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Get Started Free →Store a learning, pattern, or decision in the memory system for future recall.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 126% | 0% |
| case-20 | ✓→✗ | ▼ Worse | 34% | 0% |
| case-21 | ✓→✗ | ▼ Worse | -49% | 0% |
| case-22 | ✓→✗ | ▼ Worse | 30% | 0% |
| case-23 | ✓→✗ | ▼ Worse | 114% | 0% |
Store a learning, pattern, or decision in the memory system for future recall.
/remember <what you learned>Or with explicit type:
/remember --type WORKING_SOLUTION <what you learned>/remember TypeScript hooks require npm install before they work
/remember --type ARCHITECTURAL_DECISION Session affinity uses terminal PID
/remember --type FAILED_APPROACH Don't use subshell for store_learning command| Type | Use For | |------|---------| | WORKING_SOLUTION | Fixes, solutions that worked (default) | | ARCHITECTURAL_DECISION | Design choices, system structure | | CODEBASE_PATTERN | Patterns discovered in code | | FAILED_APPROACH | What didn't work | | ERROR_FIX | Specific error resolutions |
When this skill is invoked, run:
bashcd $CLAUDE_OPC_DIR && PYTHONPATH=. uv run python scripts/core/store_learning.py \ --session-id "manual-$(date +%Y%m%d-%H%M)" \ --type <TYPE or WORKING_SOLUTION> \ --content "<ARGS>" \ --context "manual entry via /remember" \ --confidence medium
If no --type specified, infer from content:
Other measured skills in the registry, with their headline benchmark lift.