ingest

Extract atomic insights from diverse formats and route them to memory or vault notes.

11|1|Updated Mar 28, 2026
One-click install
npx skills add https://github.com/robinslange/learning-loop --skill ingest-robinslange
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ingest
Source: https://github.com/robinslange/learning-loop/tree/main/skills/ingest
Command: npx skills add https://github.com/robinslange/learning-loop --skill ingest-robinslange

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ingest pulls external context into the second brain by extracting atomic insights from diverse formats (PDFs, images, code, conversations, docs, or raw text) and routing them to auto-memory and vault notes, enabling faster, more accurate knowledge building.

Core Features & Use Cases

  • Multi-source ingestion: Linear tickets, repositories, or pasted content.
  • Insight extraction and preview: previews before memory or vault write and validation
  • Routing and organization: memory vs vault notes, with optional refinement workflow.

Quick Start

Run /learning-loop:ingest context to provide content, review the preview, and confirm routing to memory or vault notes.

Frequently Asked Questions about ingest

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

FAQPage Schema
How do I extract insights from external sources into a second brain?

You can ingest external context by extracting atomic insights from formats like Linear tickets, repositories, or pasted content. The workflow previews extracted insights, validates them, and routes them to memory or vault notes for knowledge building.

How do I automate routing extracted knowledge to memory versus vault notes?

Automated routing directs extracted atomic insights to either auto-memory or vault notes based on content. A multi-step workflow provides a preview and review phase, allowing optional refinement to ensure quality before writing to the destination.

Can I ingest content from Linear tickets and code repositories directly?

Yes, multi-source ingestion supports Linear tickets and code repositories alongside pasted content. The system extracts atomic insights from these sources and routes them to memory or vault notes after a preview and validation step.

What is the best way to verify extracted knowledge before saving it to a vault?

The best way to verify extracted knowledge before saving is using a multi-step workflow with preview and review phases. This process allows optional refinement of atomic insights, ensuring data quality through automated validation before writing to vault notes.

Does the ingestion workflow support raw text and PDF documents?

Yes, the ingestion workflow supports raw text and PDF documents. It extracts atomic insights from diverse formats including PDFs, images, code, and conversations, routing them to auto-memory or vault notes after validation.

What are the limitations of automated insight extraction from pasted content?

Automated insight extraction from pasted content requires a multi-step workflow with preview, review, and optional refinement to manage limitations. Automated validation ensures quality before writing insights to memory or vault notes.