What problem does it solve? Long-running AI agent sessions accumulate bloated context windows and cluttered memory stores, causing degraded retrieval quality, silent deletion failures, and uncontrolled memory growth. This Skill provides a unified entry point for managing agent memory health across sessions. ## Core Features & Use Cases - Context Offloading: Automatically saves tool outputs over 10KB or 50 lines to ~/.hermes/context_refs/ files and replaces them with compact Mermaid summaries, keeping the active context lightweight. - FSRS Memory Consolidation: Runs a scheduled cron job (daily at 3:00) plus in-session health scoring to grade memory entries as removable, low-retrievability, normal, or healthy. - Memory-Fact Store Bridging: Promotes cross-session facts with high trust scores to fact_store, deduplicates entries above 80% similarity, and enforces tiered cleanup rules at 85%, 90%, and 95% capacity. - Use Case: After a complex task with many tool calls, run the memory management checklist to offload long outputs, remove stale entries, and verify capacity dropped below the threshold. ## Quick Start Ask the agent to check current memory usage and run the memory optimization workflow, offloading any long outputs and cleaning entries according to the capacity thresholds.