Compute lifecycle scores for all insight and framework notes - detect which notes are crystallizing or becoming generative
Computes lifecycle scores (0.0 reflective -> 1.0 generative) for all insight and framework notes based on behavioral signals: citation frequency, generative ratio, cross-domain reach, and temporal acceleration.
| Source | Location | Read | Write | Description |
|---|---|---|---|---|
| Enrichments | resources/brain-graph/data/graph_enrichments.json | ✓ | ✓ | Updated lifecycle scores |
| LBS Graph | resources/local-brain-search/data/brain_graph.pkl | ✓ | NetworkX graph | |
| Brain files | Brain/**/*.md | ✓ | File mtimes for temporal signals |
cd resources/brain-graph
../local-brain-search/venv/bin/python cli.py lifecycle
For JSON output:
../local-brain-search/venv/bin/python cli.py lifecycle --json
Focus on notes that crossed phase boundaries:
For promotable notes, suggest:
| Score Range | Phase | Meaning |
|---|---|---|
| 0.0 - 0.3 | Reflective | Tracks sources, sources win on conflict |
| 0.3 - 0.6 | Crystallizing | Generating own connections, authority contested |
| 0.6 - 1.0 | Generative | Drives downstream notes, this note wins on conflict |