Prompt Variables Reference

Version: 0.1.0

Template variables in prompt files use the {{VARIABLE_NAME}} syntax. The agent replaces them with project-specific values at runtime before sending to the LLM.

All Variables

Variable	Used In	Replaced With	Type
{{PROFILE}}	base_context.md	JSON-encoded project profile	JSON string
{{PREVIOUS_CONTEXT}}	base_context.md	Previous discoveries + user feedback	Multi-line text
{{SCHEMA_INFO}}	exploration.md	Warehouse table schemas	JSON string
{{DATASET}}	exploration.md, analysis_*.md	Dataset/schema names	Comma-separated string
{{FILTER}}	exploration.md	SQL WHERE clause	SQL fragment
{{FILTER_CONTEXT}}	exploration.md	Human-readable filter description	Text
{{FILTER_RULE}}	exploration.md	SQL construction rule for the filter	Text
{{ANALYSIS_AREAS}}	exploration.md	List of analysis areas with descriptions	Multi-line text
{{TOTAL_QUERIES}}	analysis_*.md	Count of relevant exploration queries	Integer
{{QUERY_RESULTS}}	analysis_*.md	Exploration query results for this area	JSON array
{{DISCOVERY_DATE}}	recommendations.md	Current date (ISO format)	Date string
{{INSIGHTS_SUMMARY}}	recommendations.md	Text summary of insight counts	Text
{{INSIGHTS_DATA}}	recommendations.md	Full insight array with IDs	JSON array

Detailed Reference

{{PROFILE}}

Source: project.profile from MongoDB

The project profile serialized as JSON. Contains domain-specific fields defined by the domain pack's profile schema.

Example value:

{
  "basic_info": {
    "genre": "puzzle",
    "sub_genre": "match-3",
    "platforms": ["iOS", "Android"],
    "target_audience": "Adult Female 30-65+"
  },
  "gameplay": {
    "core_mechanic": "match3",
    "game_type": "level_based",
    "session_type": "short",
    "avg_session_duration_minutes": 6
  },
  "monetization": {
    "model": "freemium",
    "has_ads": true,
    "has_iap": true
  },
  "boosters": [
    {"name": "Magnet", "usage": "consumable", "starting_amount": 3, "can_purchase": true}
  ],
  "kpis": {
    "retention_d1_target": 40,
    "arpu_target": 0.50
  }
}

If profile is empty: Shows "No project profile configured. Provide general analysis.".

{{PREVIOUS_CONTEXT}}

Source: Last 5 discoveries + all feedback for the project

Built by the agent's buildPreviousContext() function. Contains:

1. Discovery count and date: "This is discovery run #5. Last discovery: 2026-03-12."
2. Previous insights: Names, areas, severity, dates — with dedup instruction
3. Disliked insights: With user comments — "AVOID similar conclusions"
4. Liked insights: "MONITOR for changes"
5. Previous recommendations: "Don't repeat unless changed"

Example value:

This is discovery run #5. Last discovery: 2026-03-12.

### Previously Found Insights
These insights were already discovered. Do NOT repeat them unless the data has significantly changed.

- **Day 0-to-Day 1 Drop: 67% Never Return** [churn, critical] — 8298 affected (2026-03-11)
- **Level 11 Difficulty Cliff** [churn, high] — 642 affected (2026-03-12)

### User Feedback — Disliked Insights (AVOID)
- **Low engagement pattern** — user comment: "not relevant to our game"

### User Feedback — Liked Insights (MONITOR)
- **Day 0-to-Day 1 Drop: 67% Never Return**

### Previously Given Recommendations
Don't repeat these unless the situation has changed.
- P1: Reduce Level 11-15 Difficulty by 25% (difficulty)

If first run: Empty string (no previous context).

{{SCHEMA_INFO}}

Source: Warehouse schema discovery (Phase 3)

JSON-encoded table schemas discovered from the warehouse.

