How does AutoMem improve my Claude Code workflow?

It eliminates context drift by allowing Claude to remember your project's history, decisions, and preferences across different terminal sessions, meaning you don't have to repeat yourself.

Is it safe to store my code in AutoMem?

AutoMem is designed to store patterns, summaries, and decisions rather than raw source code. You should never store secrets, API keys, or sensitive credentials in the memory database.

What specifically does the 3-Phase Memory Pattern do?

It follows a structured workflow: first, 'Recall' relevant past context at the start of a task; second, 'Store' new insights as they happen; and third, 'Summarize' the entire session at the end.

Can I use AutoMem to link related technical decisions?

Yes, the skill includes tools to associate memories, allowing you to create relationships like linking a specific bug fix to the architectural decision that caused the issue.

AutoMem: Persistent Memory Management

Name: AutoMem: Persistent Memory Management
Author: verygoodplugins

byverygoodplugins

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Productivity & Workflow

Maintains deep project context, architectural decisions, and user preferences across Claude Code sessions using a structured 3-phase memory pattern.

About

AutoMem provides Claude Code with a long-term, persistent memory system that eliminates the need to re-explain project context in every new session. By implementing a 'Recall, Store, Summarize' pattern, this skill allows Claude to track architectural decisions, learn specific coding styles, and remember the root causes of past bugs. It builds an intelligent knowledge graph of your project, ensuring that every session benefits from previous insights and evolves alongside your codebase without manual context re-provisioning.

Key Features

Intelligent context-aware recall based on open files, error signatures, and project tags.
Importance-based storage for prioritizing critical architectural decisions over minor notes.
Automated session summarization to capture high-level progress and significant refactors.
Three-phase memory pattern (Recall, Store, Summarize) for structured session continuity.
1 GitHub stars
Knowledge graph creation using memory associations like 'DERIVED_FROM' and 'LEADS_TO'.

Use Cases

Capturing and retrieving bug-fix patterns to solve recurring errors or similar edge cases faster.
Maintaining consistent coding styles and library preferences across a distributed team or project.
Recalling complex architectural choices and technical debt context when starting a new feature.

About

Key Features

Intelligent context-aware recall based on open files, error signatures, and project tags.
Importance-based storage for prioritizing critical architectural decisions over minor notes.
Automated session summarization to capture high-level progress and significant refactors.
Three-phase memory pattern (Recall, Store, Summarize) for structured session continuity.
1 GitHub stars
Knowledge graph creation using memory associations like 'DERIVED_FROM' and 'LEADS_TO'.

Use Cases

Capturing and retrieving bug-fix patterns to solve recurring errors or similar edge cases faster.
Maintaining consistent coding styles and library preferences across a distributed team or project.
Recalling complex architectural choices and technical debt context when starting a new feature.