memory-management

Name: memory-management
Availability: InStock
Author: hiddink-ai

Store and retrieve session context using vector-based memory.

1|Updated May 21, 2026

Search Tags:#context #retrieval #memory #persistence #session #chromadb

Ready to run

npx skills add https://github.com/hiddink-ai/hiddink-harness --skill memory-management-hiddink-ai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-management
Source: https://github.com/hiddink-ai/hiddink-harness/tree/main/templates/skills/memory-management
Command: npx skills add https://github.com/hiddink-ai/hiddink-harness --skill memory-management-hiddink-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the issue of context loss during long-running AI sessions by providing a structured mechanism to save and recall project-specific decisions, tasks, and session summaries.

Core Features & Use Cases

Context Persistence: Automatically saves session summaries, completed tasks, and key decisions to a vector database.
Semantic Recall: Enables agents to retrieve relevant historical context using natural language queries.
Use Case: When working on a complex multi-day refactoring project, use this skill to store the current state of the authentication flow and recall it instantly when resuming work the next day.

Quick Start

Use the memory-management skill to save the current session context and then recall the authentication flow decisions.

Frequently Asked Questions about memory-management

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

FAQPage Schema

How do I persist AI agent session context across compactions?▼

To persist AI agent session context across compactions, use vector-based storage operations to save session summaries, project-specific tasks, and key decisions. This ensures continuity in long-term development workflows by maintaining retrievable session history.

How does semantic search for session memory retrieval work?▼

Semantic search for session memory retrieval works by using vector-based storage to match natural language queries with stored session history. This allows agents to recall relevant historical context using semantic similarity rather than exact keyword matching.

How do I save and recall project decisions for a multi-day development workflow?▼

You can save and recall project decisions for multi-day workflows by storing completed tasks, key decisions, and session summaries in a vector database. Use semantic recall to retrieve relevant historical context instantly when resuming work.

Does memory management support ID-based lookup for document metadata?▼

Yes, memory management supports ID-based lookup for document metadata. It implements both semantic search and ID-based lookup patterns to manage document metadata and session history effectively within the vector database.

When should I use vector-based memory persistence for AI agents?▼

Use vector-based memory persistence for AI agents when working on complex, long-running sessions where context loss is a risk. It provides a structured mechanism to save and recall project-specific decisions, tasks, and session summaries to ensure continuity.