memory-triage

Extract and evaluate durable facts from dialogue turns for long-term memory storage.

1|Updated May 6, 2026
One-click install
npx skills add https://github.com/New-dev0/mem0ai --skill memory-triage-new-dev0
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: memory-triage
Source: https://github.com/New-dev0/mem0ai/tree/main/openclaw/skills/memory-triage
Command: npx skills add https://github.com/New-dev0/mem0ai --skill memory-triage-new-dev0

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables systematic assessment of dialogue to identify and store durable, actionable facts, facilitating personalized memory management.

Core Features & Use Cases

  • Memory Evaluation: Analyzes conversation turns to determine their potential for long-term storage.
  • Fact Extraction & Organization: Extracts relevant information and categorizes it into meaningful, self-contained memories.
  • Use Case: Automate prioritization of key facts from chat logs, such as user preferences or system configurations, for future retrieval.

Quick Start

Provide conversation data to the skill and specify the context to evaluate which parts should be remembered.

Frequently Asked Questions about memory-triage

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

FAQPage Schema
How do I extract durable facts from conversation logs for long-term memory storage?

To extract durable facts from conversation logs for long-term memory storage, you can provide dialogue data to evaluate conversation turns, categorizing insights based on value, novelty, and safety while filtering out transient material.

What is the best way to filter out sensitive information when extracting facts from dialogue?

The best way to filter sensitive information when extracting facts from dialogue is through systematic evaluation criteria that assess conversation turns for safety, removing transient or sensitive material before preparing data for future retrieval.

How do I automate prioritizing user preferences from chat logs for future retrieval?

You can automate prioritization of user preferences from chat logs by evaluating dialogue turns to extract relevant information and categorize it into meaningful, self-contained memories for future retrieval.

Does memory triage work with mem0 for personalization and dialogue analysis?

Memory triage works with mem0 for personalization and dialogue analysis by structuring the evaluation of conversations to identify essential, durable facts suitable for long-term storage.

Can I use this skill to categorize system configurations from chat histories?

Yes, you can use this skill to categorize system configurations from chat histories by analyzing dialogue turns to extract actionable facts and organizing them into self-contained memories.

What are the limitations of using automated fact extraction for long-term memory?

Limitations of automated fact extraction for long-term memory include the need to provide specific context for evaluation and the system's focus on filtering out transient or sensitive material, which may require careful configuration.