Transforms lessons learned into domain-organized memory instructions (global or workspace). Syntax: `/remember [>domain [scope]] lesson clue` where scope is `global` (default), `user`, `workspace`, or `ws`.
Convert Markdown files to HTML similar to `marked.js`, `pandoc`, `gomarkdown/markdown`, or similar tools; or writing custom script to convert markdown to html and/or working on web template systems like `jekyll/jekyll`, `gohugoio/hugo`, or similar web templating systems that utilize markdown documents, converting them to html. Use when asked to "convert markdown to html", "transform md to html", "render markdown", "generate html from markdown", or when working with .md files and/or web a templating system that converts markdown to HTML output. Supports CLI and Node.js workflows with GFM, CommonMark, and standard Markdown flavors.
Shuffle repetitive JSON objects safely by validating schema consistency before randomising entries.
Plan a multi-file refactor with proper sequencing and rollback steps
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
Guides the Copilot CLI on how to use the WorkIQ CLI/MCP server to query Microsoft 365 Copilot data (emails, meetings, docs, Teams, people) for live context, summaries, and recommendations.
Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/
Get best practices for developing applications with Spring Boot.
Expert 10x engineer with comprehensive knowledge of web development, internet protocols, and web standards. Use when working with HTML, CSS, JavaScript, web APIs, HTTP/HTTPS, web security, performance optimization, accessibility, or any web/internet concepts. Specializes in translating web terminology accurately and implementing modern web standards across frontend and backend development.
Plain-English translation layer for non-technical Copilot CLI users. Translates every approval prompt, error message, and technical output into clear, jargon-free English with color-coded risk indicators.
List my issues in the current repository
Windows App Development CLI (winapp) for building, packaging, and deploying Windows applications. Use when asked to initialize Windows app projects, create MSIX packages, generate AppxManifest.xml, manage development certificates, add package identity for debugging, sign packages, publish to the Microsoft Store, create external catalogs, or access Windows SDK build tools. Supports .NET (csproj), C++, Electron, Rust, Tauri, and cross-platform frameworks targeting Windows.
Transform this Python script into a polished, beginner-friendly project by refactoring the code, adding clear instructional comments, and generating a complete markdown tutorial.
Get best practices for NUnit unit testing, including data-driven tests
Ready-to-use prompts for comprehensive .NET framework upgrade analysis and execution
Universal SQL code review assistant that performs comprehensive security, maintainability, and code quality analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). Focuses on SQL injection prevention, access control, code standards, and anti-pattern detection. Complements SQL optimization prompt for complete development coverage.
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
Run pytest tests with coverage, discover lines missing coverage, and increase coverage to 100%.
Ensure .NET/C# code meets best practices for the solution/project.
Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.