smixs

Agent Memory Skill — Development skill for Claude Code

Development community

License: MIT.

How to install Agent Memory Skill

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

What Agent Memory Skill does

License: MIT.

Alternatives in Development

License — Check that every Python source file carries the required SPDX Apache-2.0 license header 1.1k ★
Mulmoterminal — Run multiple Claude Code and Codex sessions in parallel — a browser terminal grid that shows which agent needs 187 ★
Product Requirements Document — Agent-skill-manager — Product: agent-skill-manager (asm) Version: 1.10.0 (current) → 3.0 (vision) Author: luongnv89 Date: 2026-03-19 185 ★

README

agent-memory-skill

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![Python 3.10+](https://img.shields.io/badge/Python-3.10+-green.svg)](https://python.org) [![No Dependencies](https://img.shields.io/badge/Dependencies-Zero-brightgreen.svg)](#)

**Memory system for AI agents based on the Ebbinghaus forgetting curve.**

One Python file. Zero dependencies. Works with any directory of markdown files.

  Memory Strength
  1.0 ┤████████████████████████████████████████████████  ← just accessed
      │ ╲
  0.9 ┤   ╲                                              ← Day 7: active
      │     ╲
  0.8 ┤       ╲
      │         ╲
  0.7 ┤           ╲                                       ← Day 21: warm
      │             ╲
  0.6 ┤               ╲
      │                 ╲
  0.5 ┤                   ╲                               ← Day 33: cold
      │                     ╲
  0.4 ┤                       ╲
      │                         ╲
  0.3 ┤                           ╲
      │                             ╲
  0.2 ┤                               ╲
      │                                 ╲
  0.1 ┤─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─floor  ← Day 60+: archive
      │
  0.0 ┤
      └──┬──────┬──────────────┬────────────┬──────────→  Days
         0      7              21           60

         active    warm          cold         archive

The Problem

AI agents forget everything between sessions. Two common solutions — and why they don't work:

**"Dump everything into context"** — Loads entire knowledge bases per turn. Burns 20K+ tokens before the conversation starts. No prioritization: a 2-month-old contact gets the same weight as yesterday's hot lead.

**"Vector search everything"** — Retrieves by semantic similarity but has no temporal awareness. A card accessed yesterday and a card untouched for 90 days look identical. No concept of "fading" or "forgetting." No serendipity.

Human memory does something different: it **decays