Skill UI

Generates a structured Epic architecture specification from a Product Requirements Document, guiding senior architects to capture high-level approaches, layered Mermeid diagrams, features, technical enablers, stack choices, and value estimates for TypeScript/Next.js monorepo projects.

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.

This skill analyzes an entire .NET solution (containing multiple projects) to map out a systematic migration path from Oracle to PostgreSQL. It scans for common Oracle indicators (NuGet packages, connection strings, code usage) and classifies every project into categories like MIGRATE, SKIP, or ALREADY_MIGRATED. The output is a structured, actionable Master Migration Plan, helping development teams manage large-scale database transitions efficiently.

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.

Builds production-ready rollout plans covering checks, procedures, verification signals, rollback, and communications for infrastructure or application changes across environments.

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.

Transforms unstructured and messy bug reports—including pasted error logs, voice dictations, and screenshots—into clean, actionable, and highly structured GitHub Issues. It automatically populates key sections like reproduction steps, expected/actual behavior, impact severity, and environment details, ensuring developers receive comprehensive and ready-to-use information.

Automates importing live Azure environments into Terraform by discovering subscriptions, groups, or resource IDs, mapping dependencies, and generating AVM-based IaC files ready for validation and planning.

Bulk migrate repo labels and Project V2 metadata into GitHub org issue fields, converting label-based tags into typed fields, optionally removing old labels, and syncing project field values for cleaner structured issue tracking.