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scheduled-noteslisted

Persistent notes a scheduled/recurring agent reads as its FIRST action and writes as its LAST action, so a weekly/nightly autonomous run "knows where it left off." Use inside /loop or /schedule tasks that span multiple wake-ups. Pairs with hypothesis calibration and the experiment queue.
Rockielab/rockie-claude · ★ 20 · AI & Automation · score 76
Install: claude install-skill Rockielab/rockie-claude
# /scheduled-notes — continuity across autonomous wake-ups Ported pattern: Cognition's "Devin can now schedule Devins" — the weekly task hands itself a note. Without this, a scheduled run starts cold every time and can't build on its own prior conclusions. ## Convention At the project root, maintain `SCHEDULED_NOTES.md`. It's a single bounded-length markdown file that the scheduled agent edits in place. Suggested structure: ```markdown # SCHEDULED_NOTES.md _Last updated: <ISO timestamp>_ _Current stage: creative_ ## Open threads (pick these up next) - Line 1: one-sentence thread + pointer to relevant journal node - Line 2: ... ## Recent conclusions (compact) - YYYY-MM-DD: outer-product init reduces warmup loss 0.02 (closed #42) - ... ## Blockers / decisions needed - (empty or a short note if human input would help) ``` Keep the whole file under ~2 KB. When it grows, compact into a line of "conclusions" and drop the oldest threads. ## Agent protocol First action of every scheduled run: ```bash cat SCHEDULED_NOTES.md ``` Last action before exit: ```bash # Update timestamp + current stage + open threads + recent conclusions # Use apply_patch.py or Edit — either is fine. ``` The natural composition is: 1. Read `SCHEDULED_NOTES.md` → know where you left off. 2. `python3 .claude/scripts/queue.py next --json` → pop next queue item. 3. Do the work (launch via autopilot_loop.sh or ad-hoc). 4. Close journal node + calibration. 5. Update `SCHEDULED_NOTES.md` (what go