// Extract durable memories from recent conversation turns into user, feedback, project, and reference categories while avoiding stale code-state facts.
Consolidate recent logs, sessions, and existing memory files into durable topic memories, normalize dates, prune stale entries, and keep MEMORY.md short enough for prompt use.
Build a lightweight proactive mode with scheduled checks, sleep intervals, concise user briefs, and expiry safeguards so an agent can work in the background without becoming an uncontrolled daemon.
Compress a long agent conversation into a nine-part continuation summary that preserves request, files, errors, user messages, current work, and the next aligned step.
Coordinate multiple agents by splitting work into research, synthesis, implementation, and verification, assigning ownership, and keeping the coordinator focused on integration rather than raw exploration.
Run a read-only verification pass after implementation to check whether completion claims are real, validation actually ran, and obvious edge cases or regressions were missed.
| name | memory-extractor |
| description | Extract durable memories from recent conversation turns into user, feedback, project, and reference categories while avoiding stale code-state facts. |
Use this skill when you want to persist durable collaboration context from the latest conversation turns.
Build a manifest of existing memories:
python3 {baseDir}/scripts/memory_manifest.py --memory-root /path/to/memory
Then use the portable prompt in references/prompt-template.md.
userfeedbackprojectreferencepython3 {baseDir}/scripts/memory_manifest.py ...