memory-agent

Retrieve past experiences via memory_search_with_experience and store learnings with ingester_ingest.

1|Updated Jan 14, 2026
One-click install
npx skills add https://github.com/Rwb3n/haios --skill memory-agent
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: memory-agent
Source: https://github.com/Rwb3n/haios/tree/main/.claude/skills/memory-agent
Command: npx skills add https://github.com/Rwb3n/haios --skill memory-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps AI agents retrieve relevant past experiences to improve reasoning and accelerate learning across sessions.

Core Features & Use Cases

  • Retrieve relevant past experiences before reasoning to inform decisions.
  • Extract new learnings after task completion and store them for future sessions.
  • Close the ReasoningBank loop by injecting strategies into current reasoning.

Quick Start

Before tackling a complex task, call memory_search_with_experience(query="<describe task>", space_id="dev_copilot"). After task completion, extract learnings with ingester_ingest(content="<what was learned>", source_path="session:<date>:<brief-context>", content_type_hint="techne")

Frequently Asked Questions about memory-agent

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

FAQPage Schema
How do I retrieve past experiences to improve AI agent reasoning?

To retrieve past experiences for AI agent reasoning, call memory_search_with_experience with a task description and space_id before complex questions or architecture discussions to surface prior patterns and learnings.

What is the best way to store AI agent learnings for future sessions?

Storing AI agent learnings for future sessions requires extracting new insights after task completion via ingester_ingest, providing the learned content, a source path, and a content type hint.

How does experiential learning work for long-running AI projects?

Experiential learning for long-running AI projects works by injecting past strategies into current reasoning to close the ReasoningBank loop, applying retrieved patterns before tackling complex tasks.

Do I need to integrate with ReasoningBank to use context retrieval?

Integrating with the ReasoningBank workflow is required to close the context-retrieval loop, as the skill injects retrieved past experiences directly into current reasoning strategies.

Can I use ingestion to extract learnings from a specific session context?

You can use ingester_ingest to extract learnings from a specific session context by setting the source_path parameter to a formatted session identifier including the date and brief context.

When should I apply memory search before AI reasoning tasks?

Memory search should be applied before complex questions, architecture discussions, and long-running projects to surface prior patterns and learnings that sharpen AI reasoning and accelerate learning.