VineetV2

Peon Mem — Security skill for Claude Code

Security community

Local-first hierarchical memory brain for AI coding agents — auto-capture, LLM consolidation, hybrid retrieval, live Neural Universe monitor, daily self-audit.

How to install Peon Mem

This entry records only its repository, not the path inside it, so there is no exact command to give. Open VineetV2/peon-mem and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Peon Mem does

Local-first hierarchical memory brain for AI coding agents — auto-capture, LLM consolidation, hybrid retrieval, live Neural Universe monitor, daily self-audit. Claude Code / Codex / any MCP client.

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README

🧠 Peon — a memory brain for your AI coding agents

[![npm](https://img.shields.io/npm/v/peon-mem)](https://www.npmjs.com/package/peon-mem) [![downloads](https://img.shields.io/npm/dm/peon-mem)](https://www.npmjs.com/package/peon-mem) [![license](https://img.shields.io/badge/license-MIT-blue)](LICENSE) [![tests](https://img.shields.io/badge/tests-passing-brightgreen)](test/) [![MCP Registry](https://img.shields.io/badge/MCP%20Registry-listed-6b4fbb)](https://registry.modelcontextprotocol.io) [![Star on GitHub](https://img.shields.io/github/stars/VineetV2/peon-mem?style=social)](https://github.com/VineetV2/peon-mem)

**Local-first, hierarchical, self-improving memory for Claude Code, Codex, and any MCP client.**

Your AI forgets everything between sessions. Peon doesn't. It records your sessions, uses an LLM to consolidate them into typed *beliefs*, and injects the relevant ones back into every prompt. It runs as a daemon on your machine, and nothing leaves it.

        PEON GLOBAL BRAIN            ← user-level facts & preferences, inherited everywhere
       /        |         \
  project A  project B  project C    ← rooted child brains (.peon/ in each project)

Why Peon

Hierarchical brains. One global parent brain holds who you are, your rules, and your tools. Each project gets an isolated child brain. Every injection carries both.
Two memory layers, honestly measured. Consolidated beliefs (decisions, preferences, facts, artifacts) give you the gist. An episodic verbatim layer recovers the exact details that lossy summaries drop. In my own LongMemEval run, raw-episodic recall scored 61% where belief-only scored 17% — self-measured, not an independent benchmark; reproduce it with npm run eval.
Automatic capture and injection. Claude Code hooks record messages and events, then inject a query-ranked memory block (with an ⚠ MOST RELEVANT headline) into every prompt. You never have to remember to save anything.
**C