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memory-recall
The memory-recall skill searches and retrieves relevant context from past sessions stored in memsearch, enabling Claude to answer questions about previous decisions, debugging notes, and project history. Use it when the user asks about past context (such as "what did we decide about X" or "have I seen this before"), when memsearch memory hints are injected into the conversation, or when historical information would significantly enhance the response. Skip this skill for questions about current code state, ephemeral tasks, or when the user explicitly requests to ignore memory.
Instalar en Claude Code
Copiargit clone --depth 1 https://github.com/zilliztech/memsearch /tmp/memory-recall && cp -r /tmp/memory-recall/plugins/opencode/skills/memory-recall ~/.claude/skills/memory-recallDespués abre una sesión nueva de Claude Code; el skill carga automáticamente.
Definición
SKILL.md
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 'root=$(git rev-parse --show-toplevel 2>/dev/null || true); if [ -n "$root" ]; then bash __INSTALL_DIR__/scripts/derive-collection.sh "$root"; else bash __INSTALL_DIR__/scripts/derive-collection.sh; 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 info), and the original conversation seems critical: - If the anchor contains `turn:`, run `python3 __INSTALL_DIR__/scripts/parse-transcript.py <session_id> --turn <turn_id> --context 3` to retrieve the original conversation around that turn. - If the anchor only contains `db:` / `session:` with no turn cursor, run `python3 __INSTALL_DIR__/scripts/parse-transcript.py <session_id> --limit 10` to retrieve the most recent turns from the SQLite database. - If the anchor format is unfamiliar (e.g. `transcript:`, `rollout:` instead of `db:`), 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: - `ls -t .memsearch/memory/ | head -10` — recent daily logs - `grep -h "^## " .memsearch/memory/*.md | sort -u | tail -40` — session headings across all days - `cat .memsearch/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."