{"id":"ecd3c130-c25d-41b7-87fd-ac2a02601e7b","shortId":"G5T8L6","kind":"skill","title":"Review-gate OpenClaw memory hygiene with openclaw-mem","tagline":"Pack trusted context and review memory writes before long OpenClaw sessions drift or accumulate low-quality memory.","description":"# Review-gate OpenClaw memory hygiene with openclaw-mem\n\nPack trusted context and review memory writes before long OpenClaw sessions drift or accumulate low-quality memory.\n\n## Prerequisites\n\nOpenClaw, SQLite, local filesystem access\n\n## Installation\n\nUse the upstream install or setup path that matches your environment:\n- pip install openclaw-context-pack\n- git clone https://github.com/phenomenoner/openclaw-mem.git\n- uv sync --locked\n- uv run --python 3.13 --frozen -- \\\n\nRequirements and caveats from upstream:\n- python -m venv .venv\n- python benchmarks/trust_policy_synthetic_proof.py --json\n\nBasic usage or getting-started notes:\n- **Run the synthetic proof:** [Trust-policy synthetic proof](docs/showcase/trust-policy-synthetic-proof.md)\n- **Pack**: run pack to get a bounded bundle_text and context_pack (schema: openclaw-mem.context-pack.v1), with citations, trust policy, and trace receipts.\n- openclaw-mem self-curator verify --receipt .state/self-curator/apply-runs/<run>/apply-receipt.json --json\n\n- Source: https://github.com/phenomenoner/openclaw-mem\n- Extracted from upstream docs: https://raw.githubusercontent.com/phenomenoner/openclaw-mem/HEAD/README.md\n\n## Documentation\n\n- https://phenomenoner.github.io/openclaw-mem/\n\n## Source\n\n- [Agent Skill 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