Skill UI

Generates customizable prompts to scan a codebase, identify high-quality exemplars, and document patterns across languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable depth, categorization, and documentation formats.

This toolkit provides comprehensive automation capabilities for Confluence platforms via Rube MCP. It allows users to automate the entire documentation lifecycle, including creating and updating pages, performing advanced content searches using CQL, managing spaces, and applying labels efficiently. Ideal for teams needing to streamline knowledge base maintenance and content governance.

Converts text-based documentation into structured Markdown by following explicit instructions, documented options, or reference files, supporting header levels, patterns, platforms, and cleanup steps for polished technical documents.

Guides an AI pair to document application needs, access patterns, and scaling concerns while producing Azure Cosmos DB NoSQL requirements and data model artifacts using established best practices in a structured workflow.

Generates an AI-ready Architectural Decision Record template with precise sections, coded bullet conventions, front-matter metadata, and sequential storage under /docs/adr to keep decision documentation structured for both machines and people.

This tool automates the entire workflow of creating a GitHub Pull Request (PR) directly from a structured specification file. It analyzes the template, extracts requirements, checks for existing PRs, creates the draft, updates the body and title with specification details, and finally transitions the PR state to 'Ready for Review'. This significantly streamlines the feature request submission process, ensuring all necessary documentation and metadata are included.

Describes how to analyze a repository, prioritize key docs, and author an llms.txt entry so LLMs immediately understand the project scope, documentation clusters, and configuration artifacts they should consult.

Template for creating time-boxed technical spike documents that capture research questions, deliverables, success criteria, and decision rationale before committing to implementation.

This tool acts as an expert technical documentation specialist, transforming lengthy, verbose official documentation from URLs into concise and actionable 'tldr' pages. It extracts the most common command patterns, adheres strictly to the tldr-pages markdown format, and provides clear, example-driven command references, making complex tools easy to reference.

This guide outlines the process and mandatory structure for documenting defects found during an Oracle-to-PostgreSQL migration. It details how to write actionable bug reports, covering required elements like Severity, Root Cause, Scenario, and Solution. It emphasizes treating Oracle as the source of truth and handling data layer nuances (e.g., NULL vs. empty string) to ensure accurate defect tracking and remediation.

Guides Fedora experts through confirming release information, executing systemctl/journalctl/dnf diagnostics, applying SELinux-aware remediation, and documenting verification plus rollback steps for distro-specific issues.

Analyzes diverse codebases to auto-detect technology stacks, microservices/monorepo traits, and frontend presence, then documents consistent folder conventions, visualization options, and template guidance to keep large projects organized.