github/awesome-copilot▌

As an AI engineering assistant, your role when building premium frontend experiences goes beyond outputting functional HTML and CSS. You must architect immersive digital environments. This skill provides the blueprint for generating highly intentional, award-level web applications that prioritize aesthetic quality, deep interactivity, and flawless performance.

Comprehensive best practices guide for building production-ready Spring Boot applications. \n \n Covers project structure, dependency injection patterns, and configuration management including externalized config, type-safe properties, and environment profiles \n Details web layer design with RESTful APIs, DTOs, validation, and global exception handling \n Addresses service layer statelessness, transaction management, and data access patterns using Spring Data JPA with custom queries and project

Validate .NET/C# code against comprehensive best practices for your solution and project. \n \n Covers 10+ practice areas including XML documentation, design patterns, dependency injection, async/await, testing standards, and error handling \n Enforces specific patterns: primary constructors for DI, Command Handler pattern with generics, interface segregation, and Factory pattern for object creation \n Includes resource management with ResourceManager for localization, structured logging via Mic

Multi-agent pipeline that generates compilable, passing unit tests across eight programming languages. \n \n Orchestrates a Research → Plan → Implement workflow: researcher analyzes the codebase, planner creates phased test strategy, implementer writes and validates tests across phases \n Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more; auto-detects language, testing framework, build commands, and project structure \n Includes builder, tester, fixer, and linter subagents th

Generate Excalidraw diagrams from natural language descriptions in multiple formats. \n \n Supports nine diagram types: flowcharts, relationship diagrams, mind maps, architecture diagrams, data flow diagrams, swimlane business flows, class diagrams, sequence diagrams, and ER diagrams \n Outputs valid .excalidraw JSON files that open directly in Excalidraw or the VS Code extension \n Includes layout guidelines, element count recommendations, and color schemes for consistent visual design \n Optio

This skill provides expert guide for testing Spring Boot 4 applications with modern patterns and best practices.

Generates comprehensive README.md files for projects with professional structure and formatting. \n \n Analyzes entire project workspace to create contextually accurate, well-organized documentation \n Follows proven open-source README patterns with clear sections for setup, usage, and key features \n Uses GitHub Flavored Markdown and admonition syntax for enhanced readability and visual hierarchy \n Balances detail with conciseness, avoiding unnecessary sections and excessive emoji while mainta

Generate ASCII art diagrams from PlantUML text syntax for terminal and documentation use. \n \n Supports six diagram types: sequence, class, activity, state, component, use case, and deployment diagrams \n Two output formats: pure ASCII ( -txt ) and Unicode-enhanced ASCII ( -utxt ) with box-drawing characters for improved readability \n Works with PlantUML installation or standalone JAR; outputs to .atxt or .utxt files ready for terminals, READMEs, and version control \n Command-line options inc

Entity Framework Core best practices guide covering design, performance, security, and testing patterns. \n \n Covers eight core areas: DbContext design, entity modeling, performance optimization, migrations, querying strategies, change tracking, security, and testing approaches \n Emphasizes practical patterns like DbContextFactory, IEntityTypeConfiguration separation, AsNoTracking() for read-only queries, and compiled queries for frequently executed operations \n Includes migration best practi

Universal SQL performance optimization across MySQL, PostgreSQL, SQL Server, Oracle, and other databases. \n \n Covers query analysis, index strategy design, subquery optimization, and JOIN tuning with before/after examples for each technique \n Addresses common anti-patterns including SELECT *, function calls in WHERE clauses, inefficient pagination, and correlated subqueries \n Provides database-agnostic guidance on batch operations, temporary tables, covering indexes, and partial indexes \n I

Generate Playwright tests from scenarios using interactive browser exploration and validation. \n \n Guides you through step-by-step test creation: scenario review, browser exploration, element inspection, interaction validation, and final test generation \n Integrates with Playwright MCP tools to inspect page elements, capture selectors, and validate interactions before writing test code \n Generates TypeScript tests using @playwright/test framework and automatically saves them to the tests dir

Analyze current repository context and suggest relevant Agent Skills from the GitHub awesome-copilot repository that are not already available in this repository. Agent Skills are self-contained folders located in the skills folder of the awesome-copilot repository, each containing a SKILL.md file with instructions and optional bundled assets.

JUnit 5 best practices for standard and data-driven unit testing with practical patterns. \n \n Covers test structure using Arrange-Act-Assert pattern, lifecycle annotations ( @BeforeEach , @AfterEach , @BeforeAll , @AfterAll ), and naming conventions with @DisplayName \n Parameterized testing via @ParameterizedTest with multiple sources: @ValueSource , @MethodSource , @CsvSource , @CsvFileSource , and @EnumSource \n Assertion strategies including static Assertions methods, AssertJ fluent syntax

Iterative evaluation and refinement patterns for improving AI agent outputs through self-critique loops. \n \n Provides three core patterns: basic reflection (self-critique loops), evaluator-optimizer (separated generation and evaluation), and code-specific test-driven refinement \n Supports multiple evaluation strategies including outcome-based assessment, LLM-as-judge comparison, and rubric-based scoring with weighted dimensions \n Includes practical Python implementations with structured JSON

Java method extraction refactoring for improved readability and maintainability. \n \n Identifies methods exceeding complexity thresholds (LOC > 15, statements > 10, cyclomatic complexity > 10) and extracts logical code blocks into focused helper methods \n Produces fully compilable Java 17 code with descriptive method names and single-line documentation comments \n Preserves all original functionality while reducing cognitive load and improving testability through smaller, single-responsibility

Diagnose and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices. \n \n Provides step-by-step triage plans using systemctl, journalctl, and pacman to identify root causes \n Delivers copy-paste-ready remediation commands with verification steps after each major change \n Addresses kernel updates, reboot considerations, and rollback procedures for safe recovery \n Accepts optional system snapshots and constraint inputs to tailor diagnosis to your environment \n

Professional UI/UX design creation in Penpot with MCP tools, design systems, and accessibility standards. \n \n Four MCP tools enable design creation, modification, export, and API access within Penpot's plugin context \n Includes discovery workflow to identify existing design systems, components, and tokens before building new designs \n Covers responsive layouts for mobile (375×812), desktop (1440×900), and platform-specific guidelines (iOS, Android, Material Design) \n Provides default design

Web-based game engine and game development using HTML5 Canvas, WebGL, and JavaScript. \n \n Covers 2D and 3D game creation with frameworks like Phaser, Three.js, Babylon.js, and A-Frame \n Includes game loop implementation, physics, collision detection, sprite management, tilemaps, and rendering techniques \n Supports multiple input methods: keyboard, mouse, touch, and gamepad controls \n Provides starter templates for breakout games, platformers, maze games, and 3D experiences \n Addresses audi

Standardized git commits using Conventional Commits specification with intelligent diff analysis and message generation. \n \n Auto-detects commit type (feat, fix, docs, refactor, perf, test, build, ci, chore, revert) and scope from actual code changes \n Generates semantic commit messages following conventional format with optional body and footer sections \n Intelligently stages files for logical grouping when needed, with support for pattern-based and interactive staging \n Detects breaking c

Generate GitHub Copilot instruction files by analyzing actual codebase patterns and technology versions. \n \n Scans projects to detect exact language, framework, and library versions, then generates instructions that enforce compatibility with those specific versions \n Analyzes existing code patterns for naming conventions, architecture style, error handling, testing approaches, and documentation standards to ensure Copilot-generated code matches established practices \n Supports multiple tech

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Spring Boot development patterns and idioms tailored for Kotlin applications. \n \n Use primary constructors for dependency injection, data class for DTOs, and the kotlin-jpa plugin to automatically open entity classes without boilerplate. \n Organize code by feature/domain rather than layer; leverage Kotlin's null-safety to clearly define optional vs. required entity fields. \n Apply @ConfigurationProperties with data class for type-safe, immutable configuration; use application.yml and Spring

Browser-based visual whiteboard for sketching ideas and collaborating with Copilot through drawings and sticky notes. \n \n Opens an interactive canvas on the user's desktop where they can draw, sketch, and add sticky notes, then share back to Copilot via a \"Share with Copilot\" button \n Reads whiteboard content through PNG snapshots (visual interpretation of sketches, diagrams, and layout) and optional JSON clipboard data (precise text from sticky notes and labels) \n Designed for non-technic

Jest testing best practices for JavaScript and TypeScript projects with mocking, async handling, and React patterns. \n \n Organize tests with descriptive names in nested describe blocks, using .test.ts / .test.js files placed alongside source code or in __tests__ directories \n Mock external dependencies with jest.mock() , jest.spyOn() , and mockImplementation() , resetting between tests to prevent state leakage \n Handle async code with promises, async/await , and resolves / rejects matchers;

Dependabot is GitHub's built-in dependency management tool with three core capabilities:

Structured prompt template for generating standardized conventional commit messages. \n \n Provides XML-formatted workflow guiding users through staging changes, inspecting diffs, and constructing commits with type, scope, description, body, and footer fields \n Includes validation rules enforcing Conventional Commits specification compliance, with allowed types (feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert) \n Offers six practical examples covering common commit patter

Analyzes codebases to generate comprehensive architectural documentation with diagrams and implementation patterns. \n \n Auto-detects technology stacks (.NET, Java, React, Angular, Python, Node.js, Flutter) and architectural patterns (Clean Architecture, Microservices, Layered, MVVM, Hexagonal, Event-Driven, Serverless, Monolithic) \n Generates C4, UML, Flow, or Component diagrams at multiple abstraction levels showing subsystems, dependencies, and data flow \n Documents core components, layers

Diagnose and resolve Fedora Linux issues with dnf, systemd, and SELinux guidance. \n \n Accepts optional Fedora release version and problem constraints to tailor troubleshooting scope \n Provides step-by-step triage plans using systemctl, journalctl, and dnf with copy-paste-ready commands \n Includes verification steps after each remediation and addresses SELinux and firewalld considerations \n Offers rollback and cleanup procedures to safely undo changes \n

Browser automation and testing toolkit for local web applications using Playwright. \n \n Supports core browser interactions including navigation, form filling, clicking, dropdown selection, and dialog handling \n Includes verification capabilities for element presence, text content, visibility, URLs, and responsive design across viewports \n Provides debugging tools: screenshot capture, console log inspection, network request monitoring, and error handling patterns \n Works with Node.js environ

Visual inspection and source-level fixing of website design issues across responsive, accessibility, and layout concerns. \n \n Detects layout overflow, alignment problems, responsive breakpoints, accessibility contrast issues, and visual inconsistencies across mobile (375px), tablet (768px), desktop (1280px), and wide (1920px) viewports \n Supports static sites, SPAs (React/Vue/Angular/Svelte), full-stack frameworks (Next.js/Nuxt/SvelteKit), and CMS platforms with automatic framework and stylin

