Persona Generation Pipeline

Generate complete AI agent personas with realistic backstories, demographics, and OCEAN personality traits for the TraitorSim game. This skill orchestrates the 5-stage pipeline that uses Gemini Deep Research for demographic grounding and Claude Opus for narrative synthesis.

Quick Start

# Generate 15 personas (estimated cost: $6-7)
./scripts/generate_persona_library.sh --count 15

# Or run stages individually:
python scripts/generate_skeleton_personas.py --count 15
python scripts/batch_deep_research.py --input data/personas/skeletons/test_batch_001.json
python scripts/poll_research_jobs.py --jobs data/personas/jobs/test_batch_001_jobs.json
python scripts/synthesize_backstories.py --reports data/personas/reports/test_batch_001_reports.json
python scripts/validate_personas.py --library data/personas/library/test_batch_001_personas.json

Pipeline Overview

The persona generation pipeline consists of 5 stages:

Stage 1: Skeleton Generation

• Creates demographic skeletons with archetype assignments
• Samples OCEAN traits from archetype ranges
• Ensures archetype diversity (max 2 per archetype for small batches)
• Output: data/personas/skeletons/*.json

Stage 2: Deep Research Submission

• Submits background research jobs to Gemini Deep Research agent
• Researches real-world UK demographics for occupation/location
• ~10-20 minutes per job (runs in background)
• Quota-limited: Submit in waves (6→4→2 pattern typical)
• Output: data/personas/jobs/*_jobs.json (job IDs)

Stage 3: Research Job Polling

• Polls Deep Research jobs until completion
• Status: processing → in_progress → completed
• Retrieves completed reports (20-30KB each)
• Output: data/personas/reports/*_reports.json

Stage 4: Backstory Synthesis

• Uses Claude Opus 4.5 to generate narrative backstories
• Applies World Bible constraints (in-universe brands)
• Batch synthesis: single query generates multiple personas
• Incremental pattern: only synthesize NEW personas
• Output: data/personas/library/*_personas.json

Stage 5: Validation

• Validates persona quality and completeness
• Detects forbidden brand leakage (Starbucks, Facebook, etc.)
• Checks OCEAN/stats ranges
• Verifies required fields present
• FAILS FAST if validation errors (no fallback)

Instructions

Full Pipeline Execution

When asked to generate a complete persona library:

1. Determine batch size (recommend 15-20 for testing, 100+ for production)

2. Run the master orchestration script:

   ./scripts/generate_persona_library.sh --count 15
   

3. Monitor quota limits:

   • Deep Research quota follows pattern: 6→4→2→2 jobs per wave
   • If quota errors occur, wait 5-10 minutes and retry
   • See "Quota Management" section below

4. Verify results:

   • Check validation output for 100% pass rate
   • Verify 0 brand leaks detected
   • Confirm archetype distribution is diverse

Incremental Generation (Adding to Existing Library)

When asked to add more personas to an existing library:

1. Load existing personas:

   with open('data/personas/library/test_batch_001_personas.json') as f:
       existing_personas = json.load(f)
   existing_ids = {p['skeleton_id'] for p in existing_personas}
   

2. Generate new skeletons (avoiding duplicate IDs)

3. Submit Deep Research jobs for new skeletons only

4. Synthesize only new personas:

   # Extract only new reports
   python -c "import json; ..."  # Filter to new reports
   
   # Synthesize
   python scripts/synthesize_backstories.py --reports /tmp/new_reports_only.json --output /tmp/new_personas
   
   # Merge
   python -c "import json; ..."  # Combine existing + new
   

5. Validate merged library

Quota Management

Deep Research API has rate limits (specific limits not publicly documented). Use these strategies:

Strategy 1: Wave Submission

# Submit in waves with delays
wave_sizes = [6, 4, 2, 2, 1]  # Observed pattern
for wave_size in wave_sizes:
    jobs = submit_batch(wave_size)
    time.sleep(300)  # 5 min between waves

