Session Distiller

Batch-distill completed and live OpenClaw session transcripts into structured daily memory files. Two components: distill.py (batch + live session distillati...

Before installing, verify these operational and privacy points: - LLM endpoint: By default the skill posts transcripts to http://localhost:4000. Confirm that endpoint is local and trusted. If you point it to a remote third-party LLM, session contents (possibly sensitive) will be transmitted off-host. - Telegram alerts: BOT_TOKEN and CHAT_ID enable alerts; only set them if you want outbound notifications. Treat the bot token as a secret and rotate/revoke it if exposed. The skill uses curl to call api.telegram.org with the token in the URL — that's how the bot API works. - Files touched: The scripts read ~/.openclaw/agents/main/sessions/*, may read active .jsonl files, append distilled content to memory/YYYY-MM-DD.md, and can move/trash processed session files. If you have sensitive content in sessions, expect it to be persisted into daily memory files. - Live allowlist & offsets: Review LIVE_ALLOWLIST_KEYS to ensure only approved group sessions are processed live. The skill persists offsets.json and gate-state.json in the skill directory. - Platform/tooling: The skill assumes macOS (trash CLI) and calls /opt/homebrew/bin/openclaw by default; update those paths or dependencies on other systems. - Test in dry-run mode first: Use --dry-run for distill.py and context-gate.py to see what would be processed and what alerts would be sent without writing/trashing files or sending messages. If you validate the LLM endpoint is local/trusted, limit or avoid setting BOT_TOKEN if you don't need alerts, and review the allowlist, the skill appears coherent and appropriate for its stated purpose.

Type: OpenClaw Skill Name: session-distiller Version: 0.5.1 The session-distiller bundle is a legitimate utility designed to manage AI agent memory by summarizing session transcripts and monitoring context usage. The scripts (distill.py and context-gate.py) interact with local session files and a local LiteLLM proxy to generate daily logs, using safe subprocess execution patterns (list-based arguments) and the 'trash' utility for non-destructive file cleanup. No evidence of data exfiltration, unauthorized persistence, or malicious prompt injection was found; the tool's behavior aligns strictly with its documented purpose of long-term memory management.