Comprehensive safety analysis and improvement framework for AI prompts with detailed assessment methodologies. \n \n Evaluates prompts across eight dimensions: safety, bias detection, security, effectiveness, best practices compliance, pattern analysis, technical robustness, and performance optimization \n Provides structured analysis reports with risk scoring, critical issue identification, and strength assessment across all evaluation criteria \n Delivers improved prompt versions with specific

Programmatic Power Automate flow management via FlowStudio MCP server. \n \n List, read, and monitor cloud flows directly from the Power Automate API without UI or manual steps \n Inspect run history, per-action error details, and trigger outputs; resubmit failed runs or cancel active executions \n Update flow definitions, manage connections, and retrieve HTTP-triggered flow callback URLs \n Requires FlowStudio MCP subscription with JWT token authentication; Python or Node.js helper functions pr

Automate GraalVM native image configuration, build, and error resolution for Java applications. \n \n Detects project structure (Maven/Gradle) and framework (Spring Boot, Quarkus, Micronaut) to apply framework-specific native image setup \n Adds GraalVM Native Build Tools plugins with appropriate configuration profiles and iteratively resolves build errors \n Handles common native image issues including reflection, resource access, JNI, and dynamic proxy configuration through generated metadata

Time-boxed technical spike documents for researching critical development decisions before implementation. \n \n Generates structured markdown spike files with clear objectives, research questions, investigation plans, and decision frameworks \n Supports six spike categories: API Integration, Architecture & Design, Performance & Scalability, Platform & Infrastructure, Security & Compliance, and User Experience \n Includes built-in checklists for research tasks, success criter

Complete reference for migrating UWP apps to WinUI 3 with before/after code examples. \n \n Maps all namespace changes from Windows.UI.Xaml.* to Microsoft.UI.Xaml.* , plus threading, windowing, and dialog APIs \n Covers the three most common Copilot mistakes: ContentDialog without XamlRoot, MessageDialog usage, and CoreDispatcher patterns \n Includes migration tables for window management, pickers, background tasks, settings, and GetForCurrentView() replacements \n Provides a 15-item migration c

Generates concise, actionable meeting minutes for internal meetings under 60 minutes with standardized metadata, decisions, and assigned action items. \n \n Captures attendees, agenda, decisions with rationale, and action items with owner and due date for immediate task tracking \n Follows a strict schema covering metadata, decisions, action items, notes by agenda item, parking lot, risks, and follow-ups \n Accepts multiple input formats: transcripts, recordings, slides, raw notes, or live meeti

Analyzes codebases to generate detailed technology stack blueprints with architectural documentation. \n \n Supports auto-detection across multiple platforms (.NET, Java, JavaScript, React, Python) or focuses analysis on a specified technology type \n Configurable depth levels (Basic to Implementation-Ready) with optional version info, licensing details, usage patterns, and coding conventions \n Generates architecture diagrams, technology relationship maps, and data flow visualizations to docume

Expert-guided triage and resolution for Debian Linux system issues using apt, systemd, and AppArmor. \n \n Diagnoses problems across package management, service health, and security policies with Debian-native tools \n Provides step-by-step triage plans using systemctl , journalctl , apt , and dpkg with copy-paste-ready commands \n Includes verification steps after remediation and rollback guidance for safe recovery \n Accounts for AppArmor and firewall considerations in troubleshooting workflow

ASP.NET Minimal API endpoints with automatic OpenAPI/Swagger documentation and strong typing. \n \n Organize endpoints using MapGroup() , endpoint filters, and feature-based folder structures for scalability \n Define explicit request/response DTOs with validation attributes; use record types and TypedResults for type safety \n Leverage .NET 9 built-in OpenAPI support with operation summaries, descriptions, operationIds, and property-level documentation via [Description()] \n Apply document and

Consolidates mature learnings from domain memory files into instruction files with quality-assured merging. \n \n Operates on two scopes: global (VS Code user prompts) and workspace (project-specific instructions), with configurable domain targeting \n Requires explicit user approval before merging, presenting proposed memories for review with proposed locations in the instruction hierarchy \n Enforces a 10/10 quality bar during merge: zero knowledge loss, minimal redundancy, and maximum scannab

Comprehensive SQL security, performance, and quality analysis across MySQL, PostgreSQL, SQL Server, and Oracle databases. \n \n Analyzes SQL injection vulnerabilities, access control issues, and sensitive data exposure with parameterized query examples for each database platform \n Reviews query performance through index strategy, join optimization, and anti-pattern detection (N+1 queries, function misuse in WHERE clauses, overuse of DISTINCT) \n Evaluates code quality including naming conventio

Diagnose and resolve CentOS issues with RHEL-compatible commands and SELinux awareness. \n \n Confirms CentOS release type (Stream vs. legacy) and provides triage steps using systemctl, journalctl, dnf/yum, and log inspection \n Includes copy-paste-ready remediation commands with verification steps after each major change \n Addresses SELinux policies and firewalld configuration as part of troubleshooting workflow \n Provides rollback and cleanup procedures to safely revert changes \n

Build optimized, secure multi-stage Dockerfiles for any language or framework. \n \n Structures builds with separate builder and runtime stages, copying only necessary artifacts to minimize final image size and attack surface \n Emphasizes layer caching optimization by ordering commands from least to most frequently changing, combined with .dockerignore and command consolidation \n Recommends minimal base images (Alpine, distroless, or official slim variants) with exact version pinning for repro

Surgical code refactoring to improve maintainability without changing behavior. \n \n Covers 10 common code smells: long methods, duplication, large classes, long parameter lists, feature envy, primitive obsession, magic numbers, nested conditionals, dead code, and inappropriate intimacy \n Includes extract method, extract class, and introduce type safety patterns with before/after examples \n Provides design pattern refactoring strategies: Strategy, Chain of Responsibility, and others for repla

Deploy and manage MCP-based declarative agents across Microsoft 365 with admin center governance, role-based access, and organizational distribution. \n \n Supports five agent types: organization-published, creator-shared, Microsoft-native, external partner, and frontier agents, each with distinct deployment and approval workflows \n Provides admin controls for publishing, deploying to user groups, blocking, and removing agents; requires AI Admin role for full management \n Includes MCP-specific

Scaffolds production-ready Rust MCP server projects with tools, prompts, resources, and full test coverage. \n \n Generates complete project structure with Cargo.toml, handler implementations, and integration tests using the official rmcp SDK \n Supports multiple transport types (stdio, SSE, HTTP) with configurable features and async/await patterns throughout \n Includes macros for tool definition ( #[tool] , #[tool_router] , #[tool_handler] ) and type-safe parameter handling via schemars::JsonS

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Comprehensive XUnit testing guide covering standard facts, data-driven theories, and best practices. \n \n Covers test structure using Arrange-Act-Assert pattern, naming conventions, and fixture-based setup/teardown with IClassFixture<T> and ICollectionFixture<T> \n Explains data-driven testing with [Theory] combined with [InlineData] , [MemberData] , and [ClassData] attributes, plus custom data attribute creation \n Details assertion methods for equality, collections, regex patterns,

Four-phase framework for onboarding enterprise customers from contract to value realization. The goal isn't just go-live — it's sustained adoption that doesn't cliff at Week 12.

Build and scale partner ecosystems that drive revenue and platform adoption. These aren't theory — they're patterns from building partner programs that drove 8-figure ARR and observing partnerships with real economic commitment.

Build self-serve acquisition and expansion motions. But first, figure out if PLG is even the right motion for your product.

Architects design workloads by integrating platform services, functionality, and code to meet both functional and nonfunctional requirements. To design effective workloads, you must understand these requirements and select topologies and methodologies that address the challenges of your workload's constraints. Cloud design patterns provide solutions to many common challenges.

Analyze a single target project to identify data access artifacts that require integration testing, then produce a structured, actionable testing plan.

Deploy static sites and serverless APIs to Azure with local development emulation and CLI automation. \n \n Provides local emulator ( swa start ) with API proxy, authentication simulation, and framework auto-detection for React, Vue, Angular, Next.js, and others \n Supports Azure Functions backends with Node.js, Python, and .NET runtimes; configure via staticwebapp.config.json for routes, auth rules, and headers \n Includes deployment workflow: swa init (auto-detects framework), swa build , swa

Enforce repository contribution guidelines before creating issues, branches, commits, or pull requests. \n \n Searches repository documentation (README, CONTRIBUTING.md, templates) to identify required contribution workflows, branch naming conventions, and commit message formats \n Applies security boundaries that prevent executing arbitrary commands, accessing files outside the repository, making network requests, or including secrets in contributions \n Treats issue and PR templates as formatt

Structured Architectural Decision Record generator with AI-optimized formatting and sequential file management. \n \n Generates standardized ADR documents with front matter, context, decision rationale, and consequences organized into positive and negative outcomes \n Requires four inputs (decision title, context, decision, alternatives, stakeholders) with validation to prompt for missing information before generation \n Uses coded bullet-point system (3-4 letter codes + 3-digit numbers) across

Systematic framework for diagnosing and resolving Power BI performance bottlenecks across models, reports, and infrastructure. \n \n Covers four diagnostic areas: model design and DAX efficiency, report layout and visual complexity, infrastructure capacity, and data source connectivity \n Includes step-by-step troubleshooting methodology with issue classification, baseline metrics collection, and targeted diagnosis workflows \n Provides concrete optimization patterns for DAX formulas, storage mo

EditorConfig file generator that analyzes projects and creates comprehensive editor configuration based on best practices. \n \n Analyzes project structure and file types to infer languages and technologies, then generates targeted configuration rules for each file type \n Applies universal best practices including UTF-8 charset, LF line endings, trailing whitespace removal, and final newline insertion \n Respects user preferences (spaces vs. tabs, indentation size) while flagging any conflicts

Expert technical writer for Diátaxis-framework documentation creation across tutorials, how-to guides, reference, and explanation formats. \n \n Structures documentation into four distinct types: tutorials for learning, how-to guides for problem-solving, reference for technical specifications, and explanations for conceptual understanding \n Follows a three-step workflow: clarify document type and audience, propose a detailed outline for approval, then generate full markdown content \n Maintains

You're a senior technical analyst and storyteller with expertise in repository archaeology, code pattern analysis, and narrative synthesis. Your mission is to transform raw repository data into compelling technical narratives that reveal the human stories behind the code.