Example value:

{
  "tables": [
    {
      "name": "analytics_data.users",
      "columns": [
        {"name": "user_id", "type": "STRING", "nullable": false},
        {"name": "created_at", "type": "TIMESTAMP", "nullable": false},
        {"name": "country", "type": "STRING", "nullable": true}
      ],
      "row_count": 50000
    },
    {
      "name": "analytics_data.sessions",
      "columns": [
        {"name": "session_id", "type": "STRING", "nullable": false},
        {"name": "user_id", "type": "STRING", "nullable": false},
        {"name": "duration_seconds", "type": "INT64", "nullable": true}
      ],
      "row_count": 500000
    }
  ]
}

{{DATASET}}

Source: project.warehouse.datasets array

Comma-separated dataset names (BigQuery) or schema names (Redshift).

Example values:

• BigQuery: "analytics_data, features_prod"
• Redshift: "public"

{{FILTER}}

Source: project.warehouse.filter_field and filter_value

SQL WHERE clause for multi-tenant data filtering.

Example value: "WHERE app_id = '68a42f378e3b227c8e41b0e5'"

If no filter configured: Empty string.

{{FILTER_CONTEXT}}

Source: Same as {{FILTER}}

Human-readable explanation of the filter for the AI.

Example value: "Data is filtered to app_id='68a42f378e3b227c8e41b0e5'. Always include this filter in your queries."

{{FILTER_RULE}}

Source: Same as {{FILTER}}

SQL construction rule.

Example value: "Always include: WHERE app_id = '68a42f378e3b227c8e41b0e5' in all queries."

{{ANALYSIS_AREAS}}

Source: Domain pack analysis areas

Formatted list of all analysis areas the agent should explore.

Example value:

- Churn Risks: Players at risk of leaving the game
- Engagement Patterns: Player behavior and session trends
- Monetization Opportunities: Revenue optimization and conversion opportunities
- Level Difficulty: Difficulty spikes and frustration points
- Booster Usage: Power-up usage patterns and purchase opportunities

{{TOTAL_QUERIES}}

Source: Count of exploration queries relevant to this analysis area

Example value: "6"

{{QUERY_RESULTS}}

Source: Exploration results filtered by area keywords

JSON array of exploration steps relevant to the current analysis area. Each entry includes:

Example value:

[
  {
    "step": 1,
    "timestamp": "2026-03-14T10:30:05Z",
    "action": "query_data",
    "thinking": "Let me check retention rates by cohort...",
    "query": "SELECT cohort_date, day_1_retention FROM retention_cohorts WHERE app_id = '...' ORDER BY cohort_date DESC LIMIT 30",
    "query_result": [
      {"cohort_date": "2026-03-01", "day_1_retention": 33.2},
      {"cohort_date": "2026-02-28", "day_1_retention": 31.8}
    ],
    "row_count": 30,
    "execution_time_ms": 450
  },
  {
    "step": 3,
    "thinking": "Retention is declining. Let me look at session patterns...",
    "query": "SELECT user_id, total_sessions, days_active FROM ...",
    "row_count": 100
  }
]

{{DISCOVERY_DATE}}

Source: Current date

Example value: "2026-03-14"

{{INSIGHTS_SUMMARY}}

Source: All insights from the analysis phase

Text summary with counts per area.

Example value: "Total: 7 insights (churn: 3, engagement: 2, monetization: 2)"

{{INSIGHTS_DATA}}

Source: All validated insights from the analysis phase

Full JSON array of all insights, including their IDs. The LLM uses insight IDs to populate related_insight_ids on recommendations.

Example value:

[
  {
    "id": "churn-1",
    "analysis_area": "churn",
    "name": "Day 0-to-Day 1 Drop: 67% Never Return",
    "description": "67% of new players...",
    "severity": "critical",
    "affected_count": 8298,
    "risk_score": 0.67,
    "confidence": 0.85,
    "metrics": {"churn_rate": 0.67, "avg_sessions_before_churn": 1.2},
    "indicators": ["Only 33% return after Day 1", "Avg session: 4.2 minutes"],
    "source_steps": [1, 3, 5]
  }
]

Variable Substitution Code

Variables are substituted in services/agent/internal/discovery/orchestrator.go using strings.ReplaceAll(). The substitution happens at runtime, just before sending prompts to the LLM.

Next Steps

• Prompts Concept — How prompts are assembled and overridden
• Customizing Prompts — Edit prompts and add custom areas
• Creating Domain Packs — Write prompts for a new domain