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mnemosyne

Skill Security Usable
Works inClaude Code
Usable Scanned — metadata only

Provides a secure, auditable mechanism for maintaining agentic context without leaking sensitive data to third-party cloud providers.

Auditable, local-first memory for AI agents in one SQLite file. MCP server for Claude Code, Cursor and Codex. npm: mnemosy-ai

153 starsMIT (commercial OK)FreeQuick setup
Usable rating — This tool is functional but has notable gaps. Review the evaluation notes below before deploying.

Provides a secure, auditable mechanism for maintaining agentic context without leaking sensitive data to third-party cloud providers. The local-first SQLite architecture mitigates data privacy risks during complex engineering workflows.

Security-conscious engineering teams requiring persistent, auditable memory for AI agents within local development environments.

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https://github.com/28naem-del/mnemosyne

By 28naem-del

How to Get It

Option 1: Claude Desktop App (Code Mode)Click the + button next to the prompt box → Plugins → Add plugin. Search and click Install. Skills work in Claude Code only.
Option 2: Paste into Claude CodeCopy the command below and paste it into your conversation. Claude will install it.
Command
claude plugins install 28naem-del/mnemosyne

Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.

Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.

First thing to try

After installing, paste this into Claude:

Help me store and retrieve technical context for AI agents in a local file
CostFree

Trust Signals Auto-scanned

Stars153Last updated2026-10-29LicenseMIT (OK for commercial use)Known CVEsNone foundSources: GitHub Advisory Database + OSV.dev · Scanned 2026-10-29 · scanner v1

Community Pulse Growing

Discussed on Hacker News

3 mentions across 1 sources

Reviewer notes

Auto-scanned review. These are observations, not a security certification.

Scored from trust signals (evidence-eval-v1): 153 GitHub stars; contributors unknown; last commit 0d ago; license MIT.

Things to check

  • Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.

How to evaluate tools before deploying →

Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.

Evaluation

Ease of Use
3/5
Versatility
3/5
Reliability
3/5
Security
3/5
Overall score3.00 / 5.00 UsableEvaluatedOct 2026
Scored from trust signals (evidence-eval-v1): 153 GitHub stars; contributors unknown; last commit 0d ago; license MIT.

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