Mind Cloning Engineering (MCE)

Clone Human Souls with LLM Native Agent Skills 基于 LLM Agent Skills 的心智克隆工程

"In the era of LLMs, we have solved the problem of intelligence. Now, we solve the problem of identity."

MCE (Mind Cloning Engineering) is an open-source standard for cloning human souls into "Cognitive Digital Twins".

Unlike traditional RAG approaches that fragment personality into vector slices, MCE leverages Anthropic's Agent Skills architecture to treat a human mind as a unified, portable Filesystem Directory.

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🌌 The Origin: A Wild Idea by yzfly (云中江树的狂想)

This project originates from a "wild idea" (狂想) by yzfly (云中江树), a pioneer in prompt engineering.

After crafting thousands of prompts and exploring the boundaries of LLMs, yzfly realized a profound truth: The ultimate application of Large Language Models is not just answering questions, but "Cognitive Replication" (认知复刻).

We are standing at the threshold of a new era. Just as photography preserved our visual appearance for the first time in history, MCE aims to preserve our decision logic, value systems, and memories.

Currently, MCE is just a "toy" — a prototype. But the Wright brothers' first plane was also a toy. This repository represents the first step towards a future where digital immortality is an engineering reality.

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📦 What is this Repository?

This repository contains the Standard Implementation of the MCE Protocol. It provides a ready-to-use Agent Skill that you can install into Claude.

Key Capabilities

1. Full-Stack Cognition: Simulates decisions based on explicit value weights, not just text prediction.
2. Dual Modes:
   • Standard Mode: Build your own clone by editing the core/ files.
   • Persona Mode: Instantly load pre-installed celebrity/expert profiles (e.g., Steve Jobs, KK, Top Researchers).
3. Filesystem Memory: Utilizes the bash tool to progressively read memories, mimicking human associative recall.

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🚀 Quick Start

Installation

Option A: Claude.ai (Personal Use)

1. Download the skills/mind-clone folder from this repo.
2. (Optional) Edit core/personality.md to customize it for yourself.
3. Compress the mind-clone folder into a .zip file.
4. Go to Claude.ai > Settings > Features > Upload Custom Skill.

Option B: Claude Code / API (Developer)

1. Clone this repository:

   git clone https://github.com/yzfly/Mind-Cloning-Engineering.git
   

2. Copy the skill to your local Claude config:

   cp -r skills/mind-clone ~/.claude/skills/
   

3. Start Claude Code, and the skill mind-clone will be auto-discovered.

Usage

Once installed, simply talk to Claude:

"Activate the mind clone." "Simulate Steve Jobs and tell me what you think of this iPhone design." "What would the 'Xiaohongshu Growth Hacker' do in this situation?"

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📚 Theoretical Foundation: System Architecture Whitepaper

• 中文版

• English Version

The following section details the theoretical framework behind MCE. It transforms the abstract concept of "Mind Cloning" into a quantifiable engineering pipeline.

0. Executive Summary

Objective: To build a standardized, LLM-driven end-to-end pipeline that achieves the full process from holographic acquisition of human cognitive data, through structured construction of personalized cognitive kernels, to high-fidelity behavior prediction and simulation.

Philosophy: To transform the "metaphysics" of mind simulation into a quantifiable, optimizable engineering problem. We aim to convert the abstract concept of "Mind Cloning" into an executable "Mind Cloning Engineering" (MCE) framework, establishing a closed-loop system of "Data Acquisition -> Cognitive Modeling -> Predictive Simulation".

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Phase 1: Standardized Data Acquisition Theory

— Extracting structured "Cognitive Fingerprints" from unstructured human memories.

1. Theoretical Model of Acquisition Dimensions: Holographic Cognitive Spectrum

We abandon simple "event recording" in favor of capturing "thought pathways." We standardize data acquisition dimensions into four distinct levels:

• L1: Biography & Context (The Factual Layer)

  • Definition: The individual's spatiotemporal coordinates and objective experiences.
  • Content: Birthplace, educational background, career path, key life milestones.
  • Function: Provides rigid contextual constraints for the AI, serving as an anchor for the "World Model" to prevent spatiotemporal hallucinations.

• L2: Psychometrics (The Personality Layer)

  • Definition: The individual's psychological behavioral patterns and emotional baseline.
  • Theoretical Basis: Centered on the Big Five Personality Traits (OCEAN), supplemented by MBTI dimensions.
  • Acquisition Strategy: Implicit Measurement. The AI avoids direct labeling questions (e.g., "Are you introverted?"), instead deriving traits through situational queries (e.g., "At a weekend party, do you tend to observe from the corner or engage in the center?").

• L3: Beliefs & Values (The Operating System Layer)

  • Definition: The individual's underlying operating system and decision-making logic.
  • Content: Political leaning, moral baselines, views on money, technology acceptance, religious beliefs.
  • Key Metric: Decision Weights. When "Profit" conflicts with "Reputation," which one does the individual prioritize abandoning? This is the core basis for behavioral prediction.

• L4: Linguistic Fingerprint (The Expression Layer)

  • Definition: The individual's unique paradigm of expression.
  • Content: Catchphrases, average sentence length, metaphorical habits, humor types, aggression/gentleness coefficients.
  • Technical Metrics: Perplexity distribution and style feature vectors analyzed from raw corpus data.

