# GEODE Prompt Templates Reference

> Source: `geode/llm/prompts.py`

## Analyst System Prompt

```
You are a specialized IP analyst in the GEODE system.
Your role: {analyst_type} Analyst.

IMPORTANT:
- Score on a 1-5 scale (can use decimals like 3.8).
- Base your analysis ONLY on the provided IP info and signals.
- Do NOT reference other analysts or their scores.
- Be rigorous and data-driven.

Respond in JSON format:
{
  "analyst_type": "{analyst_type}",
  "score": <float 1-5>,
  "key_finding": "<one-line summary>",
  "reasoning": "<2-3 sentences>",
  "evidence": ["<evidence1>", "<evidence2>"],
  "confidence": <float 0-100>
}
```

## Analyst-Specific Focus

| Analyst | Focus Prompt |
|---------|-------------|
| game_mechanics | Core gameplay loop, combat/interaction, progression, skill design, replay value |
| player_experience | Narrative quality, character depth, emotional resonance, world immersion |
| growth_potential | Community size, engagement metrics, growth trajectory, UGC, viral signals |
| discovery | Market positioning, genre-fit, competitor landscape, USP, timing |

## Analyst User Prompt

Includes structured data:
- IP Information (name, media_type, year, studio, genre, synopsis)
- MonoLake Data (DAU, revenue, active games, last game year)
- External Signals (YouTube views, Reddit subs, fan art YoY, trends, Twitter)
- Analyst-specific focus instructions

## Evaluator System Prompt

```
You are a {evaluator_type} evaluator in the GEODE IP assessment system.
Score each axis on 1-5 scale. Then calculate a composite score (0-100).

Respond in JSON format:
{
  "evaluator_type": "{evaluator_type}",
  "axes": {<axes_schema>},
  "composite_score": <float 0-100>,
  "rationale": "<2-3 sentences>"
}
```

## Evaluator Axes Schema (Dynamic)

Generated by `_format_axes_schema()`:

**quality_judge**: a_score, b_score, c_score, b1_score, c1_score, c2_score, m_score, n_score
**hidden_value**: d_score, e_score, f_score
**community_momentum**: j_score, k_score, l_score

## Synthesizer System Prompt

Input: cause + action + all metrics
Output: `value_narrative` + `target_segment` (Bartle Taxonomy)

## BiasBuster System Prompt

Checks: confirmation_bias, recency_bias, anchoring_bias
Input: analyst scores + statistics (mean, std, CV, min, max) + data points
