mem-search

Name: mem-search
Availability: InStock
Author: enigmaicon-eng

Search persistent memory entries and fetch full records for selected IDs.

1|Updated May 16, 2026

Search Tags:#knowledge management #token efficiency #memory search #cross-session context #timeline retrieval #search and filter #retrieval workflows

Ready to run

npx skills add https://github.com/enigmaicon-eng/AI-Enterprise-OS --skill mem-search-enigmaicon-eng

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mem-search
Source: https://github.com/enigmaicon-eng/AI-Enterprise-OS/tree/main/examples/claude-mem/plugin/skills/mem-search
Command: npx skills add https://github.com/enigmaicon-eng/AI-Enterprise-OS --skill mem-search-enigmaicon-eng

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the problem of not knowing whether a task, decision, or fix was already handled in earlier sessions, forcing you to recreate context and waste time.

Core Features & Use Cases

Cross-session memory search: Locate relevant prior items by querying a persistent memory index instead of relying on the current chat history.
Timeline-centered retrieval: Expand around an anchor result to understand surrounding context before pulling full records.
Filter-then-fetch workflow: Reduce tokens and noise by searching first, using a timeline to refine relevance, and fetching full details only for selected IDs.

Quick Start

Use the mem-search skill to search the memory database for prior work on authentication, then expand around the best matching result to decide which items to fetch in full.

Frequently Asked Questions about mem-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I search cross-session memory to find out if we solved a problem before?▼

Cross-session memory search queries a persistent memory index using a three-layer workflow: search for candidate IDs, anchor on a timeline, and fetch full records only for selected filtered IDs to retrieve prior work efficiently.

What is the best way to reduce token usage when retrieving past session context?▼

To reduce token usage during memory search, apply a strict filter-then-fetch workflow that retrieves candidate IDs first, expands context via a timeline, and fetches complete records only for the final selected IDs.

How does timeline retrieval work for understanding past observations and prompts?▼

Timeline retrieval expands around an anchor result to map surrounding context, allowing you to review chronological observations and prompts before committing tokens to fetch full session records.

Can I use cross-session memory search without relying on current chat history?▼

Yes, cross-session memory search queries a persistent memory index directly, functioning independently of current chat history to locate relevant prior items across past sessions, observations, and prompts.

What are the limitations of the filter-then-fetch retrieval workflow?▼

The filter-then-fetch workflow requires strict adherence to its three-layer process; skipping the timeline anchoring phase or fetching full records before filtering candidate IDs will increase token consumption and reduce retrieval efficiency.