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Contributing

These repositories are open, voluntary research and documentation projects within the Frozen Kernel ecosystem — all focused on safe, sovereign human-AI collaboration.

Everything here is a living draft. Contributions right now are genuinely useful and directly help shape the work.

What’s Most Needed

  • Real-world testing & replication — Try the practices, diagnostics, scorecards, protocols, or field-guide tools and share what actually happened (positive results, failures, edge cases, or null results).
  • Clarity and usability feedback — Edits that make things easier to read, better examples, additional citations, or improved diagrams/tables.
  • New case studies or examples — Especially from creative, therapeutic, enterprise, research, or high-stakes domains.
  • Translations or accessibility improvements — Helping more people access the material.
  • Cross-repository connections — Noticing useful links or extensions between the repos (e.g., applying a Field Guide skill to a Dimensional Authorship case).
  • Any open questions listed in the repo’s README.

How to Contribute

  • Open an Issue to ask questions, flag gaps, suggest improvements, propose alternatives, or point to related work.
  • Open a Pull Request for direct changes to text, tables, diagrams, or new sections.
  • Share independent implementations or adaptations — even if your approach differs, a link or reference is valuable.

What This Is Not Looking For

  • Suggestions to replace deterministic safety mechanisms or the Frozen Kernel foundation with purely probabilistic or “trust the model” approaches.
  • Proposals that remove or weaken human authorization from the decision loop.
  • Feature requests that expand scope beyond safe, sovereign, evidence-based human-AI collaboration.

License & Guiding Principle

All material is released under Creative Commons Attribution 4.0 International (CC BY 4.0). Attribution is appreciated but never required.

The single non-negotiable across the entire collection:
Keep humans in the authorization chain.

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Public safety scorecard and binary architectural tests for high-gain AI features (e.g. adult mode). Pre-launch criteria + AI model responses

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