extract

Extract durable facts from transcripts into a Memory graph via a configured AI provider.

20|2|Updated May 16, 2026
One-click install
npx skills add https://github.com/reddb-io/red-skills --skill extract-reddb-io
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: extract
Source: https://github.com/reddb-io/red-skills/tree/main/plugins/memory/skills/core/extract
Command: npx skills add https://github.com/reddb-io/red-skills --skill extract-reddb-io

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Valuable operational learning from conversations—decisions, root causes, gotchas, and reasoning traces—is lost when a session ends. This Skill turns a transcript or log into durable, queryable facts stored in the Memory graph so agents stop repeating old mistakes after context resets. ## Core Features & Use Cases - LLM-Backed Extraction: Reads a conversation or log from a file or stdin and asks the configured AI provider to identify durable facts. - Graph Upsert: Writes extracted knowledge as INFERRED nodes and edges into the Memory graph, reporting how many facts and edges were created. - Verification via Recall: Confirms the new facts are discoverable by running a targeted recall query after extraction. - Use Case: After a long debugging session, pipe the session transcript into the extract command so the root cause and the fix constraint are preserved as graph facts that future sessions can recall. ## Quick Start Extract the durable decisions and gotchas from my session transcript file debug-session.log into the Memory graph, then verify them with a recall query.

Frequently Asked Questions about extract

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

FAQPage Schema
How do I extract facts from a conversation transcript into a memory graph?▼

Run the extract command with a transcript file or pipe it via stdin using the plugin's bootstrap script. The configured AI provider identifies durable facts and upserts them as INFERRED nodes and edges into the Memory graph.

What content should I extract from a transcript into memory?▼

Extract durable operational learning: decisions, root causes, gotchas, why-notes, and failed attempts that explain constraints. Avoid secrets, credentials, personal data, PR numbers, commit SHAs, and transient task-completion logs.

Does memory extraction work without a configured AI provider?▼

No. Extraction requires graph mode and a provider block in the plugins.memory section of .red/config.yaml. If either is missing, the skill stops and explains rather than falling back to markdown notes.

When should I use extract instead of storing a single memory fact?▼

Use extract for rich transcripts containing multiple decisions and reasoning traces. Use the store command for a single hand-authored fact. Extraction mutates the graph, so do not run it just to summarize a conversation.

How do I verify extracted memory facts were saved correctly?▼

Run a targeted recall query for one or two extracted topics after extraction. The recall command confirms whether the new INFERRED facts are discoverable in the graph.