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shane9coy
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remember

Store conversational memories and user preferences into persistent files.

1|Updated Feb 27, 2026
Search Tags:#context#knowledge base#session management#memory#persistence
One-click install
npx skills add https://github.com/shane9coy/katana-agent --skill remember-shane9coy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: remember
Source: https://github.com/shane9coy/katana-agent/tree/main/agent/skills/memory/remember
Command: npx skills add https://github.com/shane9coy/katana-agent --skill remember-shane9coy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that important information, decisions, and user preferences from your AI sessions are not lost, providing a persistent memory for your agent.

Core Features & Use Cases

  • Contextual Memory Storage: Automatically categorizes and saves information to the appropriate memory file (soul.md, user.md, or work.md).
  • Summarization: Condenses conversation logs, stripping unnecessary details to create concise memory entries.
  • Project Context Detection: Identifies the relevant project for work-related memories.
  • Use Case: After a complex coding session, use this Skill to save the key decisions made, problems solved, and any user preferences expressed, ensuring the agent remembers them for future interactions.

Quick Start

Use the remember skill to save the current conversation context to work.md.

Frequently Asked Questions about remember

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

FAQPage Schema
How do I save conversational context and user preferences for future AI sessions?▼

You can save conversational context by storing session memories and user preferences into persistent files. This categorizes data into learned behaviors, user context, or work logs to prevent information loss across sessions.

Does session memory persistence require file I/O operations to store work logs?▼

Yes, session memory persistence requires file I/O operations to write to files like work.md. It also uses natural language processing to summarize interactions and categorize them into appropriate memory files.

How do I automatically categorize and summarize interaction logs into memory files?▼

To categorize and summarize interaction logs, the memory storage mechanism condenses conversation logs by stripping unnecessary details. It then routes the concise entries into files like soul.md, user.md, or work.md.

Can I detect project context when saving session memories for a specific coding task?▼

Yes, project context detection identifies the relevant project for work-related memories. This ensures that session memories and user preferences are saved to the correct work logs during coding sessions.

What is the best way to maintain persistent memory for an AI agent without losing decisions?▼

The best way to maintain persistent memory is to save key decisions and problems solved into categorized files. This supports both manual and automatic saving modes to capture learned behaviors effectively.

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