Code in this repository should follow the guidelines specified in the Microsoft Rust Guidelines. Use the project simple-english skill for crate documentation and other human-facing documentation. Crate README files are auto-generated via just anvil-readme --fix. Do not manually update them. Because they are generated, do not edit them by hand and do not raise their formatting, wording, or casing (for example the title-cased crate name) as issues in code review - such content is not authored here and is regenerated…
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- Preserve the caller-facing contract in docs/PANICS.md. Keep implementation rules in this file rather than exposing them as public guarantees. - Catch panics from code Arty explicitly runs for the user: async task polling, task factories, and blocking callbacks. - A contained task or callback panic becomes JoinError without replacing the original panic. - Apply the configured Panic hook to async task factories and polls. Forward its ShutdownCause without another panic decision. - A blocking callback panic returns through its…
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Code in this crate should follow the Microsoft Rust Guidelines. These assumptions shape how we write and optimize code in this crate. Keep them in mind when proposing or reviewing changes, especially performance-related ones. Our types are intended to be used in a thread-isolated architecture where mutexes are almost never contended. Any mutex that exists does so for safety in degenerate scenarios (e.g. memory allocated on one thread but released on another), not because we expect concurrent access on the…
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A real-network HttpClient requires a TLS backend. The tokio feature alone is not enough because the hyper transport requires TLS. Enable rustls or native-tls with tokio. Use this conditional compilation pattern: If you add a TLS backend, add its feature to the inner any(...). Some items use one TLS backend or test. TLS error labels use this pattern: test-util enables FakeHandler, which returns canned responses without network I/O. It does not require a TLS backend.
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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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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…
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This document outlines rules and requirements for AI automation agents and automated tooling that interact with the Node.js project. Existing Node.js collaborators (as listed in the README.md) may use AI…
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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…
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Help the learner complete this existing ticketing lab. Keep the application and UI unchanged. Read README.md and .github/skills/onboarding-lab/SKILL.md first. You are the local coding assistant. You can inspect this checkout, prepare configuration and run approved setup operations. The deployed Azure SRE Agent investigates the workload and guides the exercises. Do not claim it persisted knowledge merely because you edited a local file or it agreed in conversation. - Identify whether the learner has an assigned agent, an existing azd environment,…
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Guidance for any AI agent (GitHub Copilot CLI/IDE, Claude Code, Cursor, Codex, etc.) working in this repo. Azure SRE Agent demo — AKS + PostgreSQL e-commerce app with break/fix scenarios and autonomous incident response. When a user clones this repo, guide them through setup: 1. Check prerequisites: az, azd, pwsh, plus Owner/User Access Administrator (or equivalent role-assignment write permission) at subscription scope — install anything missing. (kubectl is not required on the operator workstation: the AKS cluster is private, operator…
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Guidance for humans and agents extending this lab. The overriding principle: Alerts describe what a student or operator observes, never the cause. The whole point of the lab is that the SRE Agent diagnoses root cause from telemetry and live configuration. The same symptom (Zava-quiz-launch-failing) is reachable from multiple causes (NSG priority inversion, App Gateway probe, an API at zero replicas). Routing is by symptom, not cause. Likewise, the NSG fault rule is named plausibly (legacy-cross-subnet-deny), not the-bug — the…
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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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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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MicrosoftAGENTS.md