create(). It guides developers to follow architectural best practices, including Atomic Design and data decoupling, for scalable cross-platform mobile development.

A utility to generate a static, centered title or end card PNG image. It accepts a main headline, an optional subtitle, background color, and dimensions. It utilizes Pillow, ensuring deterministic rendering suitable for video intros and outros. Supports CJK character wrapping and provides robust font fallbacks, making it reliable for media production pipelines.

This skill converts a single static image (PNG/JPG) into a short, animated MP4 video using the Ken-Burns zoom effect. It is specifically designed as a robust fallback mechanism for automated video workflows. If an upstream AI video generation step fails—often due to content moderation flags (e.g., facial recognition or audio safety issues)—this animator ensures a valid placeholder clip is generated. This allows downstream merging steps to complete the deliverable without interruption, maintaining the narrative flow.

This workflow systematically analyzes deployed AWS resources and Infrastructure-as-Code (IaC) definitions. It collects usage metrics from CloudWatch and Cost Explorer to identify over-provisioning, unused resources, and inefficient configurations (e.g., right-sizing EC2, S3 lifecycle policies). It generates actionable cost optimization recommendations and creates structured GitHub issues for tracking and implementing savings.

This skill allows users to query the current state of various AWS resources (including EC2, S3, Lambda, RDS, VPC, etc.) using natural language. It translates user intent into strictly read-only AWS CLI commands (e.g., `describe-*`, `list-*`). Crucially, this tool can only view data and is contractually incapable of performing any write, modify, delete, or mutation actions, ensuring maximum safety.

This automated workflow performs a comprehensive review against the AWS Well-Architected Framework (WAF). It scans IaC definitions (Terraform, CloudFormation, CDK) and deployed resources to identify compliance gaps, security risks, and optimization opportunities across six core pillars. Findings are structured and automatically generated as actionable GitHub issues with remediation code examples.

An interactive skill designed to guide users through the process of generating, auditing, and improving Best Practice Analyzer (BPA) rules for Power BI semantic models. It follows a double-diamond discovery process, first gathering deep organizational context and model details, and then recommending tailored rules for validation, governance, and compliance.