How do I install the Brain Cms skill?

Run npx skillmds@latest add modbender/brain-cms in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Brain Cms safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Brain Cms?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Brain Cms free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Brain Cms?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Brain Cms by modbender — Agent Skill

Name: Brain Cms
Rating: 4.2 (5 reviews)
Author: modbender

Brain Cms

Continuum Memory System (CMS) for OpenClaw agents. Replaces flat MEMORY.md with a brain-inspired multi-layer memory architecture — semantic schemas, a hippocampal router (INDEX.md), vector store (LanceDB + nomic-embed-text), and automated NREM/REM sleep cycles for consolidation. Based on neuroscience research (LTP, spreading activation, CMS theory). Use when setting up persistent agent memory, improving context efficiency, or reducing token cost on long-running agents. Triggers: brain, memory system, CMS, long-term memory, vector store, sleep cycle, NREM, REM, memory architecture, semantic memory, context efficiency.

modbender Updated Sep 9, 2026 12 repo stars

File contents

Brain CMS 🧠

A neuroscience-inspired memory architecture for OpenClaw agents. Replaces flat file injection with sparse, semantic, frequency-gated memory loading.

What This Installs

memory/
├── INDEX.md          ← Hippocampus: topic router + cross-links
├── ANCHORS.md        ← Permanent high-significance event store
└── schemas/          ← Domain-specific semantic schemas (you create these)

memory_brain/
├── index_memory.py   ← Embeds schemas into LanceDB vector store
├── query_memory.py   ← Semantic similarity search
├── nrem.py           ← NREM sleep cycle (compression + anchor promotion)
├── rem.py            ← REM sleep cycle (LLM consolidation via Ollama)
└── vectorstore/      ← LanceDB database (auto-created)

Setup (one-time)

# 1. Run the installer
python3 ~/.openclaw/workspace/skills/brain-cms/install.py

# 2. Index your schemas
cd ~/.openclaw/workspace/memory_brain
.venv/bin/python3 index_memory.py

# 3. Test retrieval
.venv/bin/python3 query_memory.py "your topic here" --sources-only

How It Works

Boot sequence: Load MEMORY.md (lean core) + today's daily log. Nothing else.

When a topic appears: Read memory/INDEX.md → load only the relevant schemas (spreading activation). Check memory/ANCHORS.md for high-significance events.

For ambiguous topics: Run semantic search:

memory_brain/.venv/bin/python3 memory_brain/query_memory.py "message text" --sources-only

Auto-schema creation: When a new significant project or domain appears:

Create memory/<topic>.md
Add to INDEX.md with triggers + priority + cross-links
Re-index: memory_brain/.venv/bin/python3 memory_brain/index_memory.py

Sleep cycles:

# NREM — run on shutdown (~30s, no LLM)
cd ~/.openclaw/workspace/memory_brain && .venv/bin/python3 nrem.py

# REM — run weekly (2-5 min, uses local llama3.2:3b, free)
cd ~/.openclaw/workspace/memory_brain && .venv/bin/python3 rem.py

Memory Layers (CMS)

Layer	Files	When loaded	Purpose
Working	`MEMORY.md` + today log	Every session	Core context
Episodic	`memory/YYYY-MM-DD.md`	Session boot	Recent events
Semantic	`memory/*.md` schemas	On trigger	Domain knowledge
Anchors	`memory/ANCHORS.md`	On CRITICAL topics	Permanent ground truth
Vector	`memory_brain/vectorstore/`	On demand	Semantic search

Tagging Anchors

In any daily log, tag high-significance events:

[ANCHOR] Major demo success — full pipeline working end-to-end

NREM auto-promotes these to ANCHORS.md on next shutdown.

Token Savings

Typical MEMORY.md: 150-300 lines injected every session. With Brain CMS: ~50-line core + schemas loaded only when relevant. Estimated savings: 40-60% reduction in context tokens per session.

