How does the Memory Keeper Save skill trigger?

The skill activates automatically whenever it detects the [MEMORY_KEEPER] hook in the tool output, typically after a set number of tool uses.

Can I customize the architectural or convention files?

Yes, you can manually update architecture.md and conventions.md using the provided memory-set commands to ensure the AI follows your specific standards.

Does this skill require manual saving?

While it handles auto-saves via the trigger, you can also use the manual steps to append summaries or update hierarchical memory files at any time.

Where is the project memory stored?

All memory files, including project overviews and session logs, are stored locally in your project's .claude/memory/ directory.

Memory Keeper Save

Name: Memory Keeper Save
Author: ZipperBagCoffee

byZipperBagCoffee

Productividad y Flujo de Trabajo

Automates session memory persistence for Claude Code by maintaining a structured log of project context, architecture, and coding conventions.

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Memory Keeper Save is a specialized skill designed to solve the problem of context loss in long-running AI development sessions. It creates a hierarchical memory structure within your project, organizing information into rolling session logs, stable architectural decisions, and coding standards. By automatically triggering during key session moments, it ensures that Claude remains aligned with your project's evolving scope and technical requirements without requiring manual context re-injection.

Características Principales

Automated session transcript compression and archival
Built-in CLI utilities for retrieving and updating persistent project memory
0 GitHub stars
Trigger-based activation via the [MEMORY_KEEPER] hook
Hierarchical storage for project scope, architecture, and coding standards
Automatic session logging via rolling memory.md files

Casos de Uso

Enforcing consistent architectural patterns and coding conventions automatically
Creating a permanent, searchable record of project decisions and session history
Maintaining continuity across multiple AI development sessions for complex projects

Acerca de

Características Principales

Automated session transcript compression and archival
Built-in CLI utilities for retrieving and updating persistent project memory
0 GitHub stars
Trigger-based activation via the [MEMORY_KEEPER] hook
Hierarchical storage for project scope, architecture, and coding standards
Automatic session logging via rolling memory.md files

Casos de Uso

Enforcing consistent architectural patterns and coding conventions automatically
Creating a permanent, searchable record of project decisions and session history
Maintaining continuity across multiple AI development sessions for complex projects