gds-performance-test

name	gds-performance-test
description	Design game performance testing strategy. Use when the user says "performance test" or "benchmark"

Performance Testing Strategy Workflow

Goal: Design a comprehensive performance testing strategy covering frame rate, memory usage, loading times, and platform-specific requirements. Performance directly impacts player experience — this workflow produces a concrete plan with automated tests, benchmark scenarios, and platform matrices.

Your Role: You are a senior game performance engineer and QA strategist. Work with the user to identify their platforms, performance requirements, and representative content, then produce a strategy that combines automated profiling, manual testing checklists, and CI-integrated benchmarks.

---

Conventions

• Bare paths (e.g. template.md) resolve from the skill root.
• {skill-root} resolves to this skill's installed directory (where customize.toml lives).
• {project-root}-prefixed paths resolve from the project working directory.
• {skill-name} resolves to the skill directory's basename.

On Activation

Step 1: Resolve the Workflow Block

Run: python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow

If the script fails, resolve the workflow block yourself by reading these three files in base → team → user order and applying the same structural merge rules as the resolver:

1. {skill-root}/customize.toml — defaults
2. {project-root}/_bmad/custom/{skill-name}.toml — team overrides
3. {project-root}/_bmad/custom/{skill-name}.user.toml — personal overrides

Any missing file is skipped. Scalars override, tables deep-merge, arrays of tables keyed by code or id replace matching entries and append new entries, and all other arrays append.

Step 2: Execute Prepend Steps

Execute each entry in {workflow.activation_steps_prepend} in order before proceeding.

Step 3: Load Persistent Facts

Treat every entry in {workflow.persistent_facts} as foundational context you carry for the rest of the workflow run. Entries prefixed file: are paths or globs under {project-root} — load the referenced contents as facts. All other entries are facts verbatim.

Step 4: Load Config

Load config from {project-root}/_bmad/gds/config.yaml and resolve:

• project_name
• user_name
• communication_language
• output_folder

Step 5: Greet the User

Greet {user_name}, speaking in {communication_language}.

Step 6: Execute Append Steps

Execute each entry in {workflow.activation_steps_append} in order.

Activation is complete. If activation_steps_prepend or activation_steps_append were non-empty, confirm every entry was executed in order before proceeding. Do not begin the main workflow until all activation steps have been completed.

WORKFLOW ARCHITECTURE

This uses an inline workflow pattern for autonomous execution:

• Steps execute sequentially, building toward a complete performance test plan document
• Platform detection and target configuration drive all subsequent decisions
• The final deliverable is a comprehensive Performance Test Plan document
• Knowledge base reference: knowledge/performance-testing.md

Preflight Requirements

Before proceeding, verify:

• Target platforms identified (or discoverable from project files)
• Performance requirements known (target FPS, memory limits), or to be defined in Step 1
• Representative content available for testing
• Profiling tools accessible

Paths

• installed_path = {skill_root}
• validation = {installed_path}/checklist.md
• template = {installed_path}/performance-template.md
• default_output_file = {output_folder}/performance-test-plan.md

Variables

• target_fps = 60 (configurable per platform in Step 1)
• target_platform = auto (options: auto, pc, console, mobile)
• game_engine = auto (options: auto, unity, unreal, godot)

---

EXECUTION

Detect game engine and target platforms from project files. If ambiguous, ask the user. Establish frame rate targets per platform:

Platform	Target FPS	Minimum FPS	Notes
PC (High)	60+	30	Uncapped option
PC (Low)	30	30	Scalable settings
PS5/Xbox X	60	60	Performance mode
PS4/Xbox One	30	30	Locked
Switch Docked	30	30	Stable
Switch Handheld	30	25	Power saving
Mobile (High)	60	30	Device dependent
Mobile (Standard)	30	30	Thermal throttling

Filter this table to the user's actual target platforms. Adjust targets based on game genre and user input. Establish memory budgets per target platform:

Platform	Total RAM	Game Budget	Notes
PC (Min spec)	8 GB	4 GB	Leave room for OS
PS5	16 GB	12 GB	Unified memory
Xbox Series X	16 GB	13 GB	With Smart Delivery
Switch	4 GB	2.5 GB	Tight constraints
Mobile	4-6 GB	1.5-2 GB	Background apps

Establish loading time targets:

Scenario	Target	Maximum
Initial boot	< 10s	30s
Level load	< 15s	30s
Fast travel	< 5s	10s
Respawn	< 3s	5s

Adjust based on genre (e.g., fast travel may not apply to linear games).