Socratic mentoring for junior developers through guided questions, never direct answers. \n \n Guides learners through structured questioning rather than solving problems, building autonomy and reasoning skills across fundamentals, debugging, testing, security, and architecture \n Includes the PEAR Loop (Plan, Explore, Analyze, Rewrite) for Copilot-assisted learning and teaches prompt engineering with the CTEX formula for better AI interactions \n Provides progressive clue systems from light gui

Interprets shorthand, pseudo-code, and natural language descriptions to generate production-quality code. \n \n Assesses collaborator technical expertise (high/medium/low confidence) to determine how much interpretation and correction is needed \n Processes special shorthand notation marked with start-shorthand and end-shorthand boundaries; always removes ()=> lines when implementing \n Applies expert judgment to translate incomplete or imperfect descriptions into robust code, handling typos, te

Create GitHub Issues automatically from implementation plan phases. \n \n Analyzes implementation plan files to identify phases and generates one issue per phase \n Uses feature_request.yml or chore_request.yml templates, with fallback to default issue format \n Checks for existing issues before creation to avoid duplicates, and updates existing issues when needed \n Includes phase details, requirements, and context in issue descriptions with appropriate labels \n

Generate Dockerfile and .dockerignore files customized for ASP.NET Core containerization. \n \n Accepts configurable settings for .NET version, Linux distribution (Debian, Alpine, Ubuntu, Chiseled, Azure Linux), base images, ports, and environment variables \n Creates multi-stage Dockerfile with SDK build stage and optimized runtime stage, following Microsoft best practices for .NET containerization \n Generates .dockerignore file with sensible defaults (bin/, obj/, .git/, etc.) plus custom patt

Automated website exploration and test case generation using Playwright. \n \n Navigates to provided URLs and systematically identifies 3-5 core features and user flows \n Documents UI elements, their locators, and expected outcomes for each interaction \n Generates test cases based on exploration findings for immediate use in testing workflows \n Manages browser context lifecycle automatically, closing sessions upon completion \n

Ask an AI assistant what files it needs before answering your question. \n \n Prompts the assistant to identify required and optional files for context before attempting to answer \n Structures output into four categories: must-see files, helpful files, already-seen files, and remaining uncertainties \n Helps developers avoid incomplete answers by ensuring the assistant has examined relevant code upfront \n Reduces back-and-forth by clarifying dependencies and gaps in context before the actual q

Analyze codebases and map task-relevant files before implementing changes. \n \n Automatically searches for related files, dependencies, tests, and similar code patterns to establish full context \n Generates structured markdown output with files to modify, dependency relationships, test coverage, and reference patterns \n Includes risk assessment checklist for breaking changes, migrations, and configuration updates \n Designed as a pre-implementation step to prevent missed dependencies and unin

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Analyze current repository context and suggest relevant copilot-instruction files from the GitHub awesome-copilot repository that are not already available in this repository.

VS Code extension command contribution patterns and naming conventions. \n \n Defines two command types: regular commands (accessible in Command Palette with required category and title ) and Side Bar commands (prefixed with underscore and suffixed with #sideBar , requiring an icon ) \n Side Bar commands support visibility rules via enablement and when conditions, with positioning controlled through group and order attributes \n All commands must define a title ; icons are optional for regular c

Systematic workflow for identifying missing features, prioritizing them, and creating detailed GitHub issue specifications. \n \n Guides through five structured phases: project understanding, gap analysis, prioritization scoring, specification development, and issue creation \n Uses a quantitative priority matrix (User Impact × Strategic Alignment / Implementation Effort × Risk Level) to rank features objectively \n Includes work distribution optimization to maximize independent development stre

Comprehensive test planning and quality assurance documentation generator aligned with ISTQB and ISO 25010 standards. \n \n Generates three core deliverables: test strategy, test issues checklist, and QA plan from feature PRDs and technical breakdowns \n Applies ISTQB test design techniques (equivalence partitioning, boundary value analysis, decision table testing, state transition testing) and test types (functional, non-functional, structural, change-related) \n Maps all eight ISO 25010 qualit

Convert Markdown documents to HTML using marked.js, pandoc, gomarkdown, or static site generators. \n \n Supports multiple conversion tools: marked.js for Node.js/CLI workflows, pandoc for cross-format conversion, gomarkdown for Go projects, and Jekyll/Hugo for static site generation \n Handles all standard Markdown flavors including CommonMark, GitHub Flavored Markdown, and extended syntax for tables, code blocks, math expressions, and collapsed sections \n Provides CLI and programmatic workflo

Generate and maintain file indexes in markdown documents by scanning folders and updating or creating organized file listings. \n \n Scans target markdown files and discovers files matching specified patterns in designated folders \n Generates three table format options: simple lists with descriptions, detailed tables with type/size metadata, or categorized sections grouped by file type \n Automatically identifies existing index sections by heading patterns (\"index\", \"files\", \"contents\") a

Refactor Python scripts into beginner-friendly projects with instructional comments and markdown tutorials. \n \n Applies PEP 8 style guide compliance and standard Python best practices to improve code quality \n Adds instructional comments throughout the code explaining logic, reasoning, and importance for learners \n Generates a complete README.md tutorial with project overview, setup instructions, code breakdown, usage examples, and sample output \n Renames unclear variables and functions for

Architectural planning prompt for translating product requirements into modular, scalable technical specifications. \n \n Generates comprehensive Epic Architecture Specifications in Markdown, including system diagrams, feature lists, and technology stack recommendations \n Enforces domain-driven design patterns with Docker containerization, TypeScript/Next.js, Turborepo monorepos, tRPC APIs, and Stack Auth authentication \n Produces Mermaid diagrams spanning user, application, service, data, and

Automatically create GitHub issues for specification requirements not yet implemented in code. \n \n Analyzes specification files to extract all requirements, then checks codebase implementation status for each one \n Searches existing issues to prevent duplicates before creating new feature request issues \n Generates issues with requirement IDs, detailed descriptions, implementation guidance, and acceptance criteria \n Scans related specification files and code patterns to verify whether requi

Comprehensive rollout plan generator with preflight checks, phased deployment, verification signals, and rollback procedures. \n \n Generates structured plans covering executive summary, prerequisites, preflight validation, step-by-step deployment phases, and post-deployment tasks \n Includes immediate, short-term, medium-term, and long-term verification signals to confirm deployment success across multiple time windows \n Provides rollback decision criteria, automated revert procedures, and pos

Complete Power Platform custom connector generator with MCP integration, schema validation, and Copilot Studio compliance. \n \n Generates four core files (apiDefinition.swagger.json, apiProperties.json, script.csx, readme.md) with built-in MCP protocol support and JSON-RPC 2.0 handling for Copilot Studio agents \n Validates schemas against Copilot Studio constraints: no reference types, single type values only, primitive types, and resources as tool outputs \n Supports six generation modes: com

Automate form filling with Playwright MCP, including text input, date/time selection, and file uploads. \n \n Supports multiple form field types: text inputs, date pickers, time selectors, and file uploads \n Navigates to specified URLs and populates forms with provided data without automatic submission \n Includes review checkpoint before form submission to allow manual verification of filled fields \n

Semantic modeling assistant for building optimized Power BI data models with DAX, relationships, and best practices. \n \n Connects to active Power BI models (Desktop or Fabric) to analyze current structure before providing guidance on star schemas, relationships, measures, and naming conventions \n Covers core modeling tasks: creating DAX measures, configuring table relationships and cardinality, implementing row-level security (RLS), and optimizing performance \n Includes model quality assessm

Automated code review and refactoring against project-specific coding guidelines and instructions. \n \n Reads coding standards from .github/instructions/*.md and .github/copilot-instructions.md to establish project conventions \n Performs comprehensive code review and refactoring to align with defined guidelines while preserving file structure \n Validates that existing tests continue to pass after refactoring changes \n Maintains code integrity by keeping files intact rather than splitting or

Production-ready Dataverse SDK patterns with error handling, batch operations, and optimization techniques. \n \n Demonstrates exponential backoff retry logic for transient errors, batch CRUD operations with error recovery, and OData query optimization using filters, selects, expands, and paging with correct logical names \n Covers table metadata creation and inspection, custom column definitions with IntEnum option sets, and cache flushing strategies when schema changes \n Includes configuratio

Expert-level browser automation, debugging, and performance analysis via Chrome DevTools protocol. \n \n Four tool categories cover navigation, interaction, debugging, and performance profiling across 25+ commands \n Snapshot-first workflow provides element UIDs for reliable targeting; screenshots offer visual verification \n Network request inspection, console message analysis, and JavaScript evaluation support troubleshooting and validation \n Performance tracing with Core Web Vital analysis i

Create a detailed plan for this refactoring task.

Polish and refine agent prompt files against proven best practices. \n \n Requires a prompt file as input; will request one if not provided \n Preserves front matter, encoding, and markdown structure while improving clarity and organization \n Corrects spelling, grammar, and wording issues without altering the original intent \n Applies patterns from successful prompts to strengthen structure and effectiveness \n

Comprehensive C#/.NET design pattern analysis with actionable improvement recommendations. \n \n Evaluates six core patterns: Command, Factory, Dependency Injection, Repository, Provider, and Resource patterns against GoF and SOLID principles \n Analyzes architecture compliance including namespace conventions, project separation, async/await patterns, and structured logging implementation \n Reviews code quality across security, testability, maintainability, and performance dimensions with speci

Add Azure App Insights telemetry to ASP.NET Core or Node.js webapps hosted in Azure. \n \n Supports ASP.NET Core apps (with auto-instrumentation option for App Service) and Node.js apps \n Guides you through creating an App Insights resource via Bicep templates or Azure CLI \n Provides language-specific code modification guides for C#, JavaScript/TypeScript, and Python \n Requires identifying the app's programming language, framework, and hosting environment before instrumentation begins \n

Generates formal, AI-optimized specifications for GitHub Actions workflows to standardize documentation and enable maintenance. \n \n Produces structured markdown specifications with execution flow diagrams, job dependency matrices, and requirement tables designed for token efficiency \n Covers functional, security, and performance requirements alongside input/output contracts, error handling strategies, and quality gates \n Includes sections for monitoring, compliance, edge cases, and change ma

Javadoc best practices for documenting Java types, methods, and members. \n \n Public and protected members require Javadoc comments; package-private and private members are encouraged, especially for complex code \n Use standard tags: @param for parameters, @return for return values, @throws for exceptions, @see for cross-references, and @since for version tracking \n First sentence serves as summary description and should end with a period; parameter descriptions start lowercase without period

Structured PRD generation prompt for breaking down Epics into detailed feature specifications. \n \n Generates complete Product Requirements Documents in markdown format with standardized sections covering goals, user personas, stories, functional and non-functional requirements, and acceptance criteria \n Prompts for clarifying questions when insufficient information is provided, ensuring comprehensive feature definition before engineering handoff \n Organizes output into a consistent file stru

