Biomimetic emotional mind engine for AI Agents. Provides human-like emotional responses through a 5-layer neural conduction pipeline (L0 Stochastic Noise → L1 Sensor Perception → L2 Subconscious Impulses → L3 Personality Gate → L4 Decision Output) plus 5 psychodynamic patches. Fully decoupled from any LLM — runs locally on CPU with pure Python. Simulates 150 daily impulses across 9 categories with circadian rhythms, mood modulation, and short-term memory influence.
Scanned 9/7/2026
Install to Claude Code
npx -y skills add modbender/skill-library-mcp --skill mindcore --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Mindcore?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/modbender-mindcore)More formats (shields.io, HTML) on the badges page.
---
name: mindcore
description: >
Biomimetic emotional mind engine for AI Agents. Provides human-like emotional
responses through a 5-layer neural conduction pipeline (L0 Stochastic Noise →
L1 Sensor Perception → L2 Subconscious Impulses → L3 Personality Gate →
L4 Decision Output) plus 5 psychodynamic patches. Fully decoupled from any
LLM — runs locally on CPU with pure Python. Simulates 150 daily impulses
across 9 categories with circadian rhythms, mood modulation, and short-term
memory influence.
---
# MindCore — Biomimetic Subconscious Engine
> Give your AI agent autonomous thoughts, emotions, and spontaneous impulses.
## What It Does
MindCore is a standalone background daemon that simulates a **subconscious
mind**. It rolls dice every second, modeling the random emergence of thoughts
like *"I want milk tea"*, *"I'm bored"*, or *"I suddenly want to chat"*.
When a thought's probability accumulates past the firing threshold, the engine
outputs a JSON signal telling your AI Agent: **"I have something to say."**
## Architecture
```
Layer 0: Noise Generators (3000 nodes)
├── Pink Noise (1/f, long-range correlation)
├── Ornstein-Uhlenbeck (physiological baseline)
├── Hawkes Process (emotional chain reaction)
└── Markov Chain (attention drift)
↓
Layer 1: Sensor Layer (150 sensors)
├── Body State (hunger/fatigue/bio-rhythms)
├── Environment (time/weather/noise)
└── Social Context (interaction/neglect)
↓
Layer 2: Impulse Emergence (150 impulse nodes)
├── Synapse Matrix (sensor → impulse mapping)
├── Sigmoid Probability + Mood Modulation
└── Dice Roll → Random Firing
↓
Layer 3: Personality Gate (Softmax Sampling)
├── Learnable Personality Weights
└── Short-Term Memory Topic Boost
↓
Layer 4: Output Template → JSON signal
```
## Quick Start
```bash
# Install dependencies
pip install -r requirements.txt
# Start the engine
python main.py
```
Requires Python 3.8+. On first run, automatically downloads `all-MiniLM-L6-v2`
local NLP model (~80MB) for synapse matrix generation.
## Key Features
- **150 Daily Impulses** across 9 categories (food, social, entertainment, etc.)
- **Stochastic, Not Scheduled** — Pink Noise + Hawkes Process + Sigmoid probability
- **Circadian Rhythms** — real clock-driven hunger/thirst/sleep cycles
- **Short-Term Memory** — 5-slot FIFO buffer with 2-hour exponential decay
- **Mood Baseline** — continuous valence modulation of impulse probability
- **Tunable Frequency** — single `BURST_BASE_OFFSET` parameter controls activity
## Integration
MindCore outputs standard JSON and is designed for [OpenClaw](https://openclaw.ai)
but compatible with any AI Agent framework that supports external signal injection.
See `references/INTEGRATION.md` for detailed integration guide.
## File Structure
- `main.py` — Entry point and engine loop
- `engine/` — Core 5-layer pipeline implementation
- `engine_supervisor.py` — Process supervisor for daemon mode
- `data/` — Runtime data (sensor state, synapse matrix, memory)
- `js_bridge/` — JavaScript bridge for OpenClaw integration
## License
AGPL-3.0 (commercial licensing available — contact zmliu0208@gmail.com)
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!