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</blind_spots>
    </decision_logic>
    <conflict_style>Debater | Diplomat | Passive | Controller + stress behavior</conflict_style>
    <narrative_anchors>
        <origin_story>Formative event and lesson</origin_story>
        <shadow_self>Behavior under extreme stress</shadow_self>
    </narrative_anchors>
    <agent_rules>
        <must_reject>Hard no categories</must_reject>
        <must_protect>Non-negotiable boundaries</must_protect>
        <should_prefer>Weighted preferences</should_prefer>
    </agent_rules>
</persona_profile>

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.