Hypervelocity Engineering (HVE) Core

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HVE Core helps teams ship faster with GitHub Copilot by combining specialized agents, reusable prompts, coding instructions, and validated skills into one workflow system.

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title: HVE Core description: Hypervelocity Engineering prompt library for GitHub Copilot with convention-driven AI workflows and validated artifacts author: Microsoft ms.date: 2026-07-23 ms.topic: overview keywords: - hypervelocity engineering - prompt engineering - github copilot - ai workflows - custom agents - copilot instructions - rpi methodology estimated_reading_time: 3

Hypervelocity Engineering (HVE) Core

HVE Core helps teams ship faster with GitHub Copilot by combining specialized agents, reusable prompts, coding instructions, and validated skills into one workflow system.

Use HVE Core when you want AI-assisted work to be repeatable, standards-aligned, and scalable across individuals and teams. HVE Core provides structured AI workflow building blocks:

  • Agents for specialized tasks such as research, planning, implementation, and review
  • Prompts for repeatable workflow entry points
  • Instructions that apply coding standards automatically
  • Skills that add reusable tool capabilities

[!CAUTION] HVE Core is a highly opinionated, rapidly evolving agentic SDLC framework. It is best treated as a source of patterns and learning rather than a stable platform, foundation, or production dependency. Workflows, interfaces, architecture, and recommended practices may change substantially, including in ways that are not backward compatible, as the technology landscape evolves. Evaluate all materials for your own requirements and risk tolerance. The HVE Builder skill (use with /hve-builder) and GitHub Copilot can help you adapt or copy relevant patterns into an agentic SDLC that you own and maintain independently. To build an independent implementation, start with Forking and Extending HVE Core and review the HVE Core documentation before adopting any component.

Where to Start

  1. Install the HVE Core extension from the VS Code Marketplace.
  2. Open any project and launch GitHub Copilot Chat (Ctrl+Alt+I).
  3. Select RPI Agent from the agent picker or run /rpi, then describe the task you want to complete.

[!TIP] Use HVE Core All Extension when you want the full collection deployment. See Collections Overview.

[!TIP] Using GitHub Copilot CLI? Install as a plugin instead:

copilot plugin marketplace add microsoft/hve-core
copilot plugin install hve-core@hve-core

See CLI Plugins for usage details.

Choose Your Path

  • New to HVE-Core: Start with Start Here to complete your first workflow quickly.
  • Leading a team: Use the Team Adoption Guide to roll out standards and onboarding.
  • Contributing to this repo: Follow the Contributing Guide to add or improve agents, prompts, instructions, and skills.

Navigate This Repository

Goal Go here
Getting Started docs/getting-started/README.md
Understand all setup options docs/getting-started/install.md
Learn the core methodology docs/rpi/README.md
Browse docs by topic docs/README.md
Explore agents .github/CUSTOM-AGENTS.md
Explore instructions .github/instructions/README.md
Explore prompts .github/prompts/README.md
Explore skills .github/skills/

Documentation

Full documentation is available at https://microsoft.github.io/hve-core/.

Guide Description
Getting Started Setup and first workflow tutorial
Collections Available bundles and selection guide
RPI Workflow Deep dive into Research, Plan, Implement, Review
Contributing Create custom agents, instructions, and prompts
Agents Reference All available agents
Instructions Reference All coding instructions
AI Artifacts Architecture Prompt engineering framework and artifact types
Validation Standards CI/CD validation pipeline and quality gates

Label Management

Repository labels are declared in .github/labels.yml and synced automatically by the Label Sync workflow on push to main or via manual workflow_dispatch.

Task How
Add a label Add an entry with name, color (bare hex, no #), and description to .github/labels.yml, then push to main
Update a label Edit the existing entry's color or description
Rename a label Add an aliases array under the new canonical name listing the old name; the sync migrates existing assignments automatically
Delete a label Remove it manually in the GitHub Labels UI. Deleting an entry from the file does not delete it from GitHub (the workflow runs in additive mode)

Contributing

We appreciate contributions! Whether you're fixing typos or adding new components:

  1. Read our Contributing Guide.
  2. Check out open issues.
  3. Join the discussion.

Responsible AI

Microsoft encourages customers to review its Responsible AI Standard when developing AI-enabled systems to ensure ethical, safe, and inclusive AI practices. Learn more at Microsoft's Responsible AI.

Legal

This project is licensed under the MIT License.

Licensing

Most content in this repository is covered by the MIT License. Certain skill content derived from OWASP Foundation publications is licensed under CC BY-SA 4.0. Each affected skill identifies its license in frontmatter and includes a Third-Party Attribution section. See THIRD-PARTY-NOTICES for full details.

See SECURITY.md for the security policy and vulnerability reporting.

See GOVERNANCE.md for the project governance model.

See TRANSPARENCY-NOTE.md for the Responsible AI Transparency Note covering intended uses, limitations, and the responsibility boundary between HVE Core and the host platform.

Trademark Notice

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.


🤖 Crafted with precision by ✨Copilot following brilliant human instruction, then carefully refined by our team of discerning human reviewers.

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MDRSS ASSESSMENT
Evidence46/100medium confidence
Why MDRSS assigned this score
  • Production catalog audit 2026-08-04
  • Taxonomy classified from title, annotation, source and Markdown signals
  • Agent usefulness evaluated from structure, procedures, examples, evidence and retrieval value
Evidence (1)

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