Why is the timeline tool important for memory retrieval?

The timeline tool provides the chronological context surrounding a specific search result, helping Claude understand 'what was happening' before and after a change was made.

How does mem-search help save tokens during coding?

It uses a tiered search strategy that provides high-level summaries first, allowing Claude to only fetch full, token-heavy details for the specific items identified as relevant.

What is the difference between a session and an observation?

A session represents a single interaction period with Claude, while an observation is a compressed AI-generated summary of a specific change, discovery, or decision made within that session.

Can I search for work done in a specific project?

Yes, the search tool includes a required project parameter to ensure that retrieved context is strictly relevant to the current workspace you are managing.

Persistent Memory Search

Name: Persistent Memory Search
Author: thedotmack

bythedotmack

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8,429

Productivity & Workflow

Searches and retrieves contextual data from past Claude sessions to maintain continuity across complex coding projects.

About

The mem-search skill provides Claude with a persistent cross-session memory database, enabling it to recall past bug fixes, architectural decisions, and feature implementations from previous interactions. By utilizing a structured workflow of indexing, timeline analysis, and batch fetching, it allows Claude to efficiently inject relevant historical context into current conversations. This eliminates the need for users to manually re-explain past work, significantly improving developer productivity and ensuring project consistency over long durations.

Key Features

Chronological timeline view to understand the sequence of past events
Efficient batch fetching of detailed observations to optimize token usage
8,429 GitHub stars
Automatic context injection for recurring implementation patterns
Cross-session full-text search across all historical coding activities
Granular filtering by project, date ranges, and observation types

Use Cases

Resuming long-term projects by retrieving relevant code snippets from earlier sessions
Understanding the historical context behind past architectural decisions
Recalling specific logic or bug fixes implemented in previous weeks

About

Key Features

Chronological timeline view to understand the sequence of past events
Efficient batch fetching of detailed observations to optimize token usage
8,429 GitHub stars
Automatic context injection for recurring implementation patterns
Cross-session full-text search across all historical coding activities
Granular filtering by project, date ranges, and observation types

Use Cases

Resuming long-term projects by retrieving relevant code snippets from earlier sessions
Understanding the historical context behind past architectural decisions
Recalling specific logic or bug fixes implemented in previous weeks