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continual-learning

Store and manage AI agent learnings across global and local databases.

Updated Mar 7, 2026
Search Tags:#continual learning#agent memory#knowledge retention#learning loop#ai persistence#tool patterns
One-click install
npx skills add https://github.com/involvex/llms-remote --skill continual-learning-involvex
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: continual-learning
Source: https://github.com/involvex/llms-remote/tree/main/.agents/skills/continual-learning
Command: npx skills add https://github.com/involvex/llms-remote --skill continual-learning-involvex

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the issue of AI agents forgetting information between sessions, enabling them to retain and apply learned knowledge over time for improved performance and consistency.

Core Features & Use Cases

  • Persistent Memory: Stores learnings in both global and local databases for cross-project and project-specific knowledge retention.
  • Automated Learning Capture: Hooks automatically detect and store patterns from tool usage and outcomes.
  • Manual Memory Input: Allows agents to directly write learnings via SQL or store human-readable markdown files.
  • Compaction Mechanism: Prunes old or low-value learnings to manage memory size while preserving important information.
  • Use Case: An agent that previously struggled with a specific API integration can now remember the correct parameters and error-handling strategies from past sessions, leading to faster and more successful task completion.

Quick Start

Copy the continual-learning hooks into your .github/hooks directory to begin auto-initializing memory.

Frequently Asked Questions about continual-learning

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

FAQPage Schema
How do I make an AI coding agent remember context and learned patterns across sessions?▼

Persistent agent memory captures tool patterns and project conventions in global and local databases, enabling AI coding agents to apply past learnings across sessions for consistent performance.

How does automated learning capture work for AI agent memory?▼

Automated learning capture uses hooks to detect and store patterns from tool usage and outcomes automatically, building agent knowledge without requiring manual intervention for every learning entry.

Can I manually input project conventions into an AI agent's persistent memory?▼

Agents can directly write learnings to persistent memory via SQL commands or by storing human-readable markdown files, allowing manual input of project conventions alongside automated hook capture.

What is the best way to manage memory growth and prevent unbounded storage in AI agents?▼

Compaction and decay mechanisms prune old or low-value learnings to manage memory size, preserving important information while controlling persistent database size for AI agents.

Do I need specific dependencies to set up cross-project knowledge retention for AI agents?▼

No specific dependencies are required; you set up cross-project knowledge retention by copying continual-learning hooks into your .github/hooks directory to auto-initialize global and local memory databases.

When should I use global versus local storage for AI agent learning retention?▼

Global storage retains cross-project knowledge, while local storage manages project-specific conventions; use both tiers to separate broad tool patterns from localized coding preferences in agent persistent memory.

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