This document provides setup, architecture, coding, and testing instructions for the web extension located in src/web. It is designed to help both humans and AI agents contribute effectively. The web extension extends Visual Studio Code for Power Platform scenarios. All source code for the web extension is located in the src/web directory. To build the web extension, run: This will compile the extension and output the necessary files.
This repository keeps its canonical Copilot instruction files in the .github folder and its reusable agent skills in .agents/skills. Before starting work, read .github/copilot-instructions.md for repository-wide guidance. Then use the relevant topic-specific instruction files in .github/instructions/: - addingnewparser.guidelines.instructions.md - bugfixing.guidelines.instructions.md - databaseoption.guidelines.instructions.md - debuggingworkflow.guidelines.instructions.md - function.guidelines.instructions.md - grammarvalidation.guidelines.instructions.md - grammar.guidelines.instructions.md - newdatatypes.guidelines.instructions.md - newindextypes.guidelines.instructions.md - parser.guidelines.instructions.md - testing.guidelines.instructions.md When a reusable skill applies, load it from .agents/skills/: - add-feature/SKILL.md - verify-and-test-tsql-syntax/SKILL.md Version note: - Treat TSql180 as the current vNext/latest…
Make changes that are testable, documented, backward-compatible where practical, and R CMD check-clean. If a required check cannot run, state why and name the command that remains to be run.
processes/ contains markdown files that act as the control flow — an LLM reads and executes them. src/clarity_agent/ contains Python infrastructure those processes call. clarity.py is the entry point. processes/clarity-agent.md is "main". It checks whether .clarity-protocol/ exists, runs packet_status.py to assess state, and routes to the correct process guide. After any process completes, control returns here. src/clarityagent/protocol/packetstatus.py is the build system / flow controller. It maintains a SHA-256 dependency graph among protocol documents (stored in config.json) and detects when a…
This file integrates the Clarity Protocol into your AI-assisted development workflow. The Clarity Protocol helps you build systems that actually work by maintaining shared understanding of what you're building, why, and what could go wrong. When building software with AI assistance, three things commonly go wrong: The Clarity Protocol solves this by creating a .clarity-protocol/ directory alongside your code that captures: Think of it as the "missing manual" for your code—the stuff you can't figure out just by reading the…
Azure Toolkits for Java — IDE plugins for IntelliJ IDEA and Eclipse that help Java developers create, develop, configure, test, and deploy applications to Azure. The primary focus of active…
This library exists to locate an MSBuild install (Visual Studio or .NET SDK) and register an assembly-resolution handler so the host app loads MSBuild's assemblies from that install. This is…
Follow these conventions to produce consistent, high-quality contributions. Skala is a neural network-based exchange-correlation (XC) functional for density functional theory (DFT). The codebase includes: - Formatter/linter: Ruff (ruff format, ruff check --fix --select I). - Type checking: mypy in strict mode. Ignore missing type information only for explicitly named untyped dependencies in pyproject.toml; do not use global --ignore-missing-imports. - Line length: 100 characters (Black-compatible). - Imports sorted via Ruff's isort rules. - Generated build, coverage, and documentation output is excluded…
This directory contains a standalone .NET 10 implementation of the Azure MCP server plugin-telemetry command. Read README.md for user-facing build, packaging, and usage documentation. This file defines how coding agents…
For AI Agents: This document provides patterns and conventions for creating and maintaining tests for azure-skills plugin. When asked to "scaffold tests" for a skill, follow the instructions below. When…
The microsoft/community-content repository provides prepared "content-in-a-box" presentation materials for user groups, meetups, and community events. This repository contains PowerPoint presentations, supporting documentation, demo resources, and marketing materials for technical talks…
Before making any changes to this repository, you must read and follow the instructions in .github/copilot-instructions.md. That file contains the canonical coding standards, formatting rules, and documentation requirements for this project. All code contributions — whether from humans or AI agents — must conform to those guidelines. This repository defines custom agents in .github/agents/. Use /agent in Copilot CLI to browse and select them, or reference them by name in a prompt.
Instructions for AI coding agents working on the Retrospectives Extension for Azure DevOps. Full-stack Azure DevOps extension: Imports - organize in order: Formatting (Prettier config): TypeScript (tsconfig): Naming Conventions: Component Pattern: Follow C# Coding Conventions: Frontend: try/catch with telemetry:
The Model Context Protocol (MCP) .NET Samples repository demonstrates how to build MCP servers using .NET 9.0. This repository contains four comprehensive sample implementations that showcase different aspects of MCP server development, from GitHub integration to web services, data management, and email communications.
Canonical entry point for humans and coding agents working in this repository. Start here, then follow the links below to the detailed docs and standards. What this repo is: a proof-of-concept toolkit that deploys a Microsoft Intune device configuration (policies, compliance, scripts, apps, optional Microsoft Defender for Endpoint) into a tenant from a single script. It is sample/evaluation code, not a hardened production baseline — see README.md. Classification: non-production. Trust calibration: what to treat as today's truth vs. historical/context-only. Trust…
V8-JSI implements the JavaScript Interface (JSI) on top of the V8 JavaScript engine, bridging native code and JavaScript. It ships as v8jsi.dll and is consumed mainly by React Native for…
Chapter Navigation: - 📚 Course Home: AZD For Beginners - 📖 Current Chapter: Chapter 2 - AI-First Development - ⬅️ Previous: Microsoft Foundry Integration - ➡️ Next: AI Model Deployment - 🚀 Advanced: Multi-Agent Solutions AI agents are autonomous programs that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike simple chatbots that respond to prompts, agents can: This guide shows you how to deploy AI agents to Azure using Azure Developer CLI (azd). Validation…
تنقل الفصل: - 📚 الصفحة الرئيسية للدورة: AZD للمبتدئين - 📖 الفصل الحالي: الفصل 2 - التطوير الأول للذكاء الاصطناعي - ⬅️ السابق: تكامل Microsoft Foundry - ➡️ التالي: نشر نموذج الذكاء الاصطناعي - 🚀 متقدم: حلول متعددة الوكلاء وكلاء الذكاء الاصطناعي هم برامج مستقلة يمكنها إدراك بيئتها، واتخاذ القرارات، واتخاذ الإجراءات لتحقيق أهداف محددة. على عكس روبوتات الدردشة البسيطة التي تستجيب للمطالبات، يمكن للوكلاء: يوضح هذا الدليل كيفية نشر وكلاء الذكاء الاصطناعي على Azure باستخدام Azure Developer CLI (azd).
Навигация в глава: - 📚 Начало на курса: AZD За начинаещи - 📖 Текуща глава: Глава 2 - AI-Първо разработване - ⬅️ Предишна: Интеграция на Microsoft Foundry - ➡️ Следваща: Деплоймънт на AI модел - 🚀 Напреднали: Решения с множество агенти AI агентите са автономни програми, които могат да възприемат околната среда, да вземат решения и да предприемат действия за постигане на конкретни цели. За разлика от простите чатботове, които отговарят на заявки, агентите могат: Тази инструкция показва как да…
MicrosoftAGENTS.md