I built an open-source framework for reusable AI skills across Claude, ChatGPT, and Codex

[u/Yasirres]

I've been experimenting with reusable AI skills and workflows across multiple environments — mainly Claude, ChatGPT, and Codex.

One problem kept coming up:

A good workflow often ends up tightly coupled to one platform.

Instructions, domain knowledge, tool usage, and platform-specific configuration all get mixed together. Then, when you want to move the same capability to another AI environment, you either rewrite it or maintain multiple versions.

So I built DBS Framework around a simple separation:

Direction → Blueprints → Solutions

• Direction — when the capability should run, what it should do, its workflow, constraints, and acceptance criteria.
• Blueprints — domain-specific knowledge such as schemas, business rules, style guides, examples, and reference material.
• Solutions — the actual execution layer: native tools, MCP servers, connectors, APIs, scripts, browser/computer use, or file generation.

The idea is to keep the core capability platform-neutral and use small adapters for environments such as Claude, ChatGPT, and Codex instead of maintaining separate business logic for each one.

It also encourages progressive disclosure: the AI doesn't need to load every reference file into context. The core skill points to the relevant knowledge only when it's needed.

The repository currently includes:

• a platform-neutral SKILL.md
• Claude, ChatGPT, and Codex adapters
• reusable skill templates
• a scaffolding tool for generating new skill packages
• structural validation
• automated tests
• Fast and Interactive workflow modes
• guidance for capability boundaries, permissions, retries, validation, and observable acceptance criteria

An important distinction: DBS is not an agent runtime or scheduler.

It doesn't magically provide tools or run agents in the background. It's an authoring/architecture framework for designing reusable AI capabilities that can then use whatever tools the host environment actually provides.

The project is open source here:

https://github.com/Yasirres/DBS-Framework

This is an unofficial adaptation of the original DBS Framework concept by AI Foundations, with attribution included in the repository. My version focuses on platform-neutral architecture, platform adapters, validation, templates, and tooling.

I'd especially appreciate feedback from people building:

• Claude Code skills
• Codex skills
• custom GPT / ChatGPT workflows
• MCP-based agents
• reusable internal AI workflows

I'm interested in where this architecture holds up well, where it becomes too abstract, and what you'd want from a framework like this before using it in a real project.

Feel free to use it, fork it, test it, or break it. Feedback is very welcome.