extract-memory

name	extract-memory
description	Extract durable learnings and preferences from the current conversation for memory capture. Use this after a conversation to capture what should be remembered.
kind	sop

Extract Memory

Overview

Extract durable learnings and user preferences from a conversation that will remain useful across future sessions. This is the extraction phase of memory management - run this after conversations to capture what matters.

When to use:

• After a substantial conversation with decisions, corrections, or preferences
• When you want to capture what was learned from this specific session
• Before running cleanup-memory (which organizes existing memories)

Difference from cleanup-memory:

• extract-memory = "What should I remember from this conversation?" (captures new)
• cleanup-memory = "Clean up my existing memories" (organizes old)

Parameters

• conversation (required): The serialized conversation text to extract from
• existing_memories (optional): Already-stored memories to avoid duplicating

Steps

1. Classify Conversation Content

Scan the conversation and classify each substantive exchange:

Extractable:

• Final decisions (user confirmed or explicitly chose something)
• Corrections (user said "no, do X instead" or "that's wrong")
• Stated preferences ("I prefer X", "always do Y", "don't use Z")
• Discovered facts about environment, tools, or APIs that were verified
• Patterns that were tested and confirmed working
• Bug fixes with root causes identified

Not extractable:

• Intermediate discussion before a decision was reached
• Options considered but rejected
• Transient states ("GitHub is down right now")
• Obvious facts any model would know
• One-off task details ("fix the bug on line 42")
• Anything the user explored but didn't commit to
• Version numbers, heartbeat reports, benchmark scores
• Task completion status, UI/feature implementation details

2. Check Against Existing Memories

Compare candidates against existing memories to avoid duplicates.

3. Draft Extractions

Write concise, actionable memory entries (under 200 characters).

Good: "Use printf '%s' instead of echo for piping to jq — echo adds trailing newline" Bad: "User prefers better approaches"

4. Categorize

Assign type and category:

• Learning types: Corrections, discovered patterns, environment facts, bug root causes
• Preference categories: Communication, Code, Tools, Workflow, General

Output Format

{
  "learnings": [
    {"text": "concise, actionable learning statement"}
  ],
  "preferences": [
    {"category": "Communication|Code|Tools|Workflow|General", "text": "concise preference statement"}
  ]
}

Examples

Good extraction (decision):

{"learnings": [{"text": "Rho tmux config at ~/.rho/tmux.conf is now the active ~/.tmux.conf"}]}

Good extraction (preference):

{"preferences": [{"category": "Communication", "text": "X post voice: understated, self-aware, dry"}]}

Correct output (nothing to extract):

{"learnings": [], "preferences": []}

Next Steps

After extracting memories, consider running cleanup-memory periodically to organize and prune your brain storage.

See Also

• [[cleanup-memory]] - Organize existing memories