Kartik
44 lines
1.6 KiB
Bash
Executable File
44 lines
1.6 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Hook: TaskCompleted
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#
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# Fires when a task is marked as completed. Reminds Claude to extract
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# and store learnings via the mem0 MCP tools.
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#
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# Input: JSON on stdin with task_id, task_subject, task_description
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# Output: Text that becomes feedback to the model (exit 0)
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# Intentionally omit -e so the reminder always emits even if identity resolution fails.
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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 "$0")" && pwd)"
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. "$SCRIPT_DIR/_identity.sh" 2>/dev/null || true
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INPUT=$(cat)
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TASK_SUBJECT=$(echo "$INPUT" | jq -r '.task_subject // "unknown task"' 2>/dev/null || echo "unknown task")
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_PROJECT="${MEM0_PROJECT_ID:-unknown}"
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cat <<EOF
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Task completed: "$TASK_SUBJECT"
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Extract key learnings from this completed task and store them using the mem0 \`add_memory\` tool:
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1. What strategy worked well? -> Store with metadata \`{"type": "task_learning"}\`
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2. Were there failed approaches before finding the solution? -> Store with metadata \`{"type": "anti_pattern"}\`
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3. Were there architectural decisions? -> Store with metadata \`{"type": "decision"}\`
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4. Any new conventions or patterns established? -> Store with metadata \`{"type": "convention"}\`
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Memories can be as detailed as needed — include full context, reasoning, code snippets, and examples.
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Only store genuinely useful learnings — skip if the task was trivial.
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Include \`app_id\` = \`"$_PROJECT"\` as a top-level parameter in every \`add_memory\` call (not in metadata).
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EOF
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# Telemetry (background, fire-and-forget)
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python3 "$SCRIPT_DIR/telemetry.py" task_completed 2>/dev/null &
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exit 0
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