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About SpecDrivenStore

"Don't download software. Build it."

What is Spec-Driven Software?

Spec-driven software is a new distribution paradigm where software is shared not as a compiled binary or source code, but as a high-fidelity specification document — typically a SPEC.md file hosted in a GitHub repository.

This specification contains enough detail for any capable AI coding agent to reproduce the complete system, tailored to the builder's chosen stack, language, and environment.

📄 SPEC.md → 🤖 AI Agent → ✨ Your Software

Instead of downloading an .exe or .dmg, you point your AI agent at a SPEC.md and it builds the software.

Why SpecDrivenStore?

👨‍💻

For Publishers

Showcase your spec-driven projects, gain visibility, and get community feedback through emoji reactions.

🔨

For Builders

Discover software you can build yourself using AI agents, customized for your exact stack and needs.

🌍

For the Ecosystem

Establish a standard for spec-driven distribution and build a knowledge base for the community.

🚀

For the Future

We believe the post-binary era is here. Software as specification is the next evolution of software distribution.

Frequently Asked Questions

Software distributed as a specification document (typically SPEC.md) that an AI coding agent can use to reproduce the complete system — tailored to any stack.
Find a spec you like, copy the SPEC.md URL, open your AI coding agent (Claude Code, Codex, Cursor, etc.), and paste: "Implement this specification: [URL]". The agent does the rest.
Any capable AI coding agent works: Claude Code, OpenAI Codex, Cursor, Windsurf, Aider, and others. The spec is agent-agnostic.
Your project needs a public GitHub repository. Ideally include a SPEC.md file. Go to Submit, enter your GitHub URL, select a category, and submit. Our team reviews it within a few days.
Yes, completely free for publishers and builders.

Further Reading

  • 🎵
    Symphony by OpenAI

    The canonical example of spec-driven software distribution.

  • 📝
    Harness Engineering

    The practice of designing infrastructure and constraints that make AI agents reliably productive.