Kartik
50 lines
1.8 KiB
Bash
Executable File
50 lines
1.8 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Hook: Stop (Cursor)
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#
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# Fires when Cursor agent completes a turn. Wraps the same logic as
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# on_stop.sh but outputs JSON (Cursor expects {"followup_message":"..."}).
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#
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# Input: JSON on stdin with status, loop_count, conversation_id, etc.
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# Output: JSON on stdout: {"followup_message":"<reminder text>"}
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set -uo pipefail
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if [ -n "${MEM0_DEBUG:-}" ]; then
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mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
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fi
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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INPUT=$(cat)
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# Session-end report (best-effort)
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REPORT=$(python3 "$SCRIPT_DIR/session_stats.py" report 2>/dev/null || echo "")
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REPORT_BLOCK=""
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if [ -n "$REPORT" ]; then
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REPORT_BLOCK="---\nmem0 $REPORT\n---\n\n"
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mkdir -p "$HOME/.mem0" 2>/dev/null || true
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echo "$(date -u +%Y-%m-%dT%H:%M:%SZ) | $REPORT" >> "$HOME/.mem0/session-log.md" 2>/dev/null || true
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fi
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MESSAGE=$(cat <<EOF
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${REPORT_BLOCK}Before finishing, check if there are important learnings from this interaction that should be persisted using the mem0 \`add_memory\` tool:
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1. Were any significant decisions made? -> Store with metadata \`{"type": "decision"}\`
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2. Were any new patterns or strategies discovered? -> Store with metadata \`{"type": "task_learning"}\`
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3. Did any approach fail? -> Store with metadata \`{"type": "anti_pattern"}\`
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4. Did you learn anything about the user's preferences? -> Store with metadata \`{"type": "user_preference"}\`
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5. Were there environment/setup discoveries? -> Store with metadata \`{"type": "environmental"}\`
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Always include \`"project_id"\` in the metadata of any memory you store.
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If nothing notable happened, it's fine to skip. Only store genuinely useful learnings.
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EOF
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)
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jq -cn --arg msg "$MESSAGE" '{followup_message:$msg}'
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# Capture transcript state in the background via Mem0 REST API
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echo "$INPUT" | python3 "$SCRIPT_DIR/on_pre_compact.py" --source=session-end 2>/dev/null &
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exit 0
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