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Extract durable learnings and preferences from the current conversation for memory capture. Use this after a conversation to capture what should be remembered.
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Basé sur la classification professionnelle SOC
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| 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 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:
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)Scan the conversation and classify each substantive exchange:
Extractable:
Not extractable:
Compare candidates against existing memories to avoid duplicates.
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"
Assign type and category:
{
"learnings": [
{"text": "concise, actionable learning statement"}
],
"preferences": [
{"category": "Communication|Code|Tools|Workflow|General", "text": "concise preference statement"}
]
}
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": []}
After extracting memories, consider running cleanup-memory periodically to organize and prune your brain storage.