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.   submitted by   /u/Yasirres [link]   [comments]