Planning Poker

Purpose

Conducts story point estimation using multiple developer personas with Planning Poker methodology

When to Use This Skill

1. Sprint Planning - Estimate user stories
2. Feature Sizing - Assess complexity
3. Risk Assessment - Identify high-risk items
4. Team Calibration - Align estimation standards

Responsibilities

1. Facilitate Planning - Poker sessions with multiple agents
2. Collect votes - in parallel (3x faster with ThreadPoolExecutor)
3. Build consensus - through discussion rounds
4. Assess risk - based on estimates and confidence
5. Generate detailed - estimation reports

Integration with Pipeline

Communication

Receives:

• User stories from Sprint Planning stage
• Developer agent personas (conservative, aggressive)
• Team velocity from previous sprints

Sends:

• Story point estimates with confidence
• Risk assessment (low/medium/high)
• Voting history and discussion notes

Usage Examples

Standalone Usage

python3 planning_poker.py \
  --story-title "User Authentication" \
  --story-description "Implement JWT-based authentication" \
  --team-velocity 15

Programmatic Usage

from planning_poker import PlanningPoker

poker = PlanningPoker(
    agents=developer_agents,
    llm_client=llm_client,
    team_velocity=15
)

result = poker.estimate_story(
    title="User Authentication",
    description="Implement JWT authentication",
    acceptance_criteria=["Login works", "Logout works"]
)

print(f"Estimate: {result['estimate']} points")
print(f"Confidence: {result['confidence']}")
print(f"Risk: {result['risk_level']}")

Configuration

Environment Variables

# Agent-specific configuration
ARTEMIS_PLANNING_POKER_ENABLED=true
ARTEMIS_LLM_PROVIDER=openai
ARTEMIS_LLM_MODEL=gpt-4o

Hydra Configuration (if applicable)

planning_poker:
  enabled: true
  llm:
    provider: openai
    model: gpt-4o

Best Practices

1. Use Multiple Personas - At least 2-3 developers for diverse perspectives
2. Set Team Velocity - Provides context for estimation
3. Enable Parallelization - Use ThreadPoolExecutor for 3x speedup
4. Discussion Rounds - Allow 2-3 rounds for consensus
5. Risk Assessment - Use confidence scores to identify risky estimates

Cost Considerations

Typical cost: $0.05-0.20 per operation depending on complexity

Limitations

• Depends on LLM quality
• Context window limits
• May require multiple iterations

References

• Artemis Documentation
• Agent Pattern

Version: 1.0.0

Maintained By: Artemis Pipeline Team

Last Updated: October 24, 2025