learning

Capture and classify work session learnings into structured memory entries.

Updated Apr 1, 2026
One-click install
npx skills add https://github.com/hpsgd/claude-marketplace --skill learning-hpsgd
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: learning
Source: https://github.com/hpsgd/claude-marketplace/tree/main/plugins/practices/thinking/skills/learning
Command: npx skills add https://github.com/hpsgd/claude-marketplace --skill learning-hpsgd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Capture and organize learnings from work sessions to build a usable memory of experiences, decisions, and outcomes.

Core Features & Use Cases

  • Classify learnings into SYSTEM, METHOD, DOMAIN, and FEEDBACK to provide context for future work.
  • Store structured learnings in a memory system and enable quick recall after project milestones or incidents.
  • Support recalling relevant learnings during planning, retrospective reviews, or after unexpected results.

Quick Start

Describe a noteworthy event and its learning in a structured memory entry.

Frequently Asked Questions about learning

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

FAQPage Schema
How do I capture and classify learnings from work sessions into a memory bank?

To capture learnings from work sessions, describe the noteworthy event and its outcome in a structured memory entry, then classify it explicitly using SYSTEM, METHOD, DOMAIN, or FEEDBACK categories to build a usable memory bank.

What is the best way to recall past project insights during a retrospective?

The best way to recall past project insights during a retrospective is to query a structured memory store using the predefined recall workflow, retrieving classified entries to review previous decisions, experiences, and unexpected outcomes.

When do I need to store structured learnings across different projects and teams?

You need to store structured learnings across projects and teams after completing work sessions, during retrospectives, or when an unexpected outcome occurs, preserving actionable insights for future planning and context.

How does classifying work memories by system, method, domain, and feedback help future tasks?

Classifying work memories by system, method, domain, and feedback provides explicit context for future tasks, enabling quick recall of relevant experiences and decisions to inform planning and prevent repeating past mistakes.

Can I use a lightweight memory store to preserve actionable insights without heavy setup?

Yes, you can use a lightweight memory store to preserve actionable insights, requiring only structured memory entries with explicit classification and a defined recall workflow rather than complex dependencies or heavy setup.

What are the limitations of using explicit classification for capturing work learnings?

A limitation of using explicit classification for capturing work learnings is that entries require strict categorization into SYSTEM, METHOD, DOMAIN, or FEEDBACK, meaning unstructured or ambiguous session outcomes may not be captured effectively.