reviewing-memory

What problem does it solve?

Prevents systemic drift by turning vague or stale agent memories into a verified, source-backed, correctly scoped set of guidance before releases, migrations, or complex refactors.

Core Features & Use Cases

• Staleness-first review workflow: Surfaces the highest-impact memories using stale ranking and tiering (scoped/attested/high commit count) plus contextual filtering for active subsystems.
• Strict checklist for quality: Validates provenance sources, freshness/verification recency, scope correctness against your queries, visibility discipline, actionability, and duplication/conflicts.
• Thread hygiene enforcement: Ensures context-dependent threads do not linger by moving anything not recently verified to archived/superseded or converting it into a durable, properly scoped record.
• Outcome-based results: Produces verified, corrected, superseded, archived/obsolete, or promoted-from-draft outcomes so future agents default to accurate guidance.
• Link integrity verification: Checks [[...]] references via a dry-run resolution to avoid persisting broken or renamed targets.

Quick Start

Use the reviewing-memory skill when stability matters by running a staleness-ranked review of memories and then correcting, superseding, or archiving each surfaced item until the top results match your common path/command/tag queries and are recently verified.