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.