Create structured, machine-readable implementation plans for features, refactoring, upgrades, and infrastructure changes. \n \n Generates deterministic, AI-executable plans with discrete phases, atomic tasks, and explicit completion criteria \n Enforces strict template compliance with standardized identifier prefixes (REQ-, TASK-, GOAL-, etc.) and machine-parseable formats \n Includes mandatory sections for requirements, implementation phases, dependencies, testing, risks, and alternatives \n Su

Complete TypeScript MCP server project generator with tools, resources, and transport configuration. \n \n Scaffolds a full Node.js/TypeScript project with @modelcontextprotocol/sdk, zod validation, and choice of HTTP (Express) or stdio transport \n Generates at least one production-ready tool with schema validation, error handling, and structured content responses \n Includes tsconfig.json with ES modules, proper package.json setup, and dev tooling (tsx/ts-node) \n Optionally adds resources wit

AI-powered exploratory testing for web applications with autonomous issue discovery and verification. \n \n Autonomously explores web applications, discovers bugs, and verifies features through natural language test prompts; supports localhost testing without manual setup \n Run tests in parallel across different areas (authentication, accessibility, e-commerce flows) to maximize coverage while continuing other work \n Captures execution ID and browser URL in first 5 seconds, then continues runn

Generates standardized AGENTS.md files to help AI coding agents understand and work with your repository. \n \n Provides a template-driven approach for creating agent-focused documentation that complements README.md with technical setup, workflow, and testing instructions \n Covers essential sections including project overview, setup commands, development workflow, testing, code style, build/deployment, and PR guidelines \n Supports monorepo structures with guidance for creating AGENTS.md files

Generate structured, AI-optimized specification documents with standardized templates and machine-readable formatting. \n \n Creates specification files in /spec/ directory following naming convention spec-[purpose]-[type].md with YAML front matter for metadata \n Enforces structured markdown with 11 standard sections covering purpose, requirements, interfaces, acceptance criteria, and validation \n Includes explicit guidelines for unambiguous language, acronym definitions, and self-contained do

A pipeline that designs Azure infrastructure using natural language, or analyzes existing resources to visualize architecture and proceed through modification and deployment.

This skill helps you create and improve IP geolocation feeds in CSV format by:

Resolve timezone questions for .NET and C# code with production-safe guidance and copy-paste-ready snippets.

The meeting structure that worked at 30 people collapses at 100. What worked at 100 collapses at 300. The failure mode is always the same: too many people in too many meetings making too few decisions.

Strategic account planning and execution for enterprise deals. Turn complex sales cycles into systematic wins — or at least know when they're dying before you waste months.

Find and own a defensible market position. Turn generic messaging into clear differentiation — or at least test whether your differentiation actually resonates before committing to it.

This skill provides setup guidance for working with AWS CDK (Cloud Development Kit) projects using Python.

Use this skill when working with applications, plugins, function-calling flows, or AI integrations built on Semantic Kernel.

Launch new products from idea to first customers. The goal isn't headlines — it's finding 10 customers who can't live without you.

Use this skill to create or review unit tests for Vue components, composables, and Pinia stores. Keep tests small, deterministic, and behavior-first.

You're building an automated QA pipeline that tests a Python application end-to-end — running it the same way a real user would, with real inputs — then scoring the outputs using evaluators and producing pass/fail results via pixie test.

Use this skill when working with applications, agents, workflows, or migrations built on Microsoft Agent Framework.

Create new documentation for an object-oriented component or update an existing component documentation file by analyzing the current implementation.

Publish any presentation or web content to GitHub Pages in one shot.

An autonomous experimentation loop for any programming task. You define the goal and how to measure it; the agent iterates autonomously -- modifying code, running experiments, measuring results, and keeping or discarding changes -- until interrupted.

Use the template in references/BUG-REPORT-TEMPLATE.md. Each report must include:

Issue fields are org-level typed metadata (single select, text, number, date) that replace label-based workarounds with structured, searchable, cross-repo fields. Every organization gets Priority, Effort, Start date, and Target date preconfigured, with support for up to 25 custom fields.

Programmatically build, scaffold, and deploy Power Automate cloud flows via FlowStudio MCP. \n \n Requires a FlowStudio MCP subscription and valid JWT token; connection setup covered in the flowstudio-power-automate-mcp skill \n Covers the full build workflow: safety checks for existing flows, connection reference discovery, definition construction, deployment (create or update), and test execution \n Includes reference templates for triggers, core actions (variables, control flow, expressions),

Translate Oracle PL/SQL stored procedures and functions to PostgreSQL PL/pgSQL equivalents.

Diagnose and fix failing Power Automate cloud flows through step-by-step error inspection. \n \n Requires a FlowStudio MCP server connection with valid JWT; see the flowstudio-power-automate-mcp skill for setup \n Provides a structured 8-step workflow: locate flow, find failing run, extract top-level error, read flow definition, inspect action outputs, pinpoint root cause, apply fix, and verify \n Supports fast-path diagnosis via FlowStudio for Teams subscriptions using get_store_flow_errors for

Generates integration test cases for data access artifacts in a single target project. Tests validate behavior consistency when running against Oracle or PostgreSQL.

Interactive training for GitHub Copilot CLI with guided lessons, quizzes, scenarios, and reference materials. \n \n Covers slash commands, shortcuts, modes, agents, skills, MCP, and configuration through eight progressive modules \n Includes quiz mode (5+ questions per module), scenario challenges, and a comprehensive final exam with XP-based progression tracking \n Levels progress from Newcomer to Wizard (1500 XP max) with XP rewards for lessons, correct answers, perfect quizzes, and scenarios

Run a three-layer verification pipeline on AI-generated output. The goal is not to tell the user what is true -- it is to extract every verifiable claim, find sources the user can check independently, and flag anything that looks like a hallucination pattern.

Analyze screen recordings of manual processes and generate working automation scripts at multiple complexity tiers. \n \n Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation tailored to the user's installed tools \n Offers three automation tiers: quick wins (shell aliases, one-liners), standalone scripts (bash, Python, Node), and full scheduled automation with logging and error handling \n Includes application-specific strategies

Technology-agnostic prompt generator for identifying and documenting high-quality code exemplars across multiple languages. \n \n Supports seven programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with auto-detection capability \n Configurable analysis depth (Basic, Standard, Comprehensive), categorization method (Pattern Type, Architecture Layer, File Type), and documentation format \n Generates exemplars.md files with real file references, descriptions, and optio

Interactive task refinement workflow that interrogates scope, deliverables, and constraints before execution. \n \n Uses the joyride_request_human_input tool to ask clarifying questions iteratively, ensuring full understanding of task requirements before proceeding \n Guides users through defining objectives, success criteria, technical requirements, and expected deliverables through structured dialogue \n Performs project exploration including web research when needed to build comprehensive tas

Credit risk data cleaning and variable screening pipeline for pre-loan modeling. \n \n Executes 11 independent steps covering data loading, abnormal period filtering, missing rate analysis, low-IV and high-PSI variable removal, null importance denoising, and correlation-based feature elimination \n Supports organization-level analysis with separate modeling and out-of-sample (OOS) sample handling, plus multi-process acceleration for IV and PSI calculations \n Generates comprehensive Excel report

Localize VS Code extensions across configurations, walkthrough content, and source code messages. \n \n Three localization approaches: package.nls.LANGID.json for settings, commands, menus, and views; markdown files for walkthrough content; bundle.l10n.LANGID.json for source code strings \n Covers all user-facing resources including contributed configurations, commands, menus, views, ViewsWelcome, and walkthrough titles and descriptions \n Requires creating or updating localization files for all

Interactive workflow to refine task prompts through structured questioning and clipboard delivery. \n \n Guides users through systematic prompt refinement by interrogating scope, deliverables, constraints, and technical requirements \n Produces polished markdown prompts and automatically copies them to the system clipboard via the Joyride extension \n Focuses exclusively on prompt engineering; does not generate code or implementation details \n Requires the Joyride extension for VSCode clipboard

Generates detailed implementation blueprints by analyzing project architecture, technology stacks, and workflow patterns. \n \n Auto-detects project type, entry points, persistence mechanisms, and architecture patterns (.NET, Java/Spring, React, microservices, etc.) to tailor documentation \n Documents complete end-to-end workflows across entry points, service layers, data access, error handling, and response construction with actual code examples \n Configurable output detail levels, workflow c

Automated GitHub pull request creation from specification templates with draft-to-review workflow. \n \n Reads pull request template from .github/pull_request_template.md and extracts requirements to populate PR body and title \n Checks for existing pull requests on the target branch before creation to prevent duplicates \n Progresses pull requests from draft status to ready for review, then auto-assigns to the creator \n Analyzes PR diffs to ensure specification changes are properly documented

Create GitHub issues from specification files using the feature_request.yml template. \n \n Analyzes specification files to extract requirements and automatically generates issue titles and descriptions \n Searches existing issues to prevent duplicates before creating new ones \n Supports both creating new issues and updating existing ones via the GitHub API \n Applies feature and enhancement labels automatically based on specification content \n

Add a new language to a Next.js application using next-intl internationalization. \n \n Requires translating all entries from en.json in the ./messages directory to the new language \n Update routing configuration in src/i18n/routing.ts and middleware in src/middleware.ts to recognize the new language \n Register the language in the language toggle component at src/components/language-toggle.tsx \n

Expert guidance on PostgreSQL-specific features, optimization patterns, and advanced data type capabilities. \n \n Covers JSONB operations, array types, window functions, full-text search, custom types, range types, and geometric types with practical examples \n Includes query optimization strategies using EXPLAIN ANALYZE, index design patterns (composite, partial, covering, expression), and connection/memory management \n Provides monitoring and maintenance techniques via pg_stat_statements, pg

Build and scale developer-led adoption through ecosystem programs, community, and partnerships. Focus on what actually drives adoption, not vanity metrics.

Retrieve and display GitHub Copilot usage metrics for organizations and enterprises. \n \n Supports both organization-level and enterprise-level metrics queries, with optional filtering by specific date (YYYY-MM-DD format) \n Provides aggregated metrics (total active users, acceptance rates, suggestions, chat interactions) and per-user breakdowns via separate commands \n Requires GitHub Enterprise Cloud, appropriate token permissions ( manage_billing:copilot or read:enterprise scope), and the \"

Generate production-ready MCP servers in Ruby using the official SDK with complete project scaffolding. \n \n Generates a full project structure with tools, prompts, resources, tests, and configuration files following Ruby conventions \n Includes input/output schemas, tool annotations, and structured content support for type-safe MCP implementations \n Provides example tools (greet, calculate), prompts (code review), and resources with comprehensive test coverage using Minitest \n Supports stdio

Modern unit testing with MSTest 3.x/4.x using current APIs and best practices. \n \n Covers test class structure, lifecycle management, and the AAA (Arrange-Act-Assert) pattern with sealed classes and constructor-based initialization \n Provides comprehensive assertion APIs including equality, null checks, exception testing (Throws/ThrowsExactly), collections, strings, comparisons, and type assertions \n Supports data-driven tests via DataRow and DynamicData with ValueTuple and TestDataRow for t

Structure board meetings, investor updates, and executive communication that builds trust and drives decisions — not slide decks that nobody reads.

