What problem does it solve?
This Skill fixes gradual decay in a timeline-based knowledge base where newer entries contradict older conclusions, identity profiles become outdated, cross-references go missing, and frontmatter formatting drifts over time. It is designed for periodic whole-library maintenance rather than one-off note review.
Core Features & Use Cases
- Four-phase library maintenance: Audits journal entries and identity profiles, gathers signals with parallel analysis, applies guarded fixes, and writes a maintenance summary.
- Decision evolution tracking: Marks older entries when newer entries supersede prior decisions, without rewriting original journal content.
- Identity profile repair: Updates people and product profiles, resolves contradictions, fills missing relationship links, and enforces profile structure rules through a dependency on identity profiling guidance.
- Metadata and link hygiene: Detects malformed YAML frontmatter, empty summaries, missing tags, broken related-record links, and missing cross-entry references.
- Use case: Run it when your journal archive has grown messy over weeks or months and you want the AI to safely reconcile contradictions, refresh profiles, and restore navigability across the entire workspace.
Quick Start
Ask the AI to run the lint skill to clean up the whole journal library and summarize what it fixed versus what still needs human review.