compute-lifecycle

Productivity

Compute lifecycle scores for all insight and framework notes - detect which notes are crystallizing or becoming generative

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Compute Lifecycle Scores

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.

State Dependencies

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

Process

Step 1: Run lifecycle computation

cd $PROJECT_ROOT/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

Step 2: Present transitions

Focus on notes that crossed phase boundaries:

Reflective -> Crystallizing: Note is starting to generate its own connections
Crystallizing -> Generative: Note has become a driver of new insights

For promotable notes, suggest:

"Consider promoting to framework status"
"This note drives connections across N domains"

Lifecycle Phases

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