memory — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited memory (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
Use when the user says things like: "load memory", "recall notes", "remember", "pull saved context", "fetch past tasks".
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: reads ${GRAPHITI_ENDPOINT} from .lisa/.env (written by init); group ID is automatically derived from the project folder path. 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).
After running the load command, you MUST synthesize the raw JSON facts into a useful summary. Never just show raw JSON to the user.
Organize facts into these categories (skip empty categories):
expired_at field)fact field from each item - that's the human-readable content## 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){ status: "ok", action: "load", group, query, facts: [...] }.{ status: "ok", action: "add", group, text }.{ status: "fallback", error, fallback: <last cached object> }.lisa CLI binary — no scripts to run directly.* with max_facts=10; tune via --limit and --query.fact field in each JSON object contains the human-readable content to summarize.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.