Strategy 2: Client-Side Tracking

from scripts.quota_tracker import QuotaTracker

tracker = QuotaTracker(rpm_limit=10, rpd_limit=100)
if tracker.can_make_request():
    submit_job()
    tracker.record_request()
else:
    wait_time = tracker.time_until_available()
    time.sleep(wait_time)

Strategy 3: Exponential Backoff

for attempt in range(max_retries):
    try:
        submit_job()
        break
    except QuotaError:
        wait_time = min(2 ** attempt * 60, 3600)  # Max 1 hour
        time.sleep(wait_time)

World Bible Constraints

Ensure personas use in-universe brands (not real-world brands):

Approved In-Universe Brands:

• Highland Spring Co. (water)
• Cairngorm Coffee Roasters (coffee)
• Heather & Thistle Crisps (snacks)
• Loch Provisions (meals)
• Royal Oak Spirits (alcohol)
• Baronial Casual Wear (clothing)
• ScotNet (social media/internet)
• CastleVision (production company)

Forbidden Real-World Brands:

• Starbucks, Costa, Pret (use Cairngorm Coffee)
• Facebook, Instagram, Twitter (use ScotNet)
• Netflix, BBC iPlayer (use CastleVision)
• Evian, Fiji (use Highland Spring)

Validation automatically detects forbidden brands using word boundary regex.

Cost Estimates

Per Persona:

• Deep Research: ~$0.35
• Claude Opus synthesis (batch): ~$0.05
• Total: ~$0.40-$0.45 per persona

Batch Estimates:

• 15 personas: ~$6-$7
• 50 personas: ~$20-$22
• 100 personas: ~$40-$45

Cost Optimization:

• Use batch synthesis (single query for multiple personas)
• Use incremental synthesis (only generate new personas)
• Reuse persona library across multiple game runs (no runtime cost)

Archetype System

13 predefined archetypes with distinct gameplay tendencies:

1. The Prodigy - High intellect, high openness
2. The Charming Sociopath - Low agreeableness, high extraversion
3. The Misguided Survivor - Low openness, high neuroticism
4. The Comedic Psychic - High extraversion, low conscientiousness
5. The Bitter Traitor - Low agreeableness, high neuroticism
6. The Infatuated Faithful - High agreeableness, low intellect
7. The Quirky Outsider - High openness, neurodivergent traits
8. The Incompetent Authority - Facade conscientiousness, low intellect
9. The Zealot - High conscientiousness, low openness
10. The Romantic - High agreeableness, trusting
11. The Smug Player - Low agreeableness, moral superiority
12. The Mischievous Operator - Low conscientiousness, Machiavellian
13. The Charismatic Leader - High social influence, high extraversion

Each archetype has:

• OCEAN trait ranges (e.g., openness: 0.8-0.95)
• Stat biases (intellect, dexterity, social_influence)
• Typical occupations and demographics
• Strategic gameplay profile

Validation Criteria

Personas must pass ALL validation checks:

• ✅ Required fields present (name, backstory, demographics, personality, stats)
• ✅ OCEAN traits in valid ranges (0.0-1.0)
• ✅ Stats in valid ranges (defined by archetype)
• ✅ Backstory length: 200-1600 characters
• ✅ Zero forbidden brand leakage
• ✅ UK-appropriate names and locations
• ✅ Archetype distribution is diverse

Fail Fast: If validation fails, the pipeline stops immediately. No random fallback.

