Code in this crate should follow the Microsoft Rust Guidelines. Each middleware has two logic-equivalent Service trait impls in the same file: one for layered::Service (execute) and one for tower_service::Service (call). The business logic flow must be identical between the two — they differ only mechanically (boxed futures, poll_ready, Arc cloning, Result output type). Any change to execute must be mirrored in call, and vice versa. Prefer adding logic to the shared *Shared helper structs rather than inlining into both…
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When adding a new example, update README.md with a link to the new file and a brief one-line description. Keep the list in the same order: timeout, retry, breaker, fallback, combined pipelines, then configuration. Also synchronize the Examples section in the lib.rs documentation.
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Agents are autonomous actors that make decisions and interact in the marketplace. Each agent has a profile, runs in a continuous loop, and executes actions through the platform. For our marketplace, we created two basic agents that interact: a CustomerAgent and a BusinessAgent. Each agent inherits from a BaseAgent class which handles the agent loop to register the agent, then continuously run by calling the step() function. The customer agent implements shopping behavior: searches for businesses, sends inquiries, evaluates proposals,…
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This document provides guidance for AI agents assisting with the Microsoft Fabric CLI (fab) — a Python-based command-line interface for Microsoft Fabric. The Fabric CLI is a file-system-inspired command-line interface that lets users explore, automate, and script their Microsoft Fabric environment. Key characteristics: Repository: https://github.com/microsoft/fabric-cli All AI-related resources (skills, context, prompts, modes) are organized in the .ai-assets/ folder. See .ai-assets/README.md for a complete catalog of available resources. When assisting with code contributions to this repository (adding features, fixing bugs, writing…
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Version 1.0.0 Engineering January 2026 Note: This document is mainly for agents and LLMs to follow when maintaining, generating, or refactoring React codebases using composition. Humans may also find it useful, but guidance here is optimized for automation and consistency by AI-assisted workflows. Composition patterns for building flexible, maintainable React components. Avoid boolean prop proliferation by using compound components, lifting state, and composing internals. These patterns make codebases easier for both humans and AI agents to work with as they…
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Version 1.0.0 Vercel Engineering January 2026 Note: This document is mainly for agents and LLMs to follow when maintaining, generating, or refactoring React and Next.js codebases. Humans may also find it useful, but guidance here is optimized for automation and consistency by AI-assisted workflows. Comprehensive performance optimization guide for React and Next.js applications, designed for AI agents and LLMs. Contains 40+ rules across 8 categories, prioritized by impact from critical (eliminating waterfalls, reducing bundle size) to incremental (advanced patterns). Each…
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Load this guide when work should be handled by a visible Agent conversation instead of direct Space operations. An Agent may use Huabu's default Profile or another available Profile. The entry marked default: true is the first selectable external Profile used when Agent creation omits profileId. If no entry is marked, explicitly choose an available Profile; do not assume Built-In Pi is an implicit fallback. The huabu Profile uses Huabu's configured model provider. Other Profiles use their own configured runtimes…
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Load this guide when work should be handled by a visible Agent conversation instead of direct Space operations. An Agent may use Huabu's default Profile or another available Profile. The entry marked default: true is the first selectable external Profile used when Agent creation omits profileId. If no entry is marked, explicitly choose an available Profile; do not assume Built-In Pi is an implicit fallback. The huabu Profile uses Huabu's configured model provider. Other Profiles use their own configured runtimes…
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Follow .github/copilot-instructions.md for repository-specific development guidance. Before filing a GitHub issue in this repository: Before opening a pull request:
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MOAW is a repository of workshop content plus the tooling to create, preview, and publish workshops on the MOAW website. Run from the repository root: From CONTRIBUTING.md:
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This repository contains an AI agent evaluation toolkit for Copilot Studio, grounded in Microsoft's **Practical Guidance on Agent Evaluation — a 10-step playbook**. The canonical methodology spine is skills/eval-guide/playbook.md. Helps users go from "I don't know where to start with eval" to "I have a plan, test cases, and know how to interpret results" — in one session. No running agent required for planning and test generation. This toolkit provides 6 prompt files in .github/prompts/. When the user's request matches…
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This is the Copilot Studio Extension for Visual Studio Code - a hybrid TypeScript/C# project that provides a full-featured VS Code extension for editing Microsoft Copilot Studio agents. The extension enables developers to clone agents locally, edit components (topics, triggers, actions, knowledge sources) in YAML format, and sync changes bidirectionally with cloud environments. IntelliSense is powered by a Language Server Protocol (LSP) backend written in C#. Current State: Generally Available (GA) Documentation: https://learn.microsoft.com/en-us/microsoft-copilot-studio/visual-studio-code-extension-overview Build constraint: Full .NET restore/build currently depends…
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This is a Python project that uses Microsoft Agent Framework--the new unified orchestration framework that combines the best of: To build a multi-agent event planning workflow. When writing complex features or significant refactors, use an ExecPlan (as described in specs/PLANS.md) from design to implementation. If the user request requires multiple specs, create multiple specification files in the specs/ directory. After creating the specs, create a master ExecPlan that links to each individual spec ExecPlan. Update the specs/README.md to include links…
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This file is read by AI coding agents (GitHub Copilot, Claude, Cursor, Codex, etc.) when working in this repository. It collects the small set of project-specific conventions that aren't obvious from the source itself. Mirrored at .github/copilot-instructions.md. GitHub Copilot's chat reads that file directly; other tools read this one. Keep the two in sync — they cover the same guidance, though formatting and some sections differ between them. api/hastefuncapi/function_app.py (and its sibling api/hastefuncqueues/function_app.py) should contain only top-level @app.route(...) handlers —…
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This repository is the Awesome Rayfin template gallery, not a single Rayfin app. Work primarily in templates/ and keep gallery metadata, manifests, and docs in sync. Templates must work on both Windows and Unix-like systems (macOS/Linux). When adding or modifying scripts: Use Conventional Commits: <type>(<scope>): <description>. See CONTRIBUTING.md for the full reference.
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MicrosoftAGENTS.md