Automated daily memory backfill for OpenClaw sessions

Scrape and analyze OpenClaw JSONL session logs to reconstruct and backfill agent memory files. Use when: (1) Memory appears incomplete after model switches, (2) Verifying memory coverage, (3) Reconstructing lost memory, (4) Automated daily memory sync via cron/heartbeat. Supports simple extraction and LLM-based narrative summaries with automatic secret sanitization.

This tool legitimately needs access to your OpenClaw session JSONL and memory directories to do its job, and it includes extensive secret-detection patterns to redact sensitive data — but redaction claims alone do not guarantee safety. Before installing or scheduling it: 1) Review memory_sync.py (search for any network calls, hardcoded endpoints, or functions that send data to remote hosts). 2) Run in dry-run mode on non-sensitive test data and verify that SECRET_PATTERNS actually redact secrets before any network operations. 3) If you plan to use external summarization backends (openai/anthropic), be explicit about the API key you supply and understand that data will leave your machine; prefer the 'openclaw' backend if you trust your configured model. 4) Backup memory files first and test --preserve/--force behaviors so you don't overwrite user notes. 5) Consider running it in a restricted environment (container or isolated user) and restrict cron ownership/permissions. 6) Ask the publisher for a homepage, source repo, or reproducible build; absence of a source/homepage reduces transparency. If you cannot review the code yourself, treat this skill as high-risk for sensitive accounts and data.

Type: OpenClaw Skill Name: memory-sync Version: 1.0.1 The OpenClaw skill `memory-sync` is designed to process local OpenClaw session logs and generate daily memory files, optionally using LLM summarization. The code includes robust secret sanitization (`sanitize_content` function and `SECRET_PATTERNS` list in `memory_sync.py`) to prevent sensitive data from being written to memory files or sent to external LLM APIs (OpenAI, Anthropic). The `SKILL.md` instructions are aligned with the stated purpose and do not contain malicious prompt injection attempts. The skill's use of `subprocess.run` to call `openclaw sessions spawn` is an expected internal interaction within the OpenClaw ecosystem. All file system and network access (to LLM APIs) are consistent with its stated, benign purpose, with strong safeguards against data leakage.