memory

Store and retrieve long-term memories with semantic search and category tagging.

14|6|Updated Feb 13, 2026

Search Tags:#memory #preferences #facts #semantic-search #persistent-memory #summaries #conversation-context

Ready to run

npx skills add https://github.com/jholhewres/devclaw --skill memory-jholhewres

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/jholhewres/devclaw/tree/main/pkg/devclaw/copilot/builtin/skills/memory
Command: npx skills add https://github.com/jholhewres/devclaw --skill memory-jholhewres

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill captures and retrieves non-sensitive long-term memories so conversational agents can maintain context across sessions, avoid repeated questions, and personalize responses.

Core Features & Use Cases

Persistent Storage: Save facts, preferences, events, and conversation summaries for future retrieval.
Semantic Search and Retrieval: Find related memories using queries to enrich responses with relevant past context.
Listing and Audit: Browse stored entries to review, summarize, or export past interactions.
Use Case: Remember a user's editor and formatting preferences and automatically apply them when editing code across sessions.

Quick Start

Use memory to save the user's preference "Prefers dark mode" and then search for "theme preference" before responding to UI customization requests.

Frequently Asked Questions about memory

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

FAQPage Schema

How do I add persistent memory to a chat agent so it remembers user preferences across sessions?▼

You add persistent memory by storing facts, preferences, events, and conversation summaries for future retrieval. This allows conversational agents to maintain context across sessions, avoid repeated questions, and personalize responses using semantic search.

What is semantic search for conversation context and how does it work with stored facts?▼

Semantic search for conversation context finds related memories using queries to enrich responses with relevant past context. It uses semantic search indexing to retrieve stored facts, preferences, events, and summaries based on query meaning rather than exact keyword matches.

Can I browse and audit stored conversation summaries and user preferences?▼

Yes, you can browse stored entries to review, summarize, or export past interactions. The memory skill provides listing with limits and category tagging for facts, preferences, events, and summaries, enabling audit and export of past conversation context.

What's the best way to prevent storing secrets or credentials in persistent memory?▼

The best way to prevent storing secrets is using built-in safeguards that block sensitive data from being saved. The memory skill includes safeguards to prevent storing secrets or credentials while persisting non-sensitive facts, preferences, and conversation summaries.

How do I save a user's formatting preferences and apply them automatically in future sessions?▼

You save a user's formatting preferences by storing them as persistent memory, then search for relevant preferences before responding to future requests. For example, store "Prefers dark mode" and search for "theme preference" before handling UI customization requests.

When should I not use persistent memory for conversation context?▼

You should not use persistent memory when dealing with sensitive data, secrets, or credentials. The safeguards prevent storing secrets, so persistent memory is suited only for non-sensitive facts, preferences, events, and conversation summaries across sessions.