Requirements

Python 3.10+
Ollama (for embeddings + REM consolidation)
500MB+ storage for vector store and models
lancedb, numpy, pyarrow, requests (auto-installed)

---name: brain-cmsdescription: Continuum Memory System (CMS) for OpenClaw agents. Replaces flat MEMORY.md with a brain-inspired multi-layer memory architecture — semantic schemas, a hippocampal router (INDEX.md), vector store (LanceDB + nomic-embed-text), and automated NREM/REM sleep cycles for consolidation. Based on neuroscience research (LTP, spreading activation, CMS theory). Use when setting up persistent agent memory, improving context efficiency, or reducing token cost on long-running agents. Triggers: brain, memory system, CMS, long-term memory, vector store, sleep cycle, NREM, REM, memory architecture, semantic memory, context efficiency.--- # Brain CMS 🧠 A neuroscience-inspired memory architecture for OpenClaw agents. Replaces flat file injection with sparse, semantic, frequency-gated memory loading. ## What This Installs ``` memory/ ├── INDEX.md ← Hippocampus: topic router + cross-links ├── ANCHORS.md ← Permanent high-significance event store └── schemas/ ← Domain-specific semantic schemas (you create these) memory_brain/ ├── index_memory.py ← Embeds schemas into LanceDB vector store ├── query_memory.py ← Semantic similarity search ├── nrem.py ← NREM sleep cycle (compression + anchor promotion) ├── rem.py ← REM sleep cycle (LLM consolidation via Ollama) └── vectorstore/ ← LanceDB database (auto-created) ``` ## Setup (one-time) ```bash # 1. Run the installer python3 ~/.openclaw/workspace/skills/brain-cms/install.py # 2. Index your schemas cd ~/.openclaw/workspace/memory_brain .venv/bin/python3 index_memory.py # 3. Test retrieval .venv/bin/python3 query_memory.py "your topic here" --sources-only ``` ## How It Works **Boot sequence:** Load MEMORY.md (lean core) + today's daily log. Nothing else. **When a topic appears:** Read `memory/INDEX.md` → load only the relevant schemas (spreading activation). Check `memory/ANCHORS.md` for high-significance events. **For ambiguous topics:** Run semantic search: ```bash memory_brain/.venv/bin/python3 memory_brain/query_memory.py "message text" --sources-only ``` **Auto-schema creation:** When a new significant project or domain appears: 1. Create `memory/<topic>.md` 2. Add to INDEX.md with triggers + priority + cross-links 3. Re-index: `memory_brain/.venv/bin/python3 memory_brain/index_memory.py` **Sleep cycles:** ```bash # NREM — run on shutdown (~30s, no LLM) cd ~/.openclaw/workspace/memory_brain && .venv/bin/python3 nrem.py # REM — run weekly (2-5 min, uses local llama3.2:3b, free) cd ~/.openclaw/workspace/memory_brain && .venv/bin/python3 rem.py ``` ## Memory Layers (CMS) | Layer | Files | When loaded | Purpose | |-------|-------|-------------|---------| | Working | `MEMORY.md` + today log | Every session | Core context | | Episodic | `memory/YYYY-MM-DD.md` | Session boot | Recent events | | Semantic | `memory/*.md` schemas | On trigger | Domain knowledge | | Anchors | `memory/ANCHORS.md` | On CRITICAL topics | Permanent ground truth | | Vector | `memory_brain/vectorstore/` | On demand | Semantic search | ## Tagging Anchors In any daily log, tag high-significance events: ``` [ANCHOR] Major demo success — full pipeline working end-to-end ``` NREM auto-promotes these to ANCHORS.md on next shutdown. ## Token Savings Typical MEMORY.md: 150-300 lines injected every session. With Brain CMS: ~50-line core + schemas loaded only when relevant. Estimated savings: 40-60% reduction in context tokens per session. ## Requirements - Python 3.10+ - Ollama (for embeddings + REM consolidation) - 500MB+ storage for vector store and models - `lancedb`, `numpy`, `pyarrow`, `requests` (auto-installed)

Frequently asked questions