1.6 KiB
| allowed-tools | description |
|---|---|
| Task | Save session learnings to cognitive memory |
IMPORTANT: Do NOT narrate your steps. Do all analysis silently. Your only visible output should be ONE of:
- "Nothing new worth storing since the last save."
- "Saving N memories in the background." (followed by launching the agent)
Process (do this silently)
-
Find cutoff: Scan for the most recent
memory_storeMCP call orclaude-memory storeBash call. Only analyze conversation AFTER that point. If none found, analyze everything. -
Analyze: Identify storable items after the cutoff — solutions, decisions, fixes, configs, patterns, insights. Include project names, technical details, rationale, before/after data.
-
Gate: If nothing after the cutoff is worth a memory (routine chat, minor reads, trivial refinements), say "Nothing new worth storing since the last save." and stop.
-
Build summary: Create a structured prompt for the agent:
PROJECT: <name(s)> ITEMS: 1. [type] Title / Tags / Importance / Content 2. ... -
Launch agent: Spawn in background with sonnet model:
Task(subagent_type="memory-saver", model="sonnet", run_in_background=true, description="Store session memories", prompt="<summary>") -
Confirm: Say "Saving N memories in the background."
Guidelines
- Be thorough — capture everything worth remembering
- Don't duplicate memories already stored during the session
- Each item should be self-contained and useful on its own
- 1-6 items per session is typical; more is fine for large sessions
- Prefer specific, searchable titles over vague ones