AIAny
AI Agent2025
Icon for item

HVE Core

Packages reusable GitHub Copilot building blocks — agents, prompts, instructions, and skills — to make AI-assisted coding repeatable and standards-aligned for a team. Built around an RPI (Research, Plan, Implement) workflow in VS Code.

Introduction

Most teams adopt GitHub Copilot the way they once adopted Stack Overflow: ad hoc, person by person, with no shared muscle memory. HVE — Hypervelocity Engineering — is Microsoft's attempt to turn that improvisation into a repeatable discipline, shipping the agents, prompts, instructions, and skills as version-controlled artifacts you commit to the repo instead of habits locked in one engineer's head.

What Sets It Apart
  • Built on an explicit RPI loop — Research, Plan, Implement — so a feature moves through named agents (task-researcher, rpi-agent, memory) rather than one open-ended chat. The process becomes reviewable, not just the code.
  • Treats Copilot configuration as source: instructions auto-apply coding standards, skills package reusable tool capabilities, and prompts act as reusable entry points — all diffable and shareable across a team.
  • Ships "validated artifacts," meaning workflow outputs are checked against conventions rather than trusted blindly.
Who It's For

Great fit if you run a team standardizing on Copilot and want every engineer to follow the same research-plan-implement path, with AI behavior captured in the repo. Look elsewhere if you work solo and prefer free-form chat, don't use GitHub Copilot, or want a model-agnostic agent framework — HVE is purpose-built for the Copilot and VS Code ecosystem, and is heavily PowerShell plus Python under the hood.

Information

  • Websitegithub.com
  • AuthorsMicrosoft
  • Published date2025/11/02

Categories

More Items

GitHub
AI Client2024

Unifies visual creator tools, reusable Skills, and a Codex-powered Agent to handle video translation, image/video generation, voiceovers, and editing in one local multimodal workspace. Desktop and web clients share a synchronized state machine and keep data local by default.

GitHub
AI Coding2025

Runs many shells inside one terminal and treats coding agents as first-class panes. Combines daemon-backed persistent sessions, vim-style modal control, BSP tiling, a session rail and an Inbox that surfaces approvals, questions and finished turns from LLM-based coding agents — useful when you run and manage agent workflows alongside shells.

GitHub
AI Agent2026

Audits deployed AI agents for governance and operational misalignment, running up to 60 inspections that detect deception, manipulation, fabrication, unpredictability and opacity; outputs JSON/Markdown scorecards, a graded A–F score, and optional Pro reports and dashboards.