Memory System Complete

Complete memory system with causal graph, knowledge graph, auto-detection, and evolution features

This package appears to implement a local memory and graph system and does not request secrets, but there are a few red flags you should handle before installing/use: - Review the code before running post_install: SKILL.md runs python scripts (init_database_v2.py, verify_install_v2.py) which will create/modify files under memory/database/. Inspect those scripts to confirm no unwanted operations. - Version/manifest mismatches: the registry says v3.0.0 while SKILL.md lists v2.0.0 and README references v3.0 modules not present. Verify which files you actually received and whether any referenced modules are missing or stubbed out. - Optional networked integrations: Ollama and LanceDB support is present; ensure any configured endpoints point to localhost or trusted hosts — do not provide remote credentials or URLs unless you trust them. - Run in an isolated/sandbox environment first (container or VM) and back up any important data before initialization. - If you want higher assurance, ask the author for a minimal checklist: exact files included in the claimed release, and confirmation that verify_install scripts perform only local checks. If you cannot verify, do not run install scripts on production machines. If you want, I can: summarize which specific files mention network calls or external services, list all places where files are written/created by the init scripts, or show the exact lines that create/modify the database so you can review them quickly.

Type: OpenClaw Skill Name: memory-system-complete Version: 3.0.0 The memory-system-complete skill bundle is a comprehensive local memory management framework utilizing a dual-brain architecture (SQLite for structured data and LanceDB for vector search). The bundle includes advanced cognitive modeling features such as Theory of Mind (tom_engine.py), emotional analysis (emotional_analyzer.py), and complex relationship mapping via causal and knowledge graphs (causal_knowledge_graphs.py). The system is designed to run entirely locally, interacting with a local Ollama instance for embeddings (ollama_embedding.py) and storing data in a local SQLite database. Analysis of the installation scripts (init_database_v3.py) and core logic reveals no evidence of data exfiltration, backdoors, or malicious prompt injections; the code is well-structured and aligns strictly with its stated purpose of providing a sophisticated persistence layer for AI agents.