Define stress test scenarios for frame rate validation: ``` SCENARIO: Maximum Entity Count GIVEN game level with normal enemy spawn WHEN enemy count reaches 50+ THEN frame rate stays above minimum AND no visual artifacts AND audio doesn't stutter

SCENARIO: Particle System Stress GIVEN combat with multiple effects WHEN 20+ particle systems active THEN frame rate degradation < 20% AND memory allocation stable

SCENARIO: Draw Call Stress GIVEN level with maximum visible geometry WHEN camera shows worst-case view THEN frame rate stays above minimum AND no hitching or stuttering

  </action>
  <action>Define memory test scenarios:

SCENARIO: Extended Play Session GIVEN game running for 4+ hours WHEN normal gameplay occurs THEN memory usage remains stable AND no memory leaks detected AND no crash from fragmentation

SCENARIO: Level Transition GIVEN player completes level WHEN transitioning to new level THEN previous level fully unloaded AND memory baseline returns AND no cumulative growth

  </action>
  <action>Define loading test scenarios:

SCENARIO: Cold Boot GIVEN game not in memory WHEN launching game THEN reaches interactive state in < target AND loading feedback shown AND no apparent hang

SCENARIO: Save/Load Performance GIVEN large save file (max progress) WHEN loading save THEN completes in < target AND no corruption AND gameplay resumes smoothly

  </action>
  <action>Adapt scenario details to match the specific game type and identified systems</action>
</step>

<step n="3" goal="Define Test Methodology">
  <action>Generate automated performance test code for the detected engine</action>

  <check if="engine == 'unity'">
    <action>Generate Unity Performance Test Runner examples:
```csharp
[UnityTest]
public IEnumerator Performance_CombatScene_MaintainsFPS()
{
    using (Measure.ProfilerMarkers(new[] { "Main Thread" }))
    {
        SceneManager.LoadScene("CombatStressTest");
        yield return new WaitForSeconds(30f);
    }
    var metrics = Measure.Custom(new SampleGroupDefinition("FPS"));
    Assert.Greater(metrics.Median, 30, "FPS should stay above 30");
}

</action>

Generate Unreal Automation test examples: ```cpp bool FPerformanceTest::RunTest(const FString& Parameters) { float StartTime = FPlatformTime::Seconds(); for (int i = 0; i < 100; i++) GetWorld()->SpawnActor(); float FrameTime = FApp::GetDeltaTime(); TestTrue("Frame time under budget", FrameTime < 0.033f); return true; } ``` Generate Godot benchmark test examples: ```gdscript func test_performance_entity_stress(): var frame_times = [] for i in range(100): var entity = stress_entity.instantiate() add_child(entity) for i in range(300): await get_tree().process_frame frame_times.append(Performance.get_monitor(Performance.TIME_PROCESS)) var avg_frame_time = frame_times.reduce(func(a, b): return a + b) / frame_times.size() assert_lt(avg_frame_time, 0.033, "Average frame time under 33ms (30 FPS)") ```

Define manual profiling checklists:

CPU Profiling

• Identify hotspots using engine profiler
• Check GC frequency and allocation pressure
• Verify multithreading usage and thread contention

GPU Profiling

• Draw call count at target scenes
• Overdraw analysis on complex areas
• Shader complexity assessment

Memory Profiling

• Heap allocation patterns over a 30-minute session
• Asset memory usage by category
• Leak detection across multiple level loads

Define the benchmark levels and their purpose:

Benchmark	Purpose	Duration
Combat Stress	Max entities, effects	60s
Open World	Draw distance, streaming	120s
Menu Navigation	UI performance	30s
Save/Load	Persistence performance	30s

Adapt benchmark names and durations to match the actual game content. Define baseline capture process: 1. Run benchmarks on reference hardware (document hardware specs) 2. Record baseline metrics (avg FPS, P95 frame time, peak memory) 3. Set regression thresholds (e.g., 10% FPS degradation = fail, 5% memory growth = fail) 4. Integrate benchmarks into CI pipeline as gated checks

Define platform-specific testing requirements for each target platform PC testing requirements: - Test across min/recommended hardware specs - Verify quality settings (Low/Medium/High/Ultra) all perform within budget - Check VRAM usage at each quality tier - Test at multiple resolutions (1080p, 1440p, 4K) Console testing requirements: - Test in both Performance and Quality modes if applicable - Verify thermal throttling behavior during extended sessions - Check suspend/resume impact on frame rate and memory - Test with varying storage speeds (internal SSD vs extended storage) Mobile testing requirements: - Test on low/mid/high tier representative devices - Monitor thermal throttling onset time and severity - Measure battery drain per hour of gameplay - Test with background apps consuming memory Load `{template}` and use it as the structural foundation for the output document Compile all information from Steps 1-5 into a comprehensive Performance Test Plan at `{default_output_file}` with this structure:

# Performance Test Plan: {project_name}

## Performance Targets
[FPS tables filtered to target platforms]
[Memory budget tables]
[Loading time targets]

## Test Scenarios

### Frame Rate Tests
[Stress test scenarios from Step 2]

### Memory Tests
[Extended play and leak detection scenarios]

### Loading Tests
[Boot, level load, save/load scenarios]

## Methodology

### Automated Tests
[Engine-specific code examples]
[CI integration instructions]

### Manual Profiling
[Checklists from Step 3]
[Tools to use per engine]

## Benchmark Suite
[Benchmark definitions from Step 4]
[Baseline capture process]
[Regression thresholds]

## Platform Matrix
[Platform-specific requirements from Step 5]

## Regression Criteria
[Quantified thresholds: FPS drop %, memory growth %, load time delta]
[CI gate configuration]

## Schedule
[When performance tests run: nightly, per-sprint, pre-release]
[Who reviews results and owns regressions]

Load and apply `{validation}` checklist to verify all deliverables are complete Present a summary of what was produced and the recommended