Where are the memory files stored?

All memory data is stored locally within your project's .claude/memory/ directory in human-readable Markdown and JSON formats.

Is the memory shared with other team members?

The skill distinguishes between local logs and shared knowledge. If you commit the decisions and patterns files to Git, your entire team can benefit from the stored project context.

Does this skill record every keystroke?

No, it focuses on high-level summaries, task completion, and significant architectural decisions rather than raw input logs to maintain a clean and relevant context.

What happens if I stop a session and come back later?

With Session Memory active, you can simply ask 'Where did we leave off?' and Claude will use the stored session logs to provide a summary of your progress and pending tasks.

Session Memory

Name: Session Memory
Author: Chachamaru127

byChachamaru127

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Productivity & Workflow

Persists project context and development history across Claude Code sessions to ensure seamless continuity.

About

Session Memory is a core capability designed to bridge the context gap between separate AI coding sessions. By maintaining a structured memory directory, it tracks completed tasks, architectural decisions, and recurring coding patterns. This allows Claude to instantly recall past progress, resume interrupted workflows, and adhere to project-specific conventions without requiring the user to re-explain the project state or history every time a new session begins.

Key Features

Automatic logging of session tasks and file changes
Persistent storage for architectural decisions and technical debt
Project-specific coding pattern recognition and retrieval
Metadata-driven project context summaries
Separation of shared team knowledge and local session history
4 GitHub stars

Use Cases

Maintaining a single source of truth for project-specific design patterns
Resuming complex feature implementation after a session restart
Onboarding or context-switching between multiple development projects

About

Key Features

Automatic logging of session tasks and file changes
Persistent storage for architectural decisions and technical debt
Project-specific coding pattern recognition and retrieval
Metadata-driven project context summaries
Separation of shared team knowledge and local session history
4 GitHub stars

Use Cases

Maintaining a single source of truth for project-specific design patterns
Resuming complex feature implementation after a session restart
Onboarding or context-switching between multiple development projects