Persona Card Structure

Each persona card contains:

{
  "skeleton_id": "skeleton_010",
  "archetype": "charismatic_leader",
  "archetype_name": "The Charismatic Leader",
  "name": "Gemma Ashworth-Clarke",
  "demographics": {
    "age": 31,
    "occupation": "motivational speaker",
    "location": "London",
    "socioeconomic": "upper-middle",
    "gender": "female"
  },
  "personality": {
    "openness": 0.72,
    "conscientiousness": 0.78,
    "extraversion": 0.91,
    "agreeableness": 0.65,
    "neuroticism": 0.54
  },
  "stats": {
    "intellect": 0.75,
    "dexterity": 0.60,
    "social_influence": 0.88
  },
  "backstory": "First-person narrative (200-300 words)...",
  "key_relationships": ["..."],
  "formative_challenge": "...",
  "political_beliefs": "...",
  "hobbies": ["...", "...", "..."],
  "strategic_approach": "..."
}

Common Issues and Solutions

Issue: Quota errors during Deep Research submission

• Solution: Submit in waves, wait 5-10 min between waves
• See quota management strategies above

Issue: Jobs stuck in "processing" status

• Solution: Keep polling, Deep Research takes 10-20 min per job
• Check for "in_progress" status (not just "completed")

Issue: Brand leakage detected

• Solution: Review synthesis prompts, ensure World Bible constraints clear
• Update src/traitorsim/utils/world_flavor.py with new forbidden brands

Issue: Backstory too short/long

• Solution: Adjust synthesis prompt to specify 200-300 words
• Update validation limits if needed

Issue: Re-synthesizing existing personas (wasting cost)

• Solution: Use incremental synthesis pattern
• Filter reports to only NEW skeleton IDs before synthesis

Advanced Usage

See docs/PERSONA_GENERATION_LESSONS.md for:

• Complete 12-lesson post-mortem from 15-persona test batch
• Detailed cost analysis
• Timeline projections for 100+ persona batches
• Production readiness checklist
• All quota management strategies with code examples

Testing

Test the pipeline with a small batch first:

# Generate 2 test personas
python scripts/generate_skeleton_personas.py --count 2 --output data/personas/skeletons/test_mini.json
python scripts/batch_deep_research.py --input data/personas/skeletons/test_mini.json --output data/personas/jobs/test_mini_jobs.json

# Monitor completion
python scripts/poll_research_jobs.py --jobs data/personas/jobs/test_mini_jobs.json --output data/personas/reports/test_mini_reports.json

# Synthesize
python scripts/synthesize_backstories.py --reports data/personas/reports/test_mini_reports.json --output data/personas/library/test_mini_personas.json --model claude-opus-4-5-20251101

# Validate
python scripts/validate_personas.py --library data/personas/library/test_mini_personas.json

Expected output: 100% validation pass, 0 brand leaks, ~$0.90 cost for 2 personas.

Requirements

Environment Variables:

export GEMINI_API_KEY="..."            # For Deep Research
export CLAUDE_CODE_OAUTH_TOKEN="..."  # For Claude synthesis

Python Dependencies:

pip install google-genai claude-agent-sdk

File Structure:

data/personas/
├── skeletons/       # Stage 1 output
├── jobs/            # Stage 2 output (job IDs)
├── reports/         # Stage 3 output (research reports)
└── library/         # Stage 4 output (final personas)

scripts/
├── generate_skeleton_personas.py
├── batch_deep_research.py
├── poll_research_jobs.py
├── synthesize_backstories.py
└── validate_personas.py

Success Metrics

A successful persona generation run should achieve:

• ✅ 100% validation pass rate
• ✅ 0 forbidden brand leaks
• ✅ Diverse archetype distribution
• ✅ Realistic UK demographics and backstories
• ✅ Cost within estimates (~$0.45/persona)
• ✅ All OCEAN traits within archetype ranges
• ✅ Backstories reference specific locations, occupations, relationships

When to Use This Skill

Use this skill when:

• Creating a new persona library for a game
• Adding personas to an existing library
• Testing archetype designs with real generated personas
• Troubleshooting persona quality issues
• Understanding persona generation costs and timelines
• Converting random personality initialization to grounded personas

When NOT to Use This Skill

Don't use this skill for:

• Real-time persona generation during gameplay (personas are pre-generated offline)
• Modifying existing personas (use archetype-designer skill instead)
• Validating World Bible lore compliance (use world-bible-validator skill)
• Managing API quotas (use quota-manager skill instead)