microsoft/hve-core
A refined collection of Hypervelocity Engineering components (instructions, prompts, agents, and skills) to start your project off right, or upgrade your existing projects to get the most out of GitHub Copilot
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
No releases in the last 90 days; the most recent was 185 days ago, with 101 contributors on GitHub.
Use Cases & Benefits
- Provides a prompt engineering framework with specialized agents, instructions, prompts, and skills to enhance GitHub Copilot workflows using constraint-based AI design.
- Enables scalable, validated AI workflows with JSON schema enforcement and a structured RPI methodology to ensure reliable, enterprise-grade AI-assisted development.
- Use for integrating advanced AI agents and reusable prompts to accelerate coding tasks in individual or team projects using GitHub Copilot.
- Use for enforcing coding guidelines and quality checks automatically through instruction sets and validation pipelines in CI/CD environments.
- Use for developing custom AI personas and cross-platform executable skills to extend GitHub Copilot capabilities for specialized software engineering workflows.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Hypervelocity Engineering (HVE) Core is an enterprise-ready prompt engineering framework for GitHub Copilot. Constraint-based AI workflows, validated artifacts, and structured methodologies that scale from solo developers to large teams.
HVE Core provides specialized agents, reusable prompts, instruction sets, and skills with JSON schema validation. The framework separates AI concerns into distinct artifact types with clear boundaries, preventing runaway behavior through constraint-based design.
The RPI (Research → Plan → Implement) methodology structures complex engineering tasks into phases where AI knows what it cannot do, changing optimization targets from "plausible code" to "verified truth."
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+277 stars over 90 days across 8 snapshot days; best day +15 on Aug 29.
Forks
342+104
Open issues
229+3
Compared with peers in PowerShell
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| Raphire/Win11Debloat | 61.7K | — | 60 | 4 days ago |
| zhaoxuya520/reverse-skill | 43.6K | — | 30 | 80 days ago |
| zoicware/RemoveWindowsAI | 13.6K | — | 17 | — |
| actions/runner-images | 13.5K | — | 45 | today |
| psmux/psmux | 3.8K | — | 54 | 15 days ago |
| microsoft/hve-core | 1.6K | — | 55 | 185 days ago |
| Peer median | 13.6K | — | 45 | — |
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