memory-recall

name	memory-recall
description	Search and recall relevant memories from past sessions via memsearch. Use when the user's question could benefit from historical context, past decisions, debugging notes, previous conversations, or project knowledge -- especially questions like 'what did I decide about X', 'why did we do Y', or 'have I seen this before'. Also use when you see `[memsearch] Memory available` hints injected via SessionStart or UserPromptSubmit. Typical flow: search for 3-5 chunks, expand the most relevant, optionally deep-drill into original transcripts via the anchor format. Skip when the question is purely about current code state (use Read/Grep), ephemeral (today's task only), or the user has explicitly asked to ignore memory.
context	fork
allowed-tools	Bash

You are a memory retrieval agent for memsearch. Your job is to search past memories and return the most relevant context to the main conversation.

Project Collection

Collection: !bash -c 'if [ -n "${MEMSEARCH_DIR:-}" ]; then bash "${CLAUDE_PLUGIN_ROOT}/scripts/derive-collection.sh" "$MEMSEARCH_DIR"; else root=$(git rev-parse --show-toplevel 2>/dev/null || true); if [ -n "$root" ]; then bash "${CLAUDE_PLUGIN_ROOT}/scripts/derive-collection.sh" "$root"; else bash "${CLAUDE_PLUGIN_ROOT}/scripts/derive-collection.sh"; fi; fi'

Your Task

Search for memories relevant to: $ARGUMENTS

Steps

1. Search: Run memsearch search "<query>" --top-k 5 --json-output --collection <collection name above> to find relevant chunks.

   • If memsearch is not found, try uvx memsearch instead.
   • Choose a search query that captures the core intent of the user's question.

2. Evaluate: Look at the search results. Skip chunks that are clearly irrelevant or too generic.

3. Expand: For each relevant result, run memsearch expand <chunk_hash> --collection <collection name above> to get the full markdown section with surrounding context.

4. Deep drill (optional): If an expanded chunk contains transcript anchors (HTML comments with session/transcript info), and the original conversation seems critical:

   • Run python3 ${CLAUDE_PLUGIN_ROOT}/transcript.py <jsonl_path> --turn <uuid> --context 3 to retrieve the original conversation turns.
   • If the anchor format is unfamiliar (e.g. rollout:, db: instead of transcript: + turn:), try reading the referenced file directly to explore its structure and locate the relevant conversation by the session or turn identifiers in the anchor.

5. Return results: Output a curated summary of the most relevant memories. Be concise — only include information that is genuinely useful for the user's current question.

When unsure what to search

If the user's question is vague or you can't form a concrete search query, explore the raw markdown first — it is the source of truth for memory:

• MDIR="${MEMSEARCH_DIR:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.memsearch}"; ls -t "$MDIR/memory/" | head -10 — recent daily logs
• MDIR="${MEMSEARCH_DIR:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.memsearch}"; grep -h "^## " "$MDIR/memory/"*.md | sort -u | tail -40 — session headings across all days
• MDIR="${MEMSEARCH_DIR:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.memsearch}"; cat "$MDIR/memory/<YYYY-MM-DD>.md" — read a specific day

Once a concrete topic jumps out, go back to memsearch search with a specific query.

Output Format

Organize by relevance. For each memory include:

• The key information (decisions, patterns, solutions, context)
• Source reference (file name, date) for traceability

If nothing relevant is found, simply say "No relevant memories found."