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.

This tool parses a structured, multi-shot video script (following the ai-video-script format) and automatically generates a time-coded SRT subtitle file. It calculates cumulative timestamps based on the duration of each shot, ensuring accurate subtitle cues that sync with the voiceover content across the entire sequence. Ideal for the post-production stage of short-form video creation.

An internal, language-agnostic probe designed for investigating complex stack traces when the source language is unknown. It analyzes the provided trace to output structured, language-neutral elements, including failure contract checks, minimal reproduction guidance, and specific patch targets. This tool is vital for robust failure analysis and debugging across diverse codebases.

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 comprehensive skill for generating new design screens from text prompts or images, and iteratively editing existing designs. It incorporates advanced prompt enhancement, design tokens, and professional UI/UX terminology to ensure professional, structured, and context-aware design output for web and mobile platforms.

This skill reads the contents of a UTF-8 text file and outputs the raw bytes verbatim to stdout. It is designed for meta-skills that require round-tripping an artifact through disk for user inspection or manual editing between steps. Unlike standard file readers that prepend line numbers, this tool preserves the original structure, making it ideal for structured data formats like YAML, scripts, or SRT files that need to be accurately parsed downstream.

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 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.

This guide details the principles of observability, teaching developers how to instrument code by implementing structured logging, metrics, and distributed tracing. It ensures that production features emit sufficient telemetry to answer complex operational questions, transforming unknown failures into diagnosable events. Essential for any service running in production.

PBIP is the developer-mode file format for Power BI, transforming the opaque PBIX binary into a human-readable, folder-based structure. This structure is crucial for enabling robust source control (Git diffing), external editing using specialized tools like VS Code, and seamless multi-author collaboration. It standardizes how reports and semantic models are structured for professional BI development pipelines.

This guide provides comprehensive instructions for searching Tabular Editor (TE) documentation and understanding complex configuration files. It details the usage of the `pbi-search` CLI tool, allowing users to query multiple sources (DAX, Power BI, SQLBI) simultaneously. It also covers the structure and best practices for model-specific files like `.tmuo` and application preferences, ensuring efficient data modeling and development.

Provides expert guidance for authoring and editing Tabular Model Definition Language (TMDL) files directly within PBIP projects. This skill is designed for advanced users needing to perform quick, text-level modifications—such as updating descriptions, fixing format strings, or defining measures—when full, dedicated tools (like Tabular Editor CLI) are unavailable. It details critical syntax rules, object nesting, and file structures for managing complex semantic models.