Can it understand relationships between different files or decisions?

Yes, AutoMem uses association types like LEADS_TO and DERIVED_FROM to build a knowledge graph that helps Claude understand the relational context of your work.

Will Claude be intrusive when using this memory skill?

The skill is designed for natural integration, encouraging Claude to weave recalled context into conversations seamlessly without robotic search announcements.

How does AutoMem improve Claude Code's performance?

AutoMem gives Claude a long-term memory by storing project context and decisions in a graph-vector database, ensuring it remembers your project's history across different sessions.

Does this skill store sensitive information like API keys?

No, the skill's best practices specifically advise against storing secrets or sensitive data; it is intended for logic, architectural patterns, and development insights.

What is the 3-Phase Memory Pattern?

It is a workflow consisting of recalling relevant context at session start, storing key insights during development, and summarizing progress at the end of a session.

AutoMem Memory Management

Name: AutoMem Memory Management
Author: verygoodplugins

byverygoodplugins

•

生産性とワークフロー

Maintains durable, relational memory across sessions to preserve project context, architectural decisions, and developer preferences.

概要

The AutoMem Memory Management skill equips Claude Code with a persistent, graph-vector memory service that bridges the gap between coding sessions. By implementing a structured 3-phase pattern—Recall, Store, and Summarize—it allows Claude to retain critical insights such as project architecture, past bug fixes, and specific coding styles. This eliminates the need for repetitive context-setting and enables the AI to make more informed decisions based on the evolution of your codebase and your personal development habits.

主な機能

Vectorized search for rapid retrieval of past bug fixes and insights
Multi-hop reasoning support through associated memory entities
Importance-based storage for high-signal architectural choices
30 GitHub stars
Relational knowledge graph for linking related decisions and patterns
Automated context recall at session start to regain project momentum

ユースケース

Recalling complex architectural decisions and their rationales during long-term project maintenance.
Searching for similar past error patterns and their verified solutions to accelerate debugging.
Preserving specific user style preferences and library choices across different repositories.

概要

主な機能

Vectorized search for rapid retrieval of past bug fixes and insights
Multi-hop reasoning support through associated memory entities
Importance-based storage for high-signal architectural choices
30 GitHub stars
Relational knowledge graph for linking related decisions and patterns
Automated context recall at session start to regain project momentum

ユースケース

Recalling complex architectural decisions and their rationales during long-term project maintenance.
Searching for similar past error patterns and their verified solutions to accelerate debugging.
Preserving specific user style preferences and library choices across different repositories.