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GameMechanicsDesignTask

Generate comprehensive game mechanics with balance analysis, progression systems, and economy design. Proves viability through simulated playtesting predictions.

Side-Effect Safe Model: GPT-4 Preferred Category: Games

⚙️ ExecutionConfig.json

{
  "game_concept": "Tower defense with resource management",
  "target_audience": "hardcore",
  "core_loop_duration": "20 minutes",
  "num_mechanics": 6,
  "balance_focus": "strategy",
  "include_progression_system": true,
  "include_economy_system": true,
  "playtesting_scenarios": 3
}

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👁️ SessionTask UI

Overview

Core Mechanics

Balance Analysis

Playtesting

Balance Metrics

Win Rate Variance 0.12 (Good)

Strategy Diversity 0.85 (Excellent)

Skill Expression 78/100

Luck Factor 15/100

Dominant Strategies

Early-game resource hoarding (Mitigated by Sink Mechanics)
High-density tower clusters (Countered by Splash Damage)

Test Workspace Browser

Explore actual design artifacts, balance reports, and simulation logs generated by this task.

Configuration Parameters

Field	Type	Default	Description
`game_concept` *	String	-	High-level game concept (e.g., 'Tower defense with resource management').
`target_audience`	String	"casual"	'casual', 'hardcore', 'family', 'competitive'.
`core_loop_duration`	String	"15 minutes"	Target duration for the core gameplay loop.
`num_mechanics`	Int	5	Number of core mechanics to design (3-8 recommended).
`include_progression_system`	Boolean	true	Whether to design levels, XP curves, and unlocks.
`include_economy_system`	Boolean	true	Whether to include detailed resource flow and sink design.
`include_difficulty_scaling`	Boolean	true	Whether to include difficulty scaling analysis.
`balance_focus`	String	"mixed"	'skill', 'luck', 'strategy', 'mixed'.
`playtesting_scenarios`	Int	3	Number of simulated playtesting scenarios to run (1-10).
`generate_tuning_guide`	Boolean	true	Whether to generate specific difficulty and reward parameters.
`input_files`	List<String>	[]	Optional glob patterns for context files (docs, references).

Token Usage

Estimated consumption: Medium-High. This task involves multiple LLM passes for generation, interaction analysis, and simulation.

Task Process Lifecycle

Context Gathering: Reads input files and prior task results to align with existing lore or technical constraints.
Mechanic Generation: Designs core loops, player actions, and system responses based on the concept and audience.
Interaction Analysis: Maps synergies and conflicts between mechanics to identify balance risks.
Progression Design: (Optional) Constructs XP curves, level unlocks, and difficulty multipliers.
Economy Design: (Optional) Defines resource types, generation rates, and sink mechanisms.
Balance Evaluation: Calculates skill/luck ratios and identifies dominant strategies.
Playtesting Simulation: Predicts engagement curves and frustration triggers for different player personas.
Tuning Generation: (Optional) Produces actionable difficulty and reward multipliers.

Embedded Execution (Headless)

Use the UnifiedHarness to run this task as part of a CI/CD pipeline or automated game design tool.

// 1. Initialize the Harness
val harness = UnifiedHarness(serverless = true, openBrowser = false)

// 2. Configure the Task
val executionConfig = GameMechanicsDesignTaskExecutionConfigData(
    game_concept = "Cyberpunk stealth-action with hacking",
    target_audience = "hardcore",
    balance_focus = "skill",
    playtesting_scenarios = 5
)

// 3. Run the Task
harness.runTask(
    taskType = GameMechanicsDesignTask.GameMechanicsDesign,
    executionConfig = executionConfig,
    workspace = File("./game-design-output"),
    autoFix = true
)

Test Case Example

Example of a unit test verifying game balance via the agent.

@Test
fun testGameBalance() {
    val result = harness.runTask(
        taskType = GameMechanicsDesignTask.GameMechanicsDesign,
        executionConfig = GameMechanicsDesignTaskExecutionConfigData(
            game_concept = "Simple Match-3",
            balance_focus = "luck"
        )
    )
    assertTrue(result.contains("Luck Factor"))
    assertTrue(result.contains("Win Rate Variance"))
}

Prompt Segment

The following logic is injected into the LLM context:

GameMechanicsDesign - Generate comprehensive game mechanics with balance analysis
  ** Specify the game concept (e.g., "Tower defense with resource management")
  ** Define target audience (casual, hardcore, family, competitive)
  ** Set core gameplay loop duration
  ** Configure number of mechanics to design (3-8)
  ** Choose balance focus (skill, luck, strategy, mixed)
  ** The task will:
     - Generate core gameplay mechanics
     - Analyze mechanic interactions
     - Design progression systems
     - Create economy systems
     - Evaluate balance and fairness
     - Simulate playtesting scenarios
     - Provide tuning parameters