money-learn

Store atomic learnings in a searchable learnings.jsonl file.

799|127|Updated Mar 25, 2026
One-click install
npx skills add https://github.com/iamzifei/show-me-the-money --skill money-learn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: money-learn
Source: https://github.com/iamzifei/show-me-the-money/tree/main/skills/money-learn
Command: npx skills add https://github.com/iamzifei/show-me-the-money --skill money-learn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manage project learnings — small, atomic, validated patterns that the agent should remember across all skills and sessions. Different from /money-save (which captures full session state); learnings are individual insights that get auto-loaded into every other money-* skill's context. Use when the user has just discovered something worth remembering — a customer pattern, a pricing insight, a channel that works, a failure mode. Triggered by: 'remember this', 'log a learning', 'this is a pattern', 'show learnings', 'what have we learned', '记住这个', '存入经验', '查看经验库'.

Core Features & Use Cases

  • Atomic learnings stored as individual, citable lines in a JSONL file (learnings.jsonl) for easy revision, supersession, and search.
  • Auto-loaded into other money-* skills before generation, providing context and evidence for improved decision-making.
  • Batch workflows to log, search, prune, and export learnings, enabling continuous improvement and knowledge retention across sessions.

Quick Start

Log a new learning from the current conversation to append to learnings.jsonl for future reference.

Frequently Asked Questions about money-learn

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

FAQPage Schema
How do I retain atomic learnings across AI agent sessions?▼

To retain atomic learnings across sessions, you append validated patterns as individual, citable lines into a durable learnings.jsonl file, ensuring insights are preserved and searchable for future workflows.

What's the best way to log project knowledge for AI agents to reuse?▼

Logging project knowledge for AI reuse involves capturing atomic insights in an append-only JSONL schema, which allows for interactive logging, pruning, and supersession while maintaining a searchable knowledge base.

How does auto-loading validated patterns improve AI workflow outputs?▼

Auto-loading validated patterns improves outputs by surfacing stored learnings with evidence before generation, providing necessary context and historical data to enhance decision-making across related workflows.

Can I search and export knowledge management patterns from a JSONL file?▼

Yes, you can search and export knowledge management patterns from a JSONL file using batch workflows that support pruning and exporting, enabling continuous improvement and knowledge retention across projects.

Does storing learnings as individual atomic patterns help prevent context loss?▼

Storing learnings as individual atomic patterns prevents context loss by enforcing an append-only schema that captures specific insights, distinguishing them from full session state captures for easy revision.

When should I not use an append-only schema for knowledge retention?▼

An append-only schema for knowledge retention should not be used when you need to mutate historical data directly, as it restricts modifications and relies on supersession to invalidate outdated patterns.