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.