wind7rui

wind7rui / HyperSpec

Public

规格驱动 + 工程纪律的完整开发工作流 Skill，协调 OpenSpec（规格管理）和 Superpowers（TDD + 子代理审查），从需求到实现到归档一条流程走完。

17
3
85% credibility
Found May 20, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

HyperSpec is a structured development workflow that helps AI coding assistants build features the right way. Instead of jumping straight into code, it first ensures you and your AI assistant agree on exactly what needs to be built by creating detailed specifications. Then it breaks the work into small steps, builds each piece while checking for mistakes, and saves everything so you can pick up where you left off anytime. The system works with many programming languages and automatically adapts to your project's specific tools and setup.

How It Works

1
💡 You have an idea

You think of a new feature or improvement for your project, like adding user login or an export button.

2
🤖 You ask your AI assistant for help

You simply tell your AI coding assistant what you want to build, and it automatically knows to follow the right process.

3
📝 You define exactly what you need

The system helps you write down the complete specification: what the feature should do, how it should work, and what it should look like.

4
🗺️ Your assistant creates a roadmap

Based on your specification, the AI breaks down the work into small, manageable steps that can be checked off one by one.

5
Your assistant builds while you watch
📦
Small project

Your assistant builds everything directly in one session

🏗️
Large project

Your assistant breaks work into parallel tasks that work together

6
✅ Everything gets verified

Before finishing, the system checks that all the code works correctly and follows good practices.

🎉 Your feature is complete

Your new feature is built, tested, and documented. Everything is saved and organized for future reference.

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AI-Generated Review

What is HyperSpec?

HyperSpec is a workflow orchestration skill for AI coding assistants that enforces a specification-driven development process. It coordinates two existing tools—OpenSpec for managing requirement documents and Superpowers for TDD plus code review—into a single coherent pipeline. The workflow moves through three phases: propose (where specs are written before any code), apply (where implementation happens with verification), and archive (where consistency is validated and changes are finalized). It auto-detects your project's language and framework to adapt its behavior, supporting Java, Node, Go, Rust, and Python out of the box. You invoke it by typing natural language commands like "use hyperspec to develop user authentication."

Why is it gaining traction?

The main appeal is the hard separation between specification and implementation phases. Many AI coding tools jump straight to code, which leads to scope creep and missed requirements. HyperSpec forces you to write specs first, then locks those specs during implementation so you cannot quietly change requirements mid-stream. The breakpoint recovery system is practical—if you interrupt a session, it resumes exactly where you left off using state files plus actual file verification. This discipline appeals to teams tired of AI-generated code that works but does not match what was actually requested.

Who should use this?

Development teams using Claude Code, Cursor, or Codex who want stronger guardrails around AI-assisted coding. It works best when you have multi-step features where requirements tend to drift during implementation. Individual developers working on solo projects may find the overhead excessive unless they value formal specification discipline. The tool assumes you are already using or willing to adopt OpenSpec and Superpowers.

Verdict

HyperSpec addresses a real problem—unstructured AI coding—but the credibility score of 0.85% and only 17 stars indicate it is early-stage with minimal community validation. The documentation is thorough, which is encouraging, but the lack of actual code artifacts in the repository makes it hard to assess implementation quality. Try it if you are already invested in the OpenSpec/Superpowers ecosystem and want orchestration between them. Otherwise, wait for more adoption and real-world testing before betting a production workflow on it.

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