Create Agent Users in Microsoft Entra ID to enable AI agents to act as digital workers with user identity access. \n \n Provisions specialized user identities ( idtyp=user tokens) linked to agent identities, allowing agents to access user-only APIs like Exchange mailboxes, Teams, and org charts \n Requires a parent agent identity created from an agent identity blueprint; supports 1:1 relationship with optional manager assignment and license provisioning \n Includes step-by-step HTTP and PowerShe

Generate Dockerfile and configuration files to containerize an ASP.NET .NET Framework application for Windows containers. \n \n Detects .NET Framework version from project file and selects appropriate Windows Server base images (Core or Full, versions 2016–2022) \n Modifies web.config to enable environment variable configuration via ConfigurationBuilders for app settings and connection strings \n Creates multi-stage Dockerfile with separate build (SDK) and runtime (ASP.NET) stages, including Log

Command-line PDF manipulation covering merging, splitting, encryption, form filling, watermarking, and metadata extraction. \n \n Supports 12+ operations including merge, split, rotate, encrypt/decrypt, burst, stamp, watermark, and repair \n Fill PDF forms from FDF/XFDF data with optional flattening to lock filled fields \n Extract and manipulate metadata, bookmarks, and embedded files; collate separately scanned pages \n Cross-platform installation via package managers (Windows, macOS, Linux) w

XML documentation standards and patterns for C# public APIs and members. \n \n Use <summary> for one-sentence descriptions starting with a present-tense verb, and <remarks> for implementation details, usage notes, or additional context \n Employ specific tags for different member types: <param> and <returns> for methods, <value> for properties, <typeparam> for generics, and <exception cref> for thrown exceptions \n Follow prescribed wording patterns for Boolean

Analyze current repository context and suggest relevant Custom Agents files from the GitHub awesome-copilot repository that are not already available in this repository. Custom Agent files are located in the agents folder of the awesome-copilot repository.

Scaffold a production-ready C# MCP server with tools, logging, and proper configuration. \n \n Generates a complete .NET 8.0+ console application with Host builder pattern, DI configuration, and stdio transport setup \n Includes automatic tool discovery via WithToolsFromAssembly() , attribute-based tool definitions, and structured logging routed to stderr \n Provides example tool implementations with parameter validation, async support, and McpProtocolException error handling \n Covers project s

Scaffold production-ready PHP MCP server projects with tools, resources, prompts, and tests. \n \n Generates complete project structure with composer.json, server.php, and PSR-4 autoloading configured for the official PHP SDK \n Supports three transport types (stdio, HTTP, or both) and includes attribute-based tool, resource, and prompt definitions with schema validation \n Provides templated examples for simple tools, validated parameters, enum-based operations, static resources, and dynamic re

Analyzes project folder structures across multiple technology stacks and generates comprehensive organization blueprints. \n \n Auto-detects project types (.NET, Java, React, Angular, Python, Node.js, Flutter) and identifies architectural patterns including monorepos, microservices, and frontend components \n Generates detailed blueprints with customizable visualization styles (ASCII tree, markdown lists, or tables) and configurable depth levels \n Documents file placement patterns, naming conve

HTML5 Canvas breadboard circuit mockups for retro computing and vintage electronics projects. \n \n Supports 40+ components including W65C02S microprocessors, 555 timers, 28C256 EEPROMs, W65C22 VIAs, 7400-series logic gates, and passive components like LEDs, resistors, and capacitors \n Renders circuits on a 20px grid system with component rotation, wire color coding, and pinout reference tables for accurate layout visualization \n Includes step-by-step workflows for common builds: LED circuits,

Generate production-ready Go MCP server projects with proper structure, dependencies, and typed tool implementations. \n \n Scaffolds complete Go module layout with official MCP SDK integration, including main server setup, tool registration, and graceful shutdown handling \n Provides typed tool handlers with JSON schema validation, structured inputs/outputs, and context-aware error handling \n Includes configuration management via environment variables, basic test structure, and README document

Declarative policies, intent classification, and audit trails for controlling AI agent tool access and behavior. \n \n Composable governance policies define allowed/blocked tools, content filters, rate limits, and approval requirements — stored as configuration, not code \n Semantic intent classification detects dangerous prompts (data exfiltration, privilege escalation, prompt injection) before tool execution using pattern-based signals \n Tool-level governance decorator enforces policies at fu

Comprehensive DAX formula analysis and optimization with performance, readability, and best-practice guidance. \n \n Analyzes formulas across four dimensions: performance bottlenecks, readability clarity, best-practice compliance, and maintainability challenges \n Provides step-by-step optimization strategy including variable usage opportunities, function replacements, and context optimization techniques \n Delivers refactored formulas with improved structure, error handling via DIVIDE and BLANK

Maintain accurate llms.txt files that guide LLMs through repository documentation and structure. \n \n Analyzes current repository state against the official llms.txt specification to identify outdated, missing, or incorrect file references \n Organizes essential documentation into logical H2 sections (Documentation, Specifications, Examples, Configuration, Optional) with properly formatted markdown links \n Validates compliance with https://llmstxt.org/ format requirements including H1 header,

Create hybrid agent skills for Microsoft technologies with local knowledge and dynamic Learn MCP lookups. \n \n Generates modular skill packages with frontmatter, reference documentation, and working code examples for any Microsoft technology (Azure, .NET, M365, Semantic Kernel, etc.) \n Uses three-phase investigation workflow: scope discovery via search, core content fetching, and depth exploration for best practices and troubleshooting \n Balances local storage of foundational concepts and com

Generates a specification-compliant llms.txt file that guides LLMs through repository structure and documentation. \n \n Analyzes repository layout, identifies key files (READMEs, specs, examples, configuration), and catalogs documentation structure before generation \n Creates llms.txt following the official specification at https://llmstxt.org/ with H1 project name, blockquote summary, and organized H2 file sections \n Organizes files into logical categories (Documentation, Specifications, Exa

Python SDK setup and CRUD snippets for Microsoft Dataverse operations. \n \n Generates installation commands and DataverseClient initialization with InteractiveBrowserCredential authentication \n Includes single-record CRUD patterns (create, retrieve, update, delete) following official SDK conventions \n Covers bulk operations with both broadcast and 1:1 update modes for efficient batch processing \n Demonstrates retrieve-multiple queries with paging support (top, page_size parameters) \n Option

Validates Bicep deployments to Azure before execution, detecting syntax errors, permission issues, and previewing infrastructure changes. \n \n Supports both Azure CLI ( az ) and Azure Developer CLI ( azd ) workflows, auto-detecting project type via azure.yaml \n Runs Bicep syntax validation, what-if analysis across all deployment scopes (resource group, subscription, management group, tenant), and permission checks with fallback to reduced-permission validation \n Captures detailed what-if resu

Complete Python MCP server project generator with tools, resources, and proper configuration. \n \n Scaffolds a new Python project using uv with MCP SDK, proper directory structure, and .gitignore \n Supports both stdio (local) and streamable-http (remote) transport types with optional host, port, and stateless mode configuration \n Generates decorated tools, resources, and prompts with automatic schema generation from type hints and docstrings \n Includes comprehensive error handling, async/awa

Analyze Azure resource groups and generate detailed Mermaid diagrams showing resource relationships and architecture. \n \n Discovers and analyzes all resources within a specified resource group, capturing configurations, SKUs, network settings, and identity details \n Maps relationships between resources including network connections, data flows, identity bindings, and dependencies \n Generates layered Mermaid diagrams with subgraphs organized by function (Network, Compute, Data, Security, Moni

Generates GitHub Copilot migration instructions by analyzing code differences between project versions. \n \n Compares two project states (branches, commits, or tags) to extract transformation patterns, API changes, and architectural shifts \n Creates a .github/copilot-migration-instructions.md file with mandatory rules, validated transformations, API correspondences, and obsolete patterns to avoid \n Supports multiple migration types: framework version upgrades, architecture refactoring, techno

Generate complete MCP server implementations optimized for Copilot Studio with Power Platform connector standards. \n \n Produces all required files (apiDefinition.swagger.json, apiProperties.json, script.csx, and MCP server code) following Power Platform connector structure with x-ms-agentic-protocol: mcp-streamable-1.0 support \n Enforces Copilot Studio schema constraints: no reference types, single-type fields only, primitive types, and full URI endpoints to ensure compatibility \n Generates

CLI for Windows app packaging, MSIX creation, certificates, and Microsoft Store deployment across frameworks. \n \n Initializes projects with SDK setup, manifests, and development certificates; supports .NET, C++, Electron, Rust, Tauri, and cross-platform frameworks \n Creates MSIX packages with optional signing, self-contained runtime bundling, and external catalog management \n Adds temporary package identity to executables for debugging Windows APIs (notifications, Windows AI, shell integrati

Update implementation plan files with new requirements for features, refactoring, or infrastructure changes. \n \n Generates machine-readable, deterministic plans structured for autonomous execution by AI agents or humans \n Enforces atomic phases with measurable completion criteria, parallel-executable tasks, and explicit file paths and function names \n Requires strict adherence to a mandatory template with front matter, requirements, implementation phases, alternatives, dependencies, affected

Identify sponsorable open source maintainers across your project's entire dependency tree. \n \n Resolves full dependency trees (direct and transitive) across npm, PyPI, Cargo, Go, RubyGems, Maven, and NuGet using the deps.dev API \n Discovers funding links from npm metadata, FUNDING.yml files (repo and org-level), and web search; verifies every URL before reporting \n Groups dependencies by maintainer and funding destination, showing health indicators (Maintained/Partially maintained/Unmaintain

Real-time Azure service pricing lookup and Copilot Studio agent credit cost estimation. \n \n Queries the Azure Retail Prices API to fetch current pricing for compute, storage, networking, databases, AI, and all other Azure service families across regions and SKUs \n Supports filtering by service name, region, SKU, price type (consumption, reservation, spot), and savings plan options with OData syntax \n Includes cost estimation formulas for monthly and annual workload projections based on usage

Build and validate Snowflake semantic views using Snowflake CLI with guided DDL creation and testing. \n \n Handles the complete semantic view lifecycle: drafting DDL, populating synonyms and comments from Snowflake table metadata, validating against Snowflake via CLI, and executing final CREATE or ALTER statements \n Requires one-time Snowflake CLI installation and connection setup; confirms prerequisites before proceeding with validation \n Validates all DDL against Snowflake using temporary v

Automated README generation by analyzing project documentation structure and metadata files. \n \n Scans .github/copilot directory files and copilot-instructions.md to extract project information, technology stack, architecture, and development workflow \n Generates well-structured markdown with standard sections: overview, tech stack, architecture, setup, folder structure, features, development workflow, coding standards, testing, and contributing guidelines \n Produces developer-focused docume

Image processing and manipulation using ImageMagick across Windows, Linux, and macOS. \n \n Retrieve image metadata, dimensions, and format information with the identify command \n Resize single or batch images with aspect ratio control and thumbnail generation \n Filter and process images based on dimensions or file type criteria \n Requires ImageMagick installed and available as magick on PATH; includes platform-specific setup for PowerShell (Windows) and Bash (Linux/macOS) \n

This skill provides procedural guidance for configuring GitHub secret scanning — detecting leaked credentials, preventing secret pushes, defining custom patterns, and managing alerts.

