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Compute lifecycle scores for all insight and framework notes - detect which notes are crystallizing or becoming generative
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Based on SOC occupation classification
Autonomous AI crystallization - synthesizes converged thinking topics into ai-inferred notes in a dedicated folder. Never touches the human-curated permanent knowledge base and never changes a topic's status, so manual crystallization stays available to the user.
Analyze knowledge base structure and update the knowledge-base-analysis.md report
Discover non-obvious cross-domain connections through random sampling and pattern analysis
Run a full coherence sweep across the Brain Dependency Graph - computes staleness, lifecycle transitions, structural health, and generates a report
Create long-form articles from knowledge base insights. Use when writing articles, blog posts, Substack content, or synthesizing knowledge into publishable content. Includes tone of voice, structure templates, and knowledge base integration.
Generate explanatory diagrams and infographics that visually communicate concepts. Iterates autonomously until images are logically correct, text is clean, and the concept explanation is clear. Uses Nano Banana (Gemini 2.5 Flash Image).
| name | compute-lifecycle |
| description | Compute lifecycle scores for all insight and framework notes - detect which notes are crystallizing or becoming generative |
| allowed-tools | ["Bash","Read"] |
| user-invocable | true |
| automation | gated |
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 $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
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 |