semantica/docs/governance.md

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Governance	Project governance model: roles, decision process, release cadence, and code review guidelines.	scale-balanced

Semantica is maintained by Hawksight AI with community contributions under an open governance model.

Roles

• Maintainers — Hawksight AI team: review and merge PRs, manage releases and code quality, set project direction and community standards.
• Contributors — Submit code, documentation, and bug reports. Help with issues and reviews. Recognized in CONTRIBUTORS.md.
• Community Members — Use Semantica, provide feedback, share use cases, and participate in GitHub Discussions and Discord.

Decision Process

Code Changes

Open a GitHub Issue describing the change. Community discussion on approach and scope. Submit a pull request with the change. At least one maintainer reviews the PR. Merged after CI passes and maintainer approval.

Major Decisions

• RFC posted in GitHub Issues
• Minimum 1-week community discussion period
• Maintainers decide based on community feedback and technical feasibility

Releases

Semantica follows Semantic Versioning (MAJOR.MINOR.PATCH):

Level	Trigger	Cadence
MAJOR	Breaking changes	Quarterly or as needed
MINOR	New features (backward compatible)	Monthly or when ready
PATCH	Bug fixes (backward compatible)	As bugs are fixed

Code Review

Review criteria: functionality, code quality, tests, documentation, performance, security.

Timeline: initial review within 48 hours; follow-up within 7 days.

Guidelines for reviewers: be constructive, explain reasoning, suggest alternatives.

Guidelines for contributors: address comments promptly, ask questions when unclear, be open to feedback.

Communication

• GitHub Issues: bug reports, feature requests, questions
• GitHub PRs: code contributions
• GitHub Discussions: community conversation
• Security Advisories: report security issues privately

Project Goals

• Usability — Easy to use and understand: sensible defaults, clear documentation, minimal ceremony.
• Reliability — Production-ready quality: tested across Python versions, platforms, and real-world workloads.
• Performance — Efficient and scalable: from single-machine notebooks to enterprise graph databases.
• Extensibility — Easy to extend with plugins and custom modules via the PluginRegistry pattern.
• Community — Welcoming and inclusive: all backgrounds and experience levels contribute and are recognized.

License

MIT License: see LICENSE and the License page.

See Also

• Contributing — How to submit changes.
• Community — Community guidelines and channels.