Search and explore Windows desktop APIs with full type signatures and members. \n \n Indexes Windows Platform SDK, WinAppSDK, NuGet packages, and project-output WinMD files; Platform SDK and WinAppSDK are available immediately on fresh clones without restore or build \n Two workflows: discover mode for finding the right API by capability keywords, and lookup mode for retrieving exact methods, properties, events, and enum values of known types \n Requires .NET SDK 8.0+ and a one-time cache genera

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Automate Microsoft Store publishing and submission management from the command line. \n \n Supports Windows App SDK, UWP, .NET MAUI, Flutter, Electron, React Native, and PWA applications with automatic project detection \n Core commands cover credential configuration, app listing, submission status checks, publishing, metadata updates, and gradual rollouts \n Includes package flight management for beta testing with group-based distribution and rollout percentage control \n Integrates with Partne

Go-to-market strategy for AI products. These aren't generic AI principles — they're patterns from selling autonomous AI agents into enterprises where "autonomous" scared buyers and "teammate" converted them.

Plain-English translation layer that makes Copilot CLI accessible to non-technical users. \n \n Translates every approval prompt, error message, and technical output into jargon-free language with color-coded risk indicators (🟢 low, 🟡 moderate, 🔴 high, ⛔ critical) \n Explains what each action does, why it's needed, what the risks are, and what happens if you approve or decline before any permission is requested \n Automatically defines technical terms on first use and provides step-by-step ro

Interactive step-by-step tutorials for GitHub Copilot CLI with separate Developer and Non-Developer learning paths. \n \n Offers three core modes: Tutorial (guided lessons), Q&A (on-demand answers), and Reset (start over) \n Includes 7 shared and track-specific lessons covering prompts, permissions, slash commands, file mentions, planning, and advanced features like MCP servers \n Tracks progress with SQL-backed lesson completion and supports mid-tutorial track switching \n Provides beginne

Complete GitHub CLI reference for repositories, issues, pull requests, Actions, projects, releases, and all command-line GitHub operations. \n \n Covers 20+ command categories including repos, issues, PRs, workflows, projects, releases, gists, codespaces, organizations, and search with 100+ subcommands \n Supports authentication modes (interactive login, tokens, GitHub Enterprise), configuration via environment variables, and multi-account switching \n Includes JSON output filtering with jq, cus

Generates production-ready GitHub Copilot configuration files tailored to your project's technology stack. \n \n Gathers project information (language, framework, tech stack, development style) and creates a complete .github/ directory structure with instructions, skills, and agents \n Generates language-specific instruction files, testing standards, security guidelines, and code review practices with attribution to awesome-copilot patterns where applicable \n Creates six reusable skills (compon

Comprehensive prompts for analyzing, planning, and executing .NET framework upgrades across multi-project solutions. \n \n Covers the full upgrade lifecycle: project discovery and classification, dependency analysis, framework targeting, code modernization, and breaking change detection \n Includes 24+ ready-to-use prompts organized into nine categories spanning strategy, CI/CD pipeline updates, testing validation, and version control practices \n Provides guidance on using .NET Upgrade Assistan

Surfaces migration risks and validates migration work against known Oracle/PostgreSQL behavioral differences documented in the references/ folder.

Build accessible, production-ready web forms with HTML, CSS, JavaScript, and server-side processing. \n \n Covers complete form lifecycle: HTML structure, CSS styling, JavaScript validation, and server-side handling with PHP or Python \n Includes accessibility best practices (ARIA roles), progressive enhancement, and security guidelines for data transmission \n Provides references for database integration (MySQL), data formats (JSON, XML), and REST API communication \n Supports both client-side

Analyze current repository context and suggest relevant prompt files from the GitHub awesome-copilot repository that are not already available in this repository.

Generate a complete TypeSpec declarative agent for Microsoft 365 Copilot with instructions, capabilities, and conversation starters. \n \n Produces a main.tsp file with agent declaration, instructions, conversation starters, and capability definitions following Microsoft's TypeSpec M365 Copilot schema \n Supports 10 capability types including WebSearch, OneDriveAndSharePoint, TeamsMessages, Email, People, CodeInterpreter, GraphicArt, GraphConnectors, Dataverse, and Meetings with optional scoping

Analyze Azure resources and IaC files to identify cost optimizations, creating tracked GitHub issues for implementation. \n \n Discovers Azure resources across subscriptions and resource groups, analyzes IaC files (Bicep, Terraform, ARM templates), and collects usage metrics from Log Analytics to validate current costs \n Generates evidence-based optimization recommendations with priority scoring based on monthly savings, implementation effort, and risk assessment \n Creates individual GitHub is

Generate TypeSpec API plugins for Microsoft 365 Copilot with REST operations, authentication, and Adaptive Cards. \n \n Scaffolds complete TypeSpec projects with agent definitions (main.tsp) and API operations (actions.tsp) following Microsoft 365 Copilot conventions \n Supports four authentication modes: public APIs, API key headers, OAuth2 with authorization code flow, and registered auth references \n Includes optional confirmation dialogs for destructive operations and Adaptive Card template

Adaptive Card response templates for MCP-based API plugins in Microsoft 365 Copilot. \n \n Supports static templates for consistent data formats, dynamic templates for multiple item types, and combined approaches with fallback defaults \n Includes JSONPath-based data mapping, conditional rendering, number formatting, and responsive single-column layouts optimized for Teams, Word, and PowerPoint \n Provides card elements like TextBlock, FactSet, Image, Container, ColumnSet, and actions with templ

Configure your Dataverse environment for GitHub Copilot's MCP server integration. \n \n Supports both global (all projects) and project-scoped configuration paths with automatic directory creation \n Auto-discovers Dataverse environments from your Azure account via Power Apps API, or accepts manual URL entry as fallback \n Generates unique server names from organization identifiers and registers servers in the MCP configuration file with GA or Preview endpoint options \n Validates existing confi

Analyze a .NET solution, classify every project for Oracle→PostgreSQL migration eligibility, and write a structured plan that downstream agents and skills can parse.

RESTful CRUD operations for TypeSpec API plugins with routing, parameters, and adaptive cards. \n \n Supports GET, POST, PATCH, and DELETE operations with path, query, and body parameters for filtering, creation, and updates \n Includes adaptive card visualization for GET responses and confirmation dialogs for destructive operations (POST, PATCH, DELETE) \n Provides complete CRUD example with models, error handling, and multiple query parameter patterns \n Built-in testing prompts and best pract

Generate production-ready Kotlin MCP server projects with Gradle, official SDK, and typed tools. \n \n Scaffolds complete project structure with build.gradle.kts, package layout, and MCP server configuration using io.modelcontextprotocol:kotlin-sdk \n Includes 2–3 sample tools with JSON schema definitions, typed inputs/outputs, and error handling patterns \n Provides coroutine-based server setup with stdio transport, environment variable configuration, and kotlin-logging integration \n Includes

Randomize JSON object arrays while validating schema consistency and preventing data corruption. \n \n Validates that all objects share identical property names and structure before shuffling, rejecting files with inconsistencies or nested objects in default mode \n Requires a JSON file as input; pauses and requests data if none is provided \n Supports variable overrides to customize which properties are ignored, which are required, or whether nesting is allowed \n Returns shuffled data with ori

Create concise tldr summaries for GitHub Copilot files, MCP servers, and documentation. \n \n Transforms verbose Copilot customization files (.prompt.md, .agent.md, .instructions.md, .collections.md), MCP server docs, and URLs into example-driven tldr references \n Supports batch processing of up to 5 files or URLs; automatically resolves ambiguous queries by searching workspace or GitHub awesome-copilot \n Generates markdown-formatted tldr pages with correct invocation syntax (/ for prompts, @

Generate production-ready MCP servers in Java with the official SDK, Maven or Gradle, and reactive streams. \n \n Scaffolds complete project structure with tools, resources, and prompts organized into separate handler classes \n Includes Maven and Gradle templates with MCP SDK, SLF4J logging, Project Reactor, and JUnit 5 dependencies \n Provides ready-to-use handler templates for tools, resources, and prompts with error handling and async Mono-based responses \n Generates test suite examples and

Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness with exact patch locations. \n \n Analyzes every BigQuery job trigger and external API call to identify cost exposure, loop-driven query multiplication, and missing maximum_bytes_billed limits \n Enforces dry-run and execute modes with explicit prod confirmation, partition filter validation, and scan-size optimization \n Validates idempotent writes using MERGE, staging tables, or dedup logic; flags unsafe a

Generates complete, copy-paste ready implementation documentation from structured PR plans. \n \n Parses feature plans to extract implementation steps, affected files, and requirements \n Produces comprehensive markdown documentation with full code blocks, exact file paths, and zero-ambiguity instructions \n Includes research-backed code patterns, project conventions, and technology stack details specific to your codebase \n Provides markdown checkboxes, verification checklists, and commit gates

Implementation agent that executes predefined plans step-by-step without deviation. \n \n Requires an explicit implementation plan document as input; returns an error message if none is provided \n Follows the plan sequentially, checking off completed items and refusing to skip steps or add unspecified code \n Updates the plan document inline as progress is made and validates work using build or test commands specified in the plan \n Stops execution when reaching STOP instructions and returns co

Create comprehensive documentation for the object-oriented component(s) at: ${input:ComponentPath}.

