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openclaw-mem
openclaw-mem에는 phenomenoner에서 수집한 skills 10개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.
이 저장소의 skills
Govern memory, episode, skill, and fact lifecycle maintenance through the unified curate scan, review, apply, verify, and rollback verbs.
Operate graph readiness, topology lookup, synthesis-aware routing, and symbolic evidence canvases. Use for project ownership, impact, dependency, or idea-to-project questions.
Govern embedding refresh, episodic evidence synchronization, metadata writeback, and engine dataset snapshots. Use before or after bounded sync, reindex, migration, or writeback operations.
Govern source-linked temporal facts in openclaw-mem. Use for current truth, timelines, stale detection, fact packs, and review-only fact extraction.
Operate experimental Dream Lite planning, governed refresh canaries, rollback, and Director rehearsals. Use only for explicitly reviewed derived card maintenance.
Operate the experimental GBrain lookup, restricted helper-job, and governed refresh-canary lanes. Use only when GBrain integration is explicitly enabled.
Operate the experimental read-only goal primitive and surface validation. Use for normalized goal status, compact goal packs, and receipt validation, not continuation or autonomous mutation.
Inspect experimental self-improvement consolidation surfaces. Use for read-only validation, goal readback, staged skill proposals, curator review, and system status.
Inspect and govern the experimental derived self-model sidecar. Use for continuity snapshots, attachment maps, drift, threats, public-safe wording, release receipts, or migration comparisons.
Trust-aware OpenClaw memory routing. Use when an agent must decide whether to recall, search docs or graph evidence, build a bounded pack, store a durable fact, or keep information session-local.