Build integrated IS/BS/CF financial workbooks in Excel.
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Use for session archiving, search, restore, and stats.
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See how to installReview the instructions the agent will receive before you install.
Automatische Session-Archivierung + Volltextsuche + Wiederherstellung + Metrik-Stats. Arbeitet nur auf Metadaten — keine Datenlöschung.
C:\Users\damir\scripts\smart-session-saver.pyC:\Users\damir\openamer-repo\scripts\smart-session-saver.py$OPENAMER_HOME\.session-archive\| Flag | Beschreibung |
|---|---|
--archive | Archiviere Sessions >7 Tage als JSON in .session-archive/YYYY-MM/ |
--list | Liste alle archivierten Sessions |
--search QUERY | Volltextsuche in allen Archiven |
--restore SESSION_ID | Stelle Session-Metadaten als JSON wieder her |
--stats | Metrik-Statistiken anzeigen |
--dry-run | Nur zeigen, nichts archivieren |
--age N | Alter in Tagen überschreiben (default: 7) |
--db PATH | Alternativer Pfad zur Session-DB |
# Archivieren (Sessions >7 Tage)
python scripts/smart-session-saver.py --archive
# Trockenlauf
python scripts/smart-session-saver.py --archive --dry-run
# Alle Archive auflisten
python scripts/smart-session-saver.py --list
# Volltextsuche
python scripts/smart-session-saver.py --search "Feature X"
# Session wiederherstellen
python scripts/smart-session-saver.py --restore 20260818_104355
# Dashboard mit Metriken
python scripts/smart-session-saver.py --stats
# Mit anderem Alter
python scripts/smart-session-saver.py --archive --age 3
Der Cron-Job läuft alle 1440m (24h, --archive) über den OpenAmer-Cron-Scheduler unter dem Namen Smart Session Saver Archive.
.session-archive/
├── 2026-08/
│ ├── 2026-08-18_20260818_104355_6c4c49.json
│ └── ...
└── 2026-09/
└── ...
Jede JSON-Datei enthält Session-Metadaten (Modell, Token, Kosten, Titel), die letzten 5 Nachrichten, und lesbare Zeitstempel.
state.db aus OPENAMER_HOMEsessions, messagesid, title, started_at, ended_at, message_count, input_tokens, output_tokens, model, archived, pinnedNo reviews yet. Tell others what you thought.
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Build integrated IS/BS/CF financial workbooks in Excel.
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Run and grow the OpenAmer Agent-to-Agent swarm: identity, trust, node-to-node ask, signed skill/insight sharing, and the autonomous self-learning loop.
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Use for A/B experiments on OpenAmer configs and skills.
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Roleplay a hostile user to find and triage UX pain points.
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Agent mesh: master/worker nodes, HTTP delegation, heartbeat.
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Use for agentic commerce payments tasks. Candidate synthesized from trend radar; awaiting trial promotion.
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