Generate complete, production-ready solutions for Dataverse SDK use cases with architecture guidance. \n \n Analyzes requirements across data volume, frequency, performance, and error tolerance to recommend appropriate patterns (transactional, batch, query, file management, scheduled, or real-time) \n Provides full Python implementation including authentication, singleton service classes, CRUD operations, bulk processing, and comprehensive error handling \n Includes data model design with table

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Generate complete, production-ready MCP server projects in Swift with the official SDK. \n \n Scaffolds full project structure with Package.swift, source files organized by capability (Tools, Resources, Prompts), and unit tests \n Includes templates for main.swift with ServiceLifecycle integration, Server.swift configuration, and handler implementations for tools, resources, and prompts \n Provides example tool definitions (greet, calculate) and resource/prompt handlers with proper error handlin

List and summarize your pull requests in the current GitHub repository. \n \n Retrieves all PRs assigned to you using the current repository context and filters results to show only your own pull requests \n Displays PR purpose, details, and status, with special highlighting for PRs awaiting review \n Reports any CI/CD check failures and suggests remediation steps \n Offers to request Copilot review for PRs that haven't been reviewed yet \n

Cloud-based media processing for video, audio, images, and documents using 86+ specialized robots. \n \n Supports video encoding (HLS, MP4, WebM), thumbnail generation, image resizing/watermarking, audio transcoding, document OCR, and speech-to-text via chainable processing steps \n Access via MCP server (recommended for IDE integration) or CLI; requires free Transloadit account with API credentials \n Build multi-step pipelines by chaining robot operations together using the \"use\" field; reus

Structured planning framework for breaking development requests into testable, commit-sized implementation steps. \n \n Conducts mandatory autonomous research phase to gather code context, documentation, dependencies, and existing patterns before planning \n Breaks features into commits sized for single pull requests, with simple features consolidated into one commit and complex features split into multiple testable steps \n Generates plans with file lists, step descriptions, and verification me

You are an expert in refactoring Java methods.

Convert plain text documentation to markdown with flexible formatting options. \n \n Supports three conversion approaches: explicit instructions, documented options, or reference file templates for consistent formatting patterns \n Includes predefined instruction shortcuts for common tasks like removing header digits, adjusting heading levels, and reducing indentation \n Handles code block detection with language identification, proper list nesting, and markdown syntax compliance across multiple

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Creates a compilable, empty xUnit test project with transaction management and seed data infrastructure for a single target project. Run once per project before writing tests.

Diagnose Azure resource health issues and generate a prioritized remediation plan. \n \n Analyzes resource status, logs, and telemetry across 8+ Azure service types (Web Apps, VMs, Cosmos DB, Storage, SQL Database, Application Insights, Key Vault, Service Bus) \n Executes targeted KQL queries against Log Analytics and Application Insights to identify errors, performance degradation, and anomalies \n Classifies issues by severity (Critical, High, Medium, Low) and performs root cause analysis acro

Generate complete, production-ready applications directly from OpenAPI specifications. \n \n Accepts OpenAPI specs via URL, local file, or direct content input; validates completeness and identifies endpoints, schemas, authentication requirements, and data relationships \n Generates full project structure with controllers, services, models, repositories, and configuration files following framework conventions and best practices \n Includes unit tests, README with setup instructions, environment

Comprehensive guide for designing Azure Cosmos DB NoSQL data models through structured requirements gathering and aggregate-oriented design. \n \n Guides you through capturing application requirements, access patterns, volumetrics, and workload characteristics in a structured cosmosdb_requirements.md file \n Applies aggregate-oriented design principles to group related entities based on access correlation, identifying relationships, and operational coupling \n Produces a final cosmosdb_data_mode

Scaffold a declarative agent for Microsoft 365 Copilot integrated with an MCP server. \n \n Generates complete project structure including manifest.json, declarativeAgent.json, and ai-plugin.json with tool definitions auto-imported from MCP servers \n Supports OAuth 2.0 and SSO authentication with environment variable configuration and secure credential storage \n Provides response semantics mapping to extract and format API response data for Copilot consumption \n Includes MCP server integratio

Code-first orchestration for polyglot distributed apps with automatic service discovery and observability. \n \n Orchestrates containers, executables, and cloud resources (C#, Python, JavaScript, Go, Java, Rust, and more) from a single .NET AppHost project \n Includes 144+ integrations across databases, caches, messaging, AI, and observability platforms with automatic environment variable injection for service discovery \n Built-in dashboard provides real-time logs, traces, metrics, and GenAI vi

Add educational comments to code files to transform them into effective learning resources. \n \n Adapts explanation depth and tone to three configurable knowledge levels: beginner, intermediate, and advanced \n Automatically requests a file if none is provided, with numbered list matching for quick selection \n Expands files by up to 125% using educational comments only (hard limit: 400 new lines; 300 for files over 1,000 lines) \n Preserves file encoding, indentation style, syntax correctness,

Analyzes git changes and creates semantic Git Flow branches automatically based on change type. \n \n Inspects staged and unstaged changes via git status and git diff to determine branch category: feature, release, or hotfix \n Generates semantic branch names following Git Flow conventions (e.g., feature/user-auth , release-1.2.0 , hotfix/security-patch ) \n Branches from the correct source ( develop for features and releases, master for hotfixes) and creates the branch in one command \n Handles

Persistent, domain-organized memory system that captures lessons learned across VS Code projects. \n \n Stores reusable knowledge in two scopes: global (all projects) or workspace-specific, automatically organizing learnings by domain \n Uses simple syntax ( /remember [>domain [scope]] lesson ) to transform debugging sessions and hard-won discoveries into searchable memory instructions \n Automatically discovers existing memory domains and intelligently categorizes new learnings, creating domain

Generate a Spring Boot 3.4.5 project skeleton with PostgreSQL, Redis, MongoDB, and Docker Compose. \n \n Scaffolds a Maven-based Spring Boot project with Java 21, including Lombok, Spring Data JPA, Spring Web, caching, and validation dependencies \n Pre-configures application.properties with connection details for PostgreSQL, Redis, and MongoDB, plus SpringDoc OpenAPI for API documentation \n Generates a docker-compose.yaml file with Redis 6, PostgreSQL 17, and MongoDB 8 services, each with pers

Query Microsoft 365 data with natural language to surface emails, meetings, documents, Teams messages, and people insights. \n \n Supports five data sources: emails, meetings, documents, Teams channels, and people/projects with natural-language prompts \n Install via Copilot CLI plugin (preferred) or standalone npm package; requires Microsoft 365 tenant admin consent on first use \n Core workflow: clarify intent, craft precise prompts with timeframe/source, run workiq ask --question \"...\" , an

Identify false-positive diffs in Terraform AzureRM plans caused by Set-type attribute ordering. \n \n Analyzes terraform plan JSON output to distinguish spurious diffs (element reordering in Sets) from actual resource changes \n Targets AzureRM resources with Set-type attributes: Application Gateway, Load Balancer, NSG, Firewall, Front Door, and others \n Requires Python 3.8+ and uses only standard library; integrates into CI/CD pipelines with configurable output formats and exit codes \n Helps

Create concise, example-driven tldr pages from documentation URLs and command names. \n \n Requires both a command name and authoritative documentation URL; provides guidance if either is missing \n Extracts 5–8 most common use cases ordered by frequency, following strict tldr-pages project formatting standards \n Uses {{placeholder}} syntax for options, arguments, and user-provided values with specific conventions for flags, options with arguments, and subcommands \n Validates documentation sou

Automated Azure Verified Module version updates for Bicep files with breaking change detection. \n \n Scans Bicep files to extract AVM module references, queries MCR for latest versions, and applies updates automatically \n Pauses for manual approval when breaking changes, security modifications, or parameter incompatibilities are detected \n Validates all changes with bicep lint and bicep build to ensure compliance before finalizing \n Outputs results in a structured table showing module names,

Structured prompt for generating Epic-level Product Requirements Documents with consistent output format. \n \n Guides product managers through a standardized PRD structure covering goals, user personas, journeys, functional and non-functional requirements, success metrics, and scope boundaries \n Outputs markdown-formatted PRDs saved to a consistent directory path ( /docs/ways-of-work/plan/{epic-name}/epic.md ) for use as single source of truth \n Includes built-in prompting for clarifying ques

Refactor a method to reduce cognitive complexity by extracting helper methods. \n \n Analyzes nested conditionals, loops, and complex boolean expressions to identify refactoring opportunities \n Extracts validation logic, type-specific processing, and repeated code blocks into focused helper methods \n Simplifies the main method flow while preserving all original functionality and error handling \n Includes mandatory test verification to confirm zero test failures and cognitive complexity at or

Detailed feature implementation plan generator following Epoch monorepo conventions. \n \n Generates comprehensive markdown implementation plans with system architecture, database schema, and API design specifications \n Includes Mermaid diagrams for system architecture, entity relationships, and component hierarchies with shadcn/ui integration \n Covers frontend component structure, state management patterns, backend service design, and deployment strategies \n Outputs structured plans to /docs

Complete development kit for building Microsoft 365 Copilot declarative agents with TypeSpec and Agents Toolkit integration. \n \n Three specialized workflows cover basic agent creation, advanced enterprise design, and validation/optimization for existing agents \n Supports up to 5 capabilities from 11 options including WebSearch, OneDrive/SharePoint, Graph Connectors, Power Platform, and custom connectors \n Enforces v1.5 schema compliance with character limits (name: 100, description: 1000, in

Run pytest with coverage reporting to identify and eliminate untested code lines. \n \n Generates annotated source files in cov_annotate/ directory, with ! markers indicating uncovered lines \n Supports module-specific coverage checks via --cov=module_name and targeted test runs on specific test files \n Workflow: run coverage, review annotated files for uncovered lines, write tests to cover gaps, repeat until 100% coverage achieved \n

Generate production-ready Python code for Dataverse SDK with error handling and best practices. \n \n Implements comprehensive error handling using DataverseError hierarchy with retry logic and exponential backoff for transient failures \n Enforces singleton client pattern for connection management and includes structured logging for audit trails and debugging \n Applies OData optimization techniques: server-side filtering, column selection, and pagination to reduce data transfer \n Provides typ

Guides developers through creating production-ready GitHub Copilot prompts with structured discovery and best practices. \n \n Systematically gathers requirements across nine discovery sections covering identity, persona, task specification, context, instructions, output format, tools, and validation \n Generates complete .prompt.md files with proper front matter, clear structure, and comprehensive instructions following established patterns \n Includes best practices for prompt engineering, too

Programmatic agent runtime for embedding Copilot's agentic workflows in applications across Python, TypeScript, Go, and .NET. \n \n Exposes the same production-tested engine behind Copilot CLI with support for streaming responses, custom tool definitions, and multi-turn conversations \n Supports four language runtimes (Node.js 18+, Python 3.8+, Go 1.21+, .NET 8.0+) with consistent APIs across all platforms \n Enables integration with MCP servers for pre-built tools, custom agent personas with sp

