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):

Hardware — Collect psychometrics
- CliftonStrengths (Top 5-10)
- VIA Character Strengths (Top 5-10)
- Communication samples (emails, Slack) for linguistic fingerprint
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)
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:

Question Battery — Present scenarios with multiple-choice responses
Simulant Prediction — Proxy predicts principal's choice with reasoning
Ground Truth — Principal answers independently
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:

Agent Rules Block — Define must_reject, must_protect, should_prefer
Conductor Registration — Register proxy with central Conductor
Integration Points — Connect to calendar, email, task systems
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.

silicon-doppelganger