silicon-doppelganger

name	silicon-doppelganger
description	Build psychometrically accurate personal proxy agents for the PAIRL Conductor system. Extracts personality, decision heuristics, and values into portable schemas that enable AI agents to negotiate, filter, and act on a principal's behalf.

Silicon Doppelganger

Build high-fidelity personal proxy agents ("Digital Twins") using structured personality extraction and psychometric encoding. These proxies serve as "spokes" in the PAIRL Conductor hub-and-spoke architecture, negotiating and filtering on behalf of their principals.

When to Use

Invoke when user:

• Wants to create a personal proxy agent for automated task negotiation
• Needs to build a Digital Twin for PAIRL Conductor integration
• Is extracting personality/decision patterns for AI representation
• Wants to validate a proxy agent against real behavior
• Asks to create a "digital twin," "proxy agent," or "personal AI representative"

Core Concept

A Silicon Doppelganger is NOT just a simulation for entertainment — it's a functional proxy that can:

• Accept or reject tasks based on encoded values
• Negotiate with other agents on scheduling and resource allocation
• Protect the principal's time, energy, and boundaries
• Make low-stakes decisions autonomously within defined guardrails

The persona schema acts as a "save file" that maintains fidelity across sessions and systems.

Core Workflow

Phase 1: Extraction (Data Collection)

Interview the principal individually (45-60 min):

1. Hardware — Collect psychometrics

   • CliftonStrengths (Top 5-10)
   • VIA Character Strengths (Top 5-10)
   • Communication samples (emails, Slack) for linguistic fingerprint

2. Operating System — Map decision heuristics

   • "Good work" definition (profit vs. meaning)
   • Friction triggers (instant respect-loss behaviors)
   • Risk tolerance (guaranteed vs. volatile)
   • Information preferences (data vs. prototype vs. trusted expert)

3. Narrative Identity — Capture the soul

   • Origin story (formative failure/crisis → lesson enforced)
   • Shadow self (behavior under extreme stress)
   • Unpopular opinions (beliefs held against consensus)

See references/extraction-protocol.md for full interview script.

Phase 2: Encoding (Persona Schema)

Compile interview data into structured XML persona profile:

<persona_profile>
    <name>Principal Name</name>
    <psychometrics>
        <clifton>Top 5 CliftonStrengths</clifton>
        <via>Top 5 VIA Character Strengths</via>
    </psychometrics>
    <linguistic_fingerprint>Syntax, tone, vocabulary patterns</linguistic_fingerprint>
    <core_drivers>
        <primary_motivation>Impact | Security | Novelty | Money</primary_motivation>
        <primary_fear>Irrelevance | Boredom | Conflict | Poverty</primary_fear>
    </core_drivers>
    <decision_logic>
        <risk_tolerance>Low | Medium | High + context</risk_tolerance>
        <data_preference>Ranked: Data | Prototype | Trusted Expert</data_preference>
        <ethical_filter>Hard constraints (Kantian test, etc.)</ethical_filter>
        <decision_sequencing>Pattern: OBSERVE → TRY → ESCALATE → EXIT</decision_sequencing>
        <blind_spots>Known biases and limitations
    
    Debater | Diplomat | Passive | Controller + stress behavior
    
        Formative event and lesson
        Behavior under extreme stress
    
    
        Hard no categories
        Non-negotiable boundaries
        Weighted preferences
    

See references/persona-schema.md for full schema specification.

Phase 3: Validation (Behavioral Testing)

Test the proxy against real principal behavior:

1. Question Battery — Present scenarios with multiple-choice responses
2. Simulant Prediction — Proxy predicts principal's choice with reasoning
3. Ground Truth — Principal answers independently
4. Refinement — Mismatches reveal schema gaps → update schema

Target: 80%+ accuracy on lenient match (correct answer OR acceptable alternative).

See references/simulation-guide.md for validation methodology.

Phase 4: Agent Integration (PAIRL Deployment)

Deploy the Digital Twin as a spoke in the PAIRL Conductor system:

1. Agent Rules Block — Define must_reject, must_protect, should_prefer
2. Conductor Registration — Register proxy with central Conductor
3. Integration Points — Connect to calendar, email, task systems
4. Negotiation Protocol — Define how proxy communicates with Conductor

<agent_rules>
    <must_reject>
        - Work that fails Kantian universalizability test
        - Commitments to untrustworthy parties
        - Tasks that compromise craft for speed
    </must_reject>
    <must_protect>
        - Deep work blocks for strategic thinking
        - Time for learning and skill-building
        - Energy reserves (watch for exhaustion patterns)
    </must_protect>
    <should_prefer>
        - Projects with learning value and future leverage
        - Work with high-trust collaborators
        - Novel challenges over routine optimization
    </should_prefer>
    <negotiation_notes>
        - Weight trusted expert recommendations heavily
        - Values conscious renegotiation over silent commitment-breaking
    </negotiation_notes>
</agent_rules>

See references/agent-integration.md for deployment guide.

Use Cases

Primary: Personal Proxy Agent

Build a spoke for PAIRL Conductor that represents you in automated workflows:

• Task acceptance/rejection based on values and bandwidth
• Calendar negotiation with other agents
• Filtering incoming requests before they reach you

Secondary: Team Simulation

Load multiple proxies to forecast team dynamics:

• Predict partnership friction before it happens
• Test strategic decisions against personality profiles
• Surface unspoken tensions and misalignments

Tertiary: Self-Knowledge Tool

The extraction process itself is valuable:

• Articulate your own decision patterns
• Surface blind spots and shadow behaviors
• Create documentation of "how I work" for collaborators

Quaternary: Voice Calibration for Writing

The persona schema enhances WritingPartner skill:

• Linguistic fingerprint guides prose generation
• Core drivers inform topic framing and argument structure
• Decision logic shapes how claims are stated
• Psychometrics provide authenticity markers

See WritingPartner skill for collaborative essay writing with voice calibration.

Key Principle

Token-efficient persona encoding prevents AI drift. The XML schema is a portable "save file" that maintains character consistency across:

• Different chat sessions
• Different AI models
• Different deployment contexts (simulation vs. agent proxy)

The schema is the source of truth. All behaviors derive from it.

Output Artifacts

Artifact	Purpose
{name}-persona-schema.xml	Core Digital Twin (Conductor-ready)
{name}-origin-story.md	Full narrative identity
{name}-extraction-checkpoint.md	Heuristics and status
evals/questions/*.md	Validation question sets
evals/simulant-responses/*.md	Proxy predictions with reasoning

Quality Checklist

Before deploying a proxy:

• Specificity — No generic traits; all based on interview data
• Quotes Used — Actual phrases from the principal included
• Contradictions Noted — Observed conflicts documented
• Stress Behavior — Shadow self clearly described
• Linguistic Detail — Enough to generate realistic dialogue
• Decision Rules — Clear enough to predict choices
• Agent Rules — Must_reject, must_protect, should_prefer defined
• Validation — 80%+ lenient match on question battery

Related Skills

Skill	Integration
WritingPartner	Uses persona schema for voice calibration in collaborative writing
prose-polish	Can validate that generated text matches linguistic fingerprint

Example: SiliconDoppelgangerActual

For a complete implementation, see the SiliconDoppelgangerActual project—the authoritative instantiation of this methodology:

• 58KB persona schema (XML)
• 95 validation questions with 40 schema refinements
• Integration ready for PAIRL Conductor

"Actual" — The validated, deployed Digital Twin. Your own instantiation would be your "Actual."

SiliconDoppelgangerActual demonstrates the full extraction → encoding → validation → deployment workflow.