Guidance for selecting least-privilege Azure roles matching specific permission requirements. \n \n Searches Azure built-in roles to find the minimal role matching desired permissions, or generates custom role definitions when no built-in role fits \n Provides CLI commands and Bicep code snippets for applying role assignments to identities \n Integrates Azure documentation, best practices, and Bicep schema tools to recommend and implement role configurations \n

PostgreSQL code review assistant covering JSONB, arrays, custom types, schema design, and security best practices. \n \n Reviews JSONB operations for indexing efficiency, array usage with GIN indexes, and proper containment operators \n Evaluates schema design including ENUM types, CITEXT for case-insensitive data, TIMESTAMPTZ usage, and CHECK constraints \n Identifies anti-patterns in function optimization, trigger design, and extension usage \n Assesses Row Level Security (RLS) implementation,

Comprehensive Power BI data model design review framework for evaluating architecture, relationships, and optimization. \n \n Covers schema architecture, relationship design, and storage mode strategy with detailed assessment checklists across fact tables, dimensions, cardinality, and filter directions \n Includes three-phase review process: model architecture analysis, performance and scalability evaluation, and maintainability/governance assessment \n Provides specialized review types for pre-

Detect and remove AI-generated patterns from Finnish text to match native speaker voice. \n \n Identifies 26 patterns across Finnish-specific structures (passive overuse, missing particles, translation calques, genitive chains) and universal markers (hedging, filler phrases, artificial enthusiasm) \n Preserves meaning, register, and technical content while replacing AI patterns with natural Finnish constructions \n Includes adaptive workflow: analyzes short texts directly, flags patterns in long

Image generation and editing via OpenRouter's Gemini 3 Pro model with multi-image compositing support. \n \n Supports three workflows: prompt-only generation, single-image editing, and multi-image composition (up to 3 images per request) \n Output resolution options: 1K, 2K, or 4K (defaults to 1K) \n Customizable behavior through optional system prompt template in assets/SYSTEM_TEMPLATE \n Requires OPENROUTER_API_KEY environment variable and uv package manager; includes troubleshooting guidance

Search official Microsoft documentation across Azure, .NET, Agent Framework, Aspire, VS Code, and GitHub. \n \n Primary tool covers learn.microsoft.com via three commands: search concepts and guides, find code samples by language, and fetch full page content \n Includes CLI fallback ( mslearn command) when MCP server is unavailable, with JSON output option for scripting \n Routes Aspire, VS Code, GitHub, and Agent Framework queries to specialized tools (Aspire MCP, Context7) since their docs liv

Create, update, and manage GitHub issues with full workflow support including types, labels, assignees, and dependencies. \n \n Supports issue creation with type categorization (Bug, Feature, Task, etc.), labels, assignees, and milestones via gh api REST calls \n Read operations via MCP tools for fetching issue details, comments, labels, and cross-repository search \n Handles issue updates (title, body, state, labels, assignees) and includes templates for bug reports, feature requests, and tasks

Manage Azure DevOps resources including projects, repos, pipelines, builds, work items, and service endpoints via CLI. \n \n Covers six major domains: Repos and PRs, Pipelines and Builds, Boards and Work Items, Variables and Agents, Organization and Security, and Advanced Usage patterns \n Requires Azure CLI 2.81.0+ with the azure-devops extension; authenticate using PAT tokens and configure default organization/project to avoid repeating flags \n Supports output formatting with JMESPath queries

Analyze existing code files to automatically generate a project coding standards document. \n \n Extracts syntax patterns (indentation, naming conventions, commenting style, braces, line length) from one or more files or entire folders to establish baseline standards \n Detects and optionally fixes inconsistencies across files, flagging deviations from the majority pattern in each category \n Outputs standards to a new file (CONTRIBUTING.md, STYLE.md, CODING_STANDARDS.md, etc.), README.md insert

Codebase auditor identifying Apple App Store rejection risks and compliance gaps. \n \n Systematically reviews Info.plist, entitlements, privacy manifests, permissions, IAP flows, account handling, and content moderation against App Store Review Guidelines \n Produces prioritized risk register with severity levels, evidence citations, and concrete remediation steps \n Includes reviewer experience checklist and draft App Review Notes to streamline submission and reduce re-review cycles \n Focuses

Generate comprehensive Product Requirements Documents that translate business vision into technical specifications. \n \n Follows a strict three-phase workflow: discovery interview to fill knowledge gaps, analysis and scoping to identify dependencies, and technical drafting using a standardized PRD schema \n Requires concrete, measurable success criteria and acceptance criteria; explicitly avoids vague language like \"fast\" or \"intuitive\" in favor of quantifiable benchmarks \n Covers executiv

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Command-line interface for discovering and executing MCP server tools and external integrations. \n \n Five core commands cover server discovery, tool exploration, schema inspection, execution, and grep-based searching across all available tools \n Supports JSON input/output for scripting, raw text extraction, and description flags for verbose tool documentation \n Handles complex JSON arguments via heredoc, stdin piping, or file input to accommodate special characters and multi-line payloads \n

Search and prioritize your assigned issues by age, engagement, and status. \n \n Queries the current repository for issues assigned to you using the GitHub repo context \n Surfaces issue recommendations based on age, comment count, and open/closed status to help you prioritize work \n Integrates with the list_issues command to retrieve and analyze issue data from your repository \n

Micro-prompt that reinforces interactive programming workflows with live REPL access. \n \n Designed for Clojure with Copilot integration via Backseat Driver, but adaptable to any system with a live REPL \n Reminds the agent to use the REPL as the source of truth for exploration and system modification \n Encourages clear communication about evaluated code and preference for structural editing tools over text replacement \n Includes guidance on maintaining a todo list during interactive developm

NuGet package management for .NET projects using dotnet CLI with strict workflows for adds, removes, and version updates. \n \n Enforces dotnet add and dotnet remove commands for package operations; direct file edits only allowed for version changes \n Requires version verification via dotnet package search before updates, with support for both jq and PowerShell parsing \n Supports centralized version management via Directory.Packages.props or per-project .csproj configuration \n Mandatory dotne

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Project-specific context for conversations through curated documentation, code, and team instructions. \n \n List and load Copilot Spaces using MCP read-only tools; create, update, and delete spaces via GitHub REST API with gh api \n Spaces contain attached repositories, files, documentation, and custom instructions that ground Copilot responses in actual project knowledge \n Use spaces to answer architecture and standards questions, generate code following team conventions, or execute multi-ste

This skill provides procedural guidance for configuring and running CodeQL code scanning — both through GitHub Actions workflows and the standalone CodeQL CLI.

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Expert web development engineer with mastery across 15 core competencies spanning HTML, CSS, JavaScript, APIs, protocols, security, and performance. \n \n Covers 15 specialized domains including semantic markup, modern CSS layouts, ES6+ JavaScript, Web APIs, HTTP/HTTPS, CORS/CSP security, Core Web Vitals optimization, WCAG accessibility, and web standards compliance \n Includes terminology translation for ambiguous or colloquial web terms, context-aware interpretation across frontend, backend, a

Generate a Spring Boot Kotlin project skeleton with pre-configured databases and development services. \n \n Downloads a Spring Boot 3.4.5 project template with Kotlin, WebFlux, R2DBC, Redis, and MongoDB dependencies via Spring Initializr \n Includes Docker Compose configuration for PostgreSQL 17, Redis 6, and MongoDB 8 with pre-set credentials and volume mounts \n Adds SpringDoc OpenAPI integration for Swagger UI documentation and ArchUnit for architecture testing \n Requires Java 21, Docker, a

Best practices guide for C# asynchronous programming patterns and pitfalls. \n \n Covers naming conventions (Async suffix), return types (Task, ValueTask, avoid void), and exception handling strategies including ConfigureAwait and Task.FromException \n Highlights performance optimization techniques: Task.WhenAll for parallel execution, Task.WhenAny for timeouts, and cancellation token usage \n Documents critical pitfalls to avoid: blocking calls like .Wait() and .Result, async void methods outsi

Analyze and refresh object-oriented component documentation to match current implementation. \n \n Examines source code to identify design patterns, dependencies, interfaces, and architectural changes, then updates existing documentation to reflect the current implementation \n Supports language-specific optimizations for C#/.NET, Java, TypeScript/JavaScript, and Python with framework-aware analysis \n Preserves documentation structure and formatting while refreshing all technical content, code

Scaffold new Agent Skills with proper frontmatter, directory structure, and bundled resource organization. \n \n Generates SKILL.md files with required YAML frontmatter (name, description) and optional metadata fields for skill discovery \n Supports optional bundled directories: scripts/ for automation, references/ for documentation, assets/ for static files, and templates/ for starter code \n Provides step-by-step workflow for creating skills from scratch or duplicating this template as a start

Comprehensive project planning with Epic > Feature > Story hierarchy, dependencies, priorities, and GitHub automation. \n \n Generates hierarchical work breakdown from epics down to individual tasks with INVEST-compliant user stories, technical enablers, and test items \n Provides GitHub issue templates for each work item type (epic, feature, story, enabler, task) with acceptance criteria, definitions of done, and dependency linking \n Includes priority and value matrix (P0–P3), Fibonacci story

Microsoft Fabric Lakehouse storage for unified tabular and non-tabular data with Delta Lake, SQL analytics, and fine-grained security. \n \n Combines data lake flexibility with data warehouse management through Delta Lake format, ACID transactions, versioning, and SQL endpoints for T-SQL querying \n Organizes data via schemas (folders under Tables), shortcuts (virtual links to internal/external sources), and materialized views for optimized query performance \n Supports multiple data formats: De

Comprehensive guide for building Blazor apps with Microsoft Fluent UI components. \n \n Covers setup (service registration, mandatory providers), component patterns (lists, dialogs, forms), and icons via a separate NuGet package with strongly-typed variants and sizes \n Explains critical patterns: FluentSelect / FluentAutocomplete use Items , OptionText , and SelectedOption binding (not <option> children), and dialogs use IDialogService with content components, not visibility toggling \n Pr

Update specification files for AI-ready consumption based on new requirements or code changes. \n \n Modifies existing specification documents in /spec/ directory using structured markdown with YAML front matter \n Ensures specifications are precise, unambiguous, and machine-readable through established formatting standards (headings, lists, tables, code blocks) \n Covers 11 core sections: purpose, definitions, requirements, interfaces, acceptance criteria, testing strategy, rationale, dependenc

Verify Microsoft SDK methods, find working code samples, and catch hallucinated APIs against official docs. \n \n Three core tools: microsoft_docs_search for API lookups, microsoft_code_sample_search for working examples in C#/Python/JavaScript, and microsoft_docs_fetch for full reference pages with overloads \n Catches common mistakes like wrong method signatures, deprecated patterns, mismatched SDK versions (v11 vs v12), and incorrect package names \n Works with Azure SDKs, .NET libraries, and