What is the primary purpose of the Memory Rotate skill?

It automatically summarizes and indexes old session memory files when they are rotated, keeping your workspace clean while preserving important historical data.

How does the skill know when to start summarizing?

The skill listens for a specific [MEMORY_KEEPER_ROTATE] trigger message, which is typically generated by a memory-management hook when a file reaches its limit.

Where are the generated summaries stored?

Summaries are saved as .summary.json files within the .claude/memory/ directory for easy programmatic access.

Which model does this skill use for summarization?

It utilizes the Claude 3 Haiku model to provide fast, efficient, and cost-effective summaries of your memory files.

What happens if a summarization task fails?

If a task fails, the skill logs the error and maintains the 'summaryGenerated: false' status in your index; a retry will automatically fire at the start of your next session.

Memory Rotate & Summarize

Name: Memory Rotate & Summarize
Author: ZipperBagCoffee

byZipperBagCoffee

生产力与工作流

Automates the summarization and archival of AI session memory files to maintain clean and efficient context windows.

关于

Memory Rotate is a specialized Claude Code skill designed to optimize the way AI agents handle long-term project context. When a memory file is rotated out of the active context, this skill automatically invokes a lightweight Haiku subagent to generate a concise summary of the archived session. By converting raw logs into structured JSON summaries and updating a centralized index, it ensures that critical project history remains searchable and retrievable without consuming unnecessary token space in future sessions.

主要功能

Centralized indexing for easy context retrieval
Lightweight Haiku-powered session summarization
Automated failure recovery and retry logic
Automatic trigger-based memory log processing
Structured JSON archival for machine-readable history
0 GitHub stars

使用场景

Archiving project milestones and technical decisions for future reference
Managing long-running development sessions without losing key historical context
Optimizing token usage by summarizing historical logs instead of loading full transcripts

关于

主要功能

Centralized indexing for easy context retrieval
Lightweight Haiku-powered session summarization
Automated failure recovery and retry logic
Automatic trigger-based memory log processing
Structured JSON archival for machine-readable history
0 GitHub stars

使用场景

Archiving project milestones and technical decisions for future reference
Managing long-running development sessions without losing key historical context
Optimizing token usage by summarizing historical logs instead of loading full transcripts