Dicklesworthstone

Cass Memory System

Name: Cass Memory System
Author: Dicklesworthstone

AI community

SOURCE https://github.com/Dicklesworthstone/cass_memory_system

Description

Procedural memory for AI coding agents: transforms scattered session history into persistent, cross-agent memory so every agent learns from every other

Installation

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

README

cass-memory

![Platform](https://img.shields.io/badge/platform-Linux%20%7C%20macOS%20%7C%20Windows-blue.svg) ![Runtime](https://img.shields.io/badge/runtime-Bun-f472b6.svg) ![Status](https://img.shields.io/badge/status-alpha-purple.svg) ![License](https://img.shields.io/badge/license-MIT-green.svg)

**Procedural memory for AI coding agents.** Transforms scattered agent sessions into persistent, cross-agent memory—so every agent learns from every other agent's experience.

**One-liner install (Linux/macOS):**

curl -fsSL "https://raw.githubusercontent.com/Dicklesworthstone/cass_memory_system/main/install.sh?$(date +%s)" \
  | bash -s -- --easy-mode --verify

**Or via package managers:**

# macOS/Linux (Homebrew)
brew install dicklesworthstone/tap/cm

# Windows (Scoop)
scoop bucket add dicklesworthstone https://github.com/Dicklesworthstone/scoop-bucket
scoop install dicklesworthstone/cm

🤖 Agent Quickstart (JSON)

**Always use `--json` in agent contexts.** stdout = data, stderr = diagnostics, exit 0 = success.

# 1) Get task-specific memory before you start
cm context "implement auth rate limiting" --json

# 2) See the minimum viable workflow
cm quickstart --json

# 3) Build the playbook (memory onboarding)
cm onboard status --json
cm onboard sample --fill-gaps --json
cm onboard read /path/to/session.jsonl --template --json
cm onboard mark-done /path/to/session.jsonl