server.py                 MCP server (core)
run_experiment.py         Experiment runner (Claude Agent SDK)
analyze_results.py        Compute per-persona metrics (exact match, within-1, MAE)
significance_analysis.py  Paired significance tests across phases
eval_items.json           22 held-out ANES 2024 evaluation items with ground truth

personas/                 50 ANES 2024 personas (stratified by party ID x region)
  anes_001.json ... anes_050.json

rules/                    Rule templates (researcher-specified)
  survey_respondent.txt   Full framework rule (identity anchor only)
  baseline_static.txt     Static backstory baseline (Argyle et al. style)
  rules_only.txt          Ablation: rules without Skills or MCP

skills/                   Skill templates (model-selected per question)
  factual_recall.txt      Personal circumstances and estimation
  direct_attitude.txt     Single-topic policy/social attitudes
  attitude_construction.txt  Complex cross-domain attitude formation

scripts/                  Data processing
  download_anes.py        Download ANES 2024 data
  generate_personas.py    Generate persona JSON files from ANES

results/                  Pilot experiment results (N=10 per phase)
  phase0_phase1_n10_seed2024.json   Phases 0-1
  phase2_n10_seed2024.json          Phase 2
  phase3_n10_seed2024.json          Phase 3

pip install -r requirements.txt

# stdio transport (for Cursor, Claude Desktop)
python server.py

# SSE transport (for web clients)
python server.py --transport sse

# Restrict modules for phase experiments
python server.py --allowed-modules demographics life_narrative politics economy health social_context local_context

{
  "mcpServers": {
    "silicon-sampling": {
      "command": "python",
      "args": ["/absolute/path/to/server.py"]
    }
  }
}

{
  "mcpServers": {
    "silicon-sampling": {
      "command": "python",
      "args": ["/absolute/path/to/server.py"]
    }
  }
}

# Run experiment (requires Claude Agent SDK with Max subscription)
python run_experiment.py --phases 0 1 --n-personas 10 --conditions baseline_static full_framework --seed 2024
python run_experiment.py --phases 2 --n-personas 10 --conditions baseline_static full_framework --seed 2024
python run_experiment.py --phases 3 --n-personas 10 --conditions baseline_static full_framework --seed 2024

# Analyze results
python analyze_results.py results/phase0_phase1_n10_seed2024.json results/phase2_n10_seed2024.json results/phase3_n10_seed2024.json

# Significance tests
python significance_analysis.py

1. Model reads Rule (survey_respondent.txt): receives persona_id and response constraints only
2. Model receives survey question: "How often can you trust the federal government?"
3. Model calls get_survey_skill("attitude_construction", "trust in federal government")
   -> Receives instructions to retrieve politics + economy + values_personality + demographics
4. Model calls get_persona_modules("anes_010", ["politics", "economy", "values_personality", "demographics"], "trust in federal government")
   -> Receives only those 4 modules (~80 fields), not all 170
5. Model synthesizes a response grounded in the retrieved information