learned

Name: learned
Availability: InStock
Author: andersonsrepo

Log explicit knowledge, decisions, and corrections to a persistent vault.

Updated Aug 27, 2026

Search Tags:#debugging #decision making #documentation #logging #learning #knowledge capture

Ready to run

npx skills add https://github.com/AndersonsRepo/AI-Harness --skill learned

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: learned
Source: https://github.com/AndersonsRepo/AI-Harness/tree/main/.claude/skills/learned
Command: npx skills add https://github.com/AndersonsRepo/AI-Harness --skill learned

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you systematically log important learnings, decisions, and discoveries made during conversations, ensuring valuable knowledge is not lost and can be easily retrieved later.

Core Features & Use Cases

Explicit Knowledge Capture: Log specific insights, debugging findings, architectural decisions, or recurring patterns.
Contextual Metadata: Automatically associates learnings with projects, areas, and severity based on conversation context.
Duplicate Detection: Checks for similar existing entries and updates them, preventing redundant logs.
Use Case: After debugging a complex issue, you can use this skill to log the root cause, the fix, and why it matters, creating a permanent record for future reference.

Quick Start

Log that SQLite WAL mode is fast enough for our needs.

Frequently Asked Questions about learned

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

FAQPage Schema

How do I capture and log debugging insights for future reference?▼

You can capture debugging insights by logging explicit knowledge, discoveries, and corrections to a persistent vault. This ensures valuable findings like root causes and fixes are systematically recorded and not lost, creating a permanent record for future retrieval.

What is the best way to persist architectural decisions for AI agents?▼

Persisting architectural decisions for AI agents involves logging them to a persistent knowledge vault. This facilitates knowledge management by automatically capturing contextual metadata like project, area, and priority alongside the explicit decision.

How do I prevent duplicate knowledge logs when capturing recurring patterns?▼

Preventing duplicate knowledge logs relies on semantic search and recurrence tracking to check for similar existing entries. When similarities are detected, the system updates the existing record rather than creating a redundant log, ensuring deduplication.

Can I associate project context and severity metadata with my learning logs?▼

Yes, you can associate project context and severity metadata with learning logs. The system automatically associates explicit knowledge captures with contextual metadata like project, area, and priority based on the current conversation context.

Why does my knowledge capture system lose context during debugging sessions?▼

Knowledge capture systems lose context when insights are not persisted. Logging explicit knowledge to a persistent vault with semantic search ensures discoveries and corrections are retained with their contextual metadata rather than being lost.