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Contributing
Mohammed Ammar edited this page Oct 6, 2026
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See Getting Started. Run make dataset once, then make test.
- Branch from
main:feature/<short-name>orfix/<short-name>. - Small, focused commits with a clear message. Open a pull request and get one review from a teammate who owns a different workstream.
-
make testpasses. - Output is still deterministic (the determinism test covers this).
- If thresholds or rules changed: bump
rubric_version, rerunmake evaluate, and note whether the change was tuned on dev or held-out data. - If the output schema changed: update
docs/schema.mdand the Result Schema page, and tell the dashboard owner. - No secrets, credentials or personal data committed.
- AI assistance used for the change is noted in the PR (the project discloses AI use).
- Define feature, normalization, window, threshold, direction and template in
rubric.py. - Add a detector in
compare.pyand thresholds inconfig.py. - Add a dataset flaw family if it can be injected, and a test.
- Document it on the Rubric page.
Follow Dataset: provenance and license for the text and the recording, transcript, format, speaker notes, flaw type and severity, spans, and transformation parameters. Mark labels as human-reviewed only if a person reviewed them.
Python, readable over clever. Keep functions deterministic. No hidden randomness; seed anything random.
Arachne · Multimodal AI Hackathon 2026, Track C · Parts of this project were built with assistance from Claude (Anthropic).