Memory Flush

Promote recent Aeon logs into MEMORY.md, resolve contradictions, and decay stale details.

Updated Jun 3, 2026
One-click install
npx skills add https://github.com/swarm-ai-research/aeon --skill memory-flush-swarm-ai-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Memory Flush
Source: https://github.com/swarm-ai-research/aeon/tree/main/skills/memory-flush
Command: npx skills add https://github.com/swarm-ai-research/aeon --skill memory-flush-swarm-ai-research

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Promote important recent log entries into MEMORY.md, resolve contradictions, and decay stale detail.

Core Features & Use Cases

  • Scan recent logs for entries worth promoting to long-term memory.
  • Resolve contradictions by keeping newer, grounded facts and removing outdated ones.
  • Apply graded decay to older, less referenced memory to preserve canonical facts.
  • Maintain recall-readiness by linking memory to related topics with signals like keywords and timing.
  • Update memory state and logs to ensure a compact, actionable memory store.

Quick Start

Promote the most recent log entries to MEMORY.md, resolve contradictions, and decay stale details.

Frequently Asked Questions about Memory Flush

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate memory management and promote recent log entries into long-term memory?▼

Automated memory management scans recent logs and promotes important entries into MEMORY.md. It reconciles conflicts, applies graded decay to stale details, and writes canonical facts with recall signals to maintain a coherent, up-to-date memory state.

What is the best way to resolve contradictions in a memory store?▼

Resolving contradictions in a memory store involves scanning candidate entries and keeping newer, grounded facts while removing outdated ones. This rule-based reconciliation ensures memory coherence by preserving only accurate, verified information across logs.

How does graded decay work for stale details in log analysis?▼

Graded decay applies to older, less referenced memory logs to preserve canonical facts. By scanning Aeon log data and decaying stale details, the pipeline maintains recall-readiness and links memory to related topics using keywords and timing signals.

Can I maintain recall-readiness by linking memory to related topics with signals?▼

Yes, you can maintain recall-readiness by writing canonical facts into MEMORY.md with attached recall signals. These signals use keywords and timing metadata to link memory entries to related topics, ensuring a coherent, up-to-date memory state between runs.

Do I need any dependencies to run a rule-based pipeline for memory curation?▼

No dependencies are required to run a rule-based pipeline for memory curation. The process operates directly on Aeon log data across the memory store, memory/logs, and related topics to enforce scanning, conflict reconciliation, and decay without external tools.

Why does my memory store accumulate contradictions and stale details between runs?▼

A memory store accumulates contradictions and stale details between runs because logs continuously generate new entries without automatic curation. Without a pipeline to promote recent logs, reconcile conflicts, and decay outdated data, memory loses coherence and recall-readiness.