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Propagate staleness from a changed note through the Brain Dependency Graph - shows which downstream notes need review
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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
Compute lifecycle scores for all insight and framework notes - detect which notes are crystallizing or becoming generative
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.
| name | propagate-change |
| description | Propagate staleness from a changed note through the Brain Dependency Graph - shows which downstream notes need review |
| argument-hint | <note name> |
| allowed-tools | ["Bash","Read"] |
| user-invocable | true |
| automation | gated |
When a note is substantially edited, this skill computes which downstream notes may be stale and need review. Uses the Brain Dependency Graph's staleness propagation engine with edge-type decay, distance decay, and hub dampening.
| Source | Location | Read | Write | Description |
|---|---|---|---|---|
| Enrichments | resources/brain-graph/data/graph_enrichments.json | ✓ | ✓ | Updated staleness scores |
| LBS Graph | resources/local-brain-search/data/brain_graph.pkl | ✓ | NetworkX graph |
cd $PROJECT_ROOT/resources/brain-graph
../local-brain-search/venv/bin/python cli.py propagate "<NOTE_NAME>"
Optional: specify change magnitude (0.0-1.0) for partial changes:
../local-brain-search/venv/bin/python cli.py propagate "<NOTE_NAME>" --magnitude 0.5
Show the user which notes are flagged for review, sorted by staleness score.
For each affected note, explain:
If the user wants details on any affected note:
../local-brain-search/venv/bin/python cli.py inspect "<NOTE_NAME>"