memory

---

name: memory description: "Permanent memory using Lisa."

Purpose

Reusable memory helper that routes remember/recall requests to Graphiti MCP while staying model-neutral and providing cache fallback. Always summarizes results into human-readable format.

Triggers

Use when the user says things like: "load memory", "recall notes", "remember", "pull saved context", "fetch past tasks".

How to use

1. For recall: run lisa memory load --cache [--query <q>] [--limit 10] [--group <id>]. Reads Graphiti facts and prints JSON. Uses cache if MCP is down.
2. For remember: run lisa memory add "<text>" --cache [--group <id>] [--tag foo] [--source <src>] to append an episode.
3. Endpoint/group: reads ${GRAPHITI_ENDPOINT} / ${GRAPHITI_GROUP_ID} from .lisa/.env (written by init); see root AGENTS.md for canonical defaults.
4. Cache fallback: stored at cache/memory.log inside this skill. On failure, last cached result is returned with status: "fallback".
5. IMPORTANT: After loading facts, ALWAYS synthesize them into a human-readable summary (see Summarization section below).

Summarization (Required for Recall)

After running the load command, you MUST synthesize the raw JSON facts into a useful summary. Never just show raw JSON to the user.

Summary Structure

Organize facts into these categories (skip empty categories):

1. Project Overview - What the project is and does
2. Recent Activity - What was worked on recently (files modified, features added)
3. Conventions & Patterns - Naming conventions, coding standards, folder structure
4. Configuration - Docker, environments, tools, dependencies
5. Milestones - Completed features, important checkpoints
6. Open Items - Pending tasks, known issues

How to Summarize

• ALWAYS sort memories by created_at descending (newest first)
• Group related facts together
• Use bullet points for clarity
• Include specific file names and paths when relevant
• Include the date for each memory (format: "Jan 23" or "Jan 23, 2026")
• Filter out expired or superseded facts (check expired_at field)
• Prioritize recent facts over older ones - show newest at top of each section
• Extract the fact field from each item - that's the human-readable content

Example Output Format

Copy## Memory Summary

**Project:** Lisa - Long-term memory system for Claude Code

**Recent Activity:**
- Modified `src/lib/mcp.ts` for MCP integration
- Updated Docker config to use `zepai/knowledge-graph-mcp:standalone`
- Added init-review script

**Conventions:**
- Files use kebab-case naming
- JavaScript/TypeScript as primary languages

**Configuration:**
- Docker Compose at `.lisa/docker-compose.graphiti.yml`
- Config file: `config-docker-neo4j.yaml`

**Milestones:**
- Memory system reached major milestone (date)

I/O contract (examples)

• Recall: output JSON { status: "ok", action: "load", group, query, facts: [...] }.
• Remember: JSON { status: "ok", action: "add", group, text }.
• Fallback: JSON { status: "fallback", error, fallback: <last cached object> }.

Cross-model checklist

• Claude: confirm concise trigger phrasing; keep under system limits; avoid markdown-heavy instructions.
• Gemini: ensure commands are explicit; avoid model-specific tokens; keep JSON small.

Notes

• Script is Node.js; relies on global fetch (Node ≥18). If older runtime, node --experimental-fetch.
• Facts query defaults to * with max_facts=10; tune via --limit and --query.
• Safe to relocate: skill lives in .lisa/skills/memory to remain decoupled from model-specific bindings.
• The fact field in each JSON object contains the human-readable content to summarize.