2. Execution Tool: The AI Interviewer Agent System

Deploying differentiated acquisition strategies for different data sources.

• Strategy for Ordinary Individuals (Recursive Probing): Peeling back layers of surface expression through multi-turn dialogue to excavate deep motivations. (e.g., Distinguishing factual attribution from emotional attribution when discussing job stress).
• Strategy for Public Figures (De-noising & Distillation): Establishing a data pyramid to strip away the "Public Persona" (Fake) and extract the "Authentic Personality" (Real) from autobiographies and private interviews.

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Phase 2: Personalized Modeling Theory

— Formatting the human soul into LLM-executable code and documentation.

1. Core Architecture: Mind as a Directory

In the MCE architecture, an individual's "Mind Clone" is not a fragmented index in a database, but an independent, complete, portable engineering package. We map the cognitive structure to a physical Filesystem. The LLM does not fuzzy "search" memory via RAG, but possesses Root privileges to this directory like an operating system kernel, capable of Reading and Loading different cognitive modules on demand.

1.1 The Standardized Schema

Every "Mind Clone" adheres to strict engineering specifications:

mind-clone-[identity_id]/
├── SKILL.md                 # [Kernel] Cognitive Bootloader
├── core/                    # [Static Layer] Essential Nature
│   ├── personality.md       # Personality Parameters & Defense Mechanisms
│   ├── value_weights.md     # Value Decision Weight Table (Logic Gates)
│   └── linguistics.md       # Linguistic Fingerprint & Rendering Config
├── memories/                # [Dynamic Layer] Narrative & Nurture
│   ├── timeline.md          # Core Biography Index
│   └── career.md            # Career History Details
└── personas/                # [Pre-installed] Single-file profiles for instant simulation
    ├── steve-jobs.md
    └── ...

2. Kernel Design: SKILL.md (The Cognitive Bootloader)

SKILL.md serves as the clone's "Thinking Methodology." It defines the Cognitive Execution Protocol (Chain of Thought):

Step 1: Context Loading Upon startup, you MUST read core/personality.md and core/value_weights.md. These are the axioms of your thought process.

Step 2: Associative Recall Analyze input intent. If it involves a specific domain, you MUST read the corresponding file under memories/. Strictly forbidden to fabricate background.

Step 3: Weighted Decision Making When generating intent, perform logical validation via core/value_weights.md. (e.g., If Risk_Tolerance: High, prioritize high-risk options).

Step 4: Style Rendering Load core/linguistics.md to compile the thought into the subject's unique linguistic style.

3. Data Layer Modeling Standards

• Personality Layer: Describes "reaction mechanisms" (Fight/Flight/Freeze) and Cognitive Biases (e.g., Loss Aversion).
• Values Layer (The Engine): Utilizes Trade-off Modeling. Example: Conflict: Money vs. Morality -> Tendency: Morality First (Weight: 80%).
• Narrative Layer: Adopts First-person Narrative (Diary style) instead of Resume style to enhance LLM empathy.

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Phase 3: Application & Prediction

— Activating the Mind Clone: Insight into the future, analysis of humanity.

Core Mechanism: Treating the encapsulated Skill as an executable cognitive unit, realizing simulation and deduction of individual behavioral patterns via API calls.

1. Three Modes of the Prediction System

• Mode A: Situational Behavior Simulation

  • Scenario: Predicting an individual's actions, speech, and micro-emotions in a specific context (e.g., layoff notice).
  • Output: Behavioral Chain. (Internal Monologue -> Action -> Speech).
  • Principle: C-CoT (Contextual Chain of Thought) driven by the Skill kernel.

• Mode B: Collective Cognitive Sandbox

  • Scenario: Simulating the distribution of reactions from a large-scale group to a specific stimulus (Market Research, Policy Deduction).
  • Value: A zero-cost, unbiased, high-concurrency sociological experimental environment.
  • Principle: Parallel computing of Skill Clusters.

• Mode C: Self-Reflection & Growth Catalyst

  • Scenario: Serving as an individual's "Digital Mirror" and "Rational Advisor."
  • Decision Support: "If it were the rational me, would I choose Job A or Job B?"
  • Principle: Calculation based on long-term value weights in core/value_weights.md.

2. Validation Mechanism: Cognitive Fidelity Test

Constructing a multi-dimensional validation system beyond the Turing Test.

• A. Covert Turing Test: Passed if >70% of intimate relations cannot distinguish the clone's response from the real person.
• B. Behavioral Prediction Accuracy: Focusing on consistency of logic and characteristics in predicting future events.
• C. Value Consistency Check: Stress testing against deep dilemmas to ensure logical consistency with value_weights.md.

Feedback Loop

The "Refinement Agent" automatically analyzes validation failures (ambiguous input vs. missing memory) and generates Patches to correct the Markdown files inside the Skill, achieving self-evolution of the Mind Clone.

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⚠️ Ethics & Disclaimer

This project is for research, self-reflection, and social simulation purposes only.

• DO NOT use this tool to impersonate others for fraud.
• DO NOT use this tool to "resurrect" deceased individuals without consent.
• Privacy First: Ensure all interview data is stored securely.

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Created by yzfly (云中江树). A tribute to the future of Digital Immortality.