Skill UI

Transforms an exported JSON list of open pull requests into a structured, blockers-first digest. Ideal for standup meetings or triage sessions, this tool identifies which PRs are blocked (due to failing checks, required changes, or staleness) and which are ready to merge, providing a concise summary count for quick status updates.

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

This skill audits a live web page against WCAG standards. It precisely locates accessibility violations, providing a detailed, selector-grounded worklist of necessary fixes without making any changes to the code. Ideal for QA engineers and developers needing a comprehensive, non-destructive report of broken accessibility elements.

Use the `Infra.Api.MaxBundleSize` extension to control the maximum allowed size of the built API Lambda bundle. This is crucial when your project's dependencies or code significantly increase the bundle size beyond the default 4.5 MB limit, or when you need to enforce a stricter size constraint. Configuration must be done using precise byte calculations in webiny.config.tsx.

nano-pdf is a robust command-line interface (CLI) tool designed to modify PDF documents. Instead of requiring complex PDF editing syntax, users can provide natural language instructions (e.g., 'Change the title to X and fix Y') to make precise edits to specific pages of a PDF. This drastically simplifies the content modification process for technical users and automates complex document handling tasks.

This toolkit provides deterministic, programmatic capabilities for working with PDF documents. Users can extract structured data like text and tables, merge multiple PDFs, split documents by page ranges, fill out PDF form fields using JSON data, or generate entirely new PDFs from scratch using libraries like ReportLab. It is designed for explicit, byte-level operations, making it ideal for automation workflows that require precise document manipulation.

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.

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.

A comprehensive guide detailing the selection criteria for core modern system components, including databases (SQL/NoSQL), caching, message queues, and API protocols. It helps architects choose the optimal technology for specific use cases, scalability needs, and architectural patterns.

This skill operates on two layers: the THINK layer enforces rigorous coding discipline (simplicity, surgical changes, goal-driven execution), and the SPEAK layer compresses prose output (caveman/classical modes) for maximum token efficiency. Use it when brevity is explicitly requested or when disciplined, high-precision code generation is required.