DBS Framework

Direction / Blueprints / Solutions — a platform-neutral architecture for reusable AI skills, agents, and workflow systems.

DBS separates workflow decisions, specialized knowledge, and execution. Maintain one core and adapt it to Claude, ChatGPT, and Codex through small platform adapters. This is an authoring framework, not a hosted agent runtime or scheduler.

Origin: this is an unofficial adaptation of The DBS Framework Skill by AI Foundations. Credit for the original DBS framework and source skill belongs to AI Foundations. Developed by this repository's maintainer from their original work, this version adds platform-neutral guidance, separate adapters, modes, and tooling; it is not an official AI Foundations release or an endorsed project.

Attribution and permission: the maintainer confirms that AI Foundations permits publication of this adaptation with a link to their website and clear source credit. See ATTRIBUTION.md for that record and LICENSE for the separate MIT scope covering new tooling.

Start here

1. Read SKILL.md or provide it to your AI environment.
2. Use the Claude, ChatGPT, or Codex adapter for setup and capability mapping.
3. Ask: “Use DBS in Fast Mode to build a reusable monthly report workflow from a supplied CSV. Validate totals and deliver a local PDF. Do not publish it.”
4. Review the resulting contract and run representative tests before relying on it.

For a plain chat, explicitly provide the instructions and required references. For a skill-aware host, install the package using that host's supported location or import flow. Adapters are guidance, not installed integrations. Tools and permissions depend on the actual environment. See validation status.

The architecture

Use only necessary layers. A production capability may need Direction alone, Direction + Solutions, or all three. Read principles.

Fast Mode produces a complete draft from clear inputs with a consolidated review. Interactive Mode resolves consequential uncertainty through focused questions. Both preserve the user's authorization and host permissions.

DBS applies to designing reusable capabilities. “Remind me tomorrow,” “run this existing task,” and one-off edits do not activate it merely because they mention automation or workflows. An explicit request to use DBS remains supported.

Repository map

dbs-framework/
├── SKILL.md
├── README.md
├── CONTRIBUTING.md
├── LICENSE
├── ATTRIBUTION.md
├── CHANGELOG.md
├── .gitignore
├── .github/workflows/ci.yml
├── references/
│   ├── dbs-principles.md
│   ├── testing-guide.md
│   └── platforms/{claude,chatgpt,codex}.md
├── templates/
│   ├── generic-skill-template.md
│   ├── claude-skill-template.md
│   ├── chatgpt-skill-template.md
│   └── codex-skill-template.md
├── scripts/{validate_skill,scaffold_skill}.py
├── tests/test_tools.py
└── docs/VALIDATION.md

Generate a starter

Python 3.10+; no third-party packages or network access required. Run from the repository root (Windows users can substitute py for python if needed):

python scripts/scaffold_skill.py monthly-report --output ./examples --platform codex --description "Create monthly reports from supplied CSV data when a report is requested."
python scripts/validate_skill.py ./examples/monthly-report --allow-draft

Choose generic, claude, chatgpt, or codex. The generator creates a new directory and refuses an existing destination. It does not install anything or generate task-specific implementation. Complete the TODOs before normal validation. Templates carry starter markers intentionally; generated platform packages contain a local adapter and do not depend on a remote DBS repository at runtime.

Validate this repository and run tests:

python scripts/validate_skill.py . --repository
python -m unittest discover -s tests -v

The validator uses a documented, restricted frontmatter profile, not a full YAML parser. Read testing and limitations before using it to check packages with platform-specific metadata.

Publish and contribute

See CONTRIBUTING.md for contribution and compatibility checks.

Published release: v1.0.0, released on 2026-09-13. Changes after that release are recorded under Unreleased in CHANGELOG.md. Adapted from AI Foundations' DBS skill architecture guide supplied by the user. The original Direction / Blueprints / Solutions model and progressive disclosure are preserved; shared instructions are now platform-neutral.

License

MIT covers only the newly authored Python scripts, Python tests, and CI configuration. The adapted documentation and templates are not offered under MIT: the reported permission covers publication with attribution, not MIT relicensing of the original material. See ATTRIBUTION.md. Vendor services and third-party integrations have their own terms.