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Contributing Guidelines

Thank you for your interest in contributing. These repositories focus on AI governance, release readiness, responsible AI deployment, evaluation, and operational tooling.

What We Welcome

  • New use cases with realistic constraints
  • Documentation improvements that make guidance clearer or more actionable
  • Framework extensions such as new checklists, mappings, or governance patterns
  • Worked examples with realistic inputs and outputs
  • Tooling improvements for validators, CLIs, templates, and demo apps
  • Translations that preserve technical accuracy

How to Contribute

1. Open an issue first

Before opening a pull request, open an issue describing:

  • what you want to add or change
  • why it would help practitioners
  • any relevant industry, regulatory, or implementation context

This avoids duplicated effort and helps keep each repository within scope.

2. Fork and branch

git clone https://github.com/simaba/<repo-name>
git checkout -b feature/your-descriptive-branch-name

3. Make the change practical

Please prefer:

  • concrete examples over abstract claims
  • reusable templates over one-off prose
  • truthful maturity labels over aspirational wording
  • clear boundaries between what is shipped, planned, and illustrative

4. Open a pull request

Include:

  • a concise summary of the change
  • the reason for the change
  • any user-visible behavior change
  • linked issue number when relevant

Contribution priorities by repository

Repository Strongest contribution types
governance-playbook operating-model patterns, governance artifacts, worked examples
release-governance release-stage criteria, gate definitions, lifecycle examples
release-checklist validator rules, example configs, tests, reporting improvements
accountability-patterns accountability patterns, redress flows, oversight examples
ai-prism high-quality curated resources, broken-link fixes, better categorization
nist-rmf-guide implementation examples, mappings, gap-closure guidance
regulated-ai template improvements, starter workflows, CI validation examples
multi-agent-governance trust models, failure handling, governance controls
agent-orchestration orchestration patterns, runnable examples, control-flow tradeoffs
agent-eval evaluation scenarios, rubric design, measurable pass/fail criteria
agent-simulator new scenarios, simulator logic, evaluation extensions
lean-ai-ops app quality, analytics rigor, examples, export quality, tests

Quality bar

A contribution is much more likely to be accepted if it:

  • matches the repository's actual scope
  • improves clarity or practical usefulness
  • includes at least one realistic example when adding a new concept
  • avoids placeholder content presented as shipped capability
  • keeps naming and cross-repo references consistent

Code of Conduct

By contributing, you agree to abide by the Code of Conduct.

Questions

Use GitHub Discussions for broader questions about framework usage, repo direction, or contribution fit.