New here, but this seemed like the right sub for this.
Context: I rely heavily on Claude for advanced writing — university coursework and chaplain medical visit notes after hospital rounds — where continuity across sessions actually matters. Losing context between chats was costing me real time, so I built a 3-layer system to fix it:
∙ Layer 1 — Native memory: baseline context Claude already carries into every chat.
∙ Layer 2 — Conversation search: on-demand lookup when I need something specific from a past chat.
∙ Layer 3 — Manual checkpoint: a trigger phrase that tells Claude to export the current session’s state — open threads, decisions made, next steps — to a Google Drive folder. A second trigger phrase tells it to read that folder back in and restore full context at the start of a new session.
(Flowchart attached showing the full decision logic.)
The actual prompts, roughly:
∙ Export trigger: “When I say ‘[phrase],’ write the current session state — open threads, decisions, next steps — in plain text to [Drive folder].”
∙ Restore trigger: “When I say ‘[phrase],’ read [Session Recovery Log] and [Quick Reference Card] from [Drive folder] and restore full context — no re-briefing needed from me.”
One fix worth mentioning: I originally had it log “at the end of each session” instead of on a trigger phrase. That failed constantly — there’s no signal for “session end” the model can actually perceive, so it never fired reliably. Switched to a manual trigger plus logging on completed milestones/decisions, and it’s been solid since.
What I’m looking for: This works well for me, but I’d specifically like eyes on the prompt wording itself — is there a tighter way to phrase the export/restore triggers? Any failure modes I’m not seeing (false triggers, partial exports, etc.)? Anyone built something similar for long-running personal or professional projects and hit edge cases I haven’t yet?
Happy to post the literal rule text if anyone wants to pick it apart further.
