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explain-self-learning

Answer questions about Catalyst's self-learning memory system using stored architecture memories.

10|1|Updated Jul 7, 2026
Search Tags:#architecture#context retrieval#self-learning#explain#catalyst#memory system
One-click install
npx skills add https://github.com/catalystctl/catcode --skill explain-self-learning-catalystctl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: explain-self-learning
Source: https://github.com/catalystctl/catcode/tree/main/.catalyst-code/skills/explain-self-learning
Command: npx skills add https://github.com/catalystctl/catcode --skill explain-self-learning-catalystctl

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? When users repeatedly ask what is valuable about Catalyst's self-learning and memory system, this Skill answers from stored architecture memories instead of re-deriving the explanation from the codebase each time. ## Core Features & Use Cases - Memory-grounded answers: Pulls specific architecture memories (self-learning-system-architecture, learning-layer-turn-loop-wiring, codebase-intelligence-phase1-2) before responding. - Consistent answer shape: Delivers a one-line thesis, benefit bullets, where benefits appear in practice, and an honest caveat about thinly populated stores like failure_atlas. - Use Case: A user returning to a session asks "What's so good about the memory system?" and receives a consistent, fact-based pitch without the agent re-walking core/src. ## Quick Start Ask the agent what is good about Catalyst's self-learning memory system and where the benefits show up.

Frequently Asked Questions about explain-self-learning

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

FAQPage Schema
How do I explain the benefits of a self-learning memory system?▼

Pull the stored architecture memories first, then answer with a one-line thesis, short benefit bullets, and where users feel the benefits in practice. Avoid re-deriving the explanation from source code each time.

What is good about Catalyst's memory system?▼

Durable facts, project structure, and past outcomes inject into the live turn loop, so later sessions start smarter without re-teaching. Benefits include scoped memory with hygiene, auto-reflect, per-turn context packs, and local offline operation.

Where do memory system benefits show up in practice?▼

Benefits appear when returning to a repository, using cross-project global memories, handling recurring task shapes, navigating large unfamiliar codebases, running subagents with role packs, and keeping preference stickiness across sessions.

Does the memory system mean the model remembers chat history?▼

No, the win is not the model remembering chat. The value comes from durable stored facts and retrieval injecting context into each turn, using lexical or hybrid local retrieval rather than hosted sync.

What are the limitations of the self-learning memory system?▼

Some stores like failure_atlas and rejected_approaches are read-wired but thinly populated in production. Retrieval is lexical or hybrid and local, with no embedding-based hosted sync.

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