compute-lifecycle
Skill109 starsChanged 37 days agoMITView on GitHub
Compute lifecycle scores for all insight and framework notes - detect which notes are crystallizing or becoming generative
In this repository61 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- adviseSkill
- ai-crystallizeSkill
- analyze-kbSkill
- auto-discoverySkill
- benchmark-memorySkill
- brain-mergeSkill
- changelog-protocolSkill
- coherence-sweepSkill
- create-articleSkill
- create-explanatory-imageSkill
- decideSkill
- deep-researchSkill
- detect-tensionsSkill
- dialecticSkill
- domain-watchSkill
- elicitation-techniquesSkill
- epistemic-classificationSkill
- epub-chapter-extractorSkill
- extract-document-insightsSkill
- extract-insightsSkill
- find-connectionsSkill
- first-runSkill
- get-perspective-onSkill
- get-youtube-transcriptSkill
- git-commit-pushSkill
- graduate-insightsSkill
- incubation-loopSkill
- ingest-sourceSkill
- insight-capture-formatSkill
- insight-interviewSkill
- integrate-recent-notesSkill
- learn-new-thingsSkill
- manage-reference-dataSkill
- manage-thinking-topicsSkill
- manage-watching-domainsSkill
- multi-format-book-extractorSkill
- nano-banana-image-generatorSkill
- propagate-changeSkill
- quick-searchSkill
- reasoning-checksSkill
- recallSkill
- ref-auditSkill
- ref-bridgeSkill
- ref-ingestSkill
- ref-querySkill
- ref-reconcileSkill
- ref-refreshSkill
- refresh-indexSkill
- ref-supersedeSkill
- scheduled-runSkill
- scope-mountSkill
- search-vaultSkill
- self-diagnosticSkill
- start-hereSkill
- synthesize-insightsSkill
- talkSkill
- test-memory-systemSkill
- think-about-itSkill
- update-changelogSkill
- The first 60 shown.
The file
Files
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 lifecycleFor JSON output:
../local-brain-search/venv/bin/python cli.py lifecycle --jsonStep 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 |
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.