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The skill's files and runtime instructions generally match a cognitive-memory purpose, but there are several inconsistencies and behavioral surprises (local scripts that auto-commit, suggested edits t... [内容已截断]
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{
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"_id": "k972ara6vj411fdffd80nn2s0980hw0m",
"changelog": "### v1.0.8 — Adds explicit step-by-step reflection flow and token gating\n\n- Introduced a structured, four-step reflection process with STOP points for user approval at key stages.\n- Added mandatory user approval for token rewards before executing reflection, ensuring all reflections are user-initiated.\n- Updated documentation to clarify separation between reward request, reflection, and archival steps.\n- Improved reflection engine section, emphasizing internal monologue formatting and self-awareness tagging.\n- Expanded process transparency for reward logging, decay updates, and self-image consolidation triggers.\n- No code changes in this release (documentation\/process update only).",
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"version": "1.0.8"
},
"owner": {
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"_id": "publishers:missing",
"displayName": "Icemilo414",
"handle": "icemilo414",
"image": "https:\/\/avatars.githubusercontent.com\/u\/252997151?v=4",
"kind": "user",
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"skill": {
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"displayName": "Cognitive Memory",
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"slug": "cognitive-memory",
"stats": {
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"stars": 27,
"versions": 9
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"summary": "Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember\/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.",
"tags": {
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"Memory": "k978eyqwy5w57dp3k1w6rqp0m180faxj",
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