Thriving 
PY TRACE protocol for auditing decision-provenance in AI-assisted scientific workflows
https://doi.org/10.5281/zenodo.21711455 ↗ HEALTH ? 76 / Stable
Thriving PY // readme
Why TRACE?
In the age of AI, how do we know who proposed what in a scientific or coding dev workflow? Was the idea for that methodological decision made by AI or by a human? And when decisions are proposed by AI, are they being accepted, rejected, or iterated on?
What does the solution to this look like?
Every row above is a real event from session trace_20260730_32c108, captured as
the work happened. Note what a diff could not have told you: one decision is
still awaiting resolution because the AI is not permitted to resolve its own
proposal, and the last event retracts an earlier contribution that turned out
to be wrong. Regenerate it from any session with
python3 scripts/make_provenance_animation.py <session.json>.
One sentence from you, fully-scoped session from Claude:
- From inside
TRACE/, ask Claude to start a session and review the manuscript (which is inside sibling dir,TRACE-research/). - Past-session memory makes Claude pivot to that sibling repo before logging anything.
trace_start_sessionruns there, learnings auto-recall, and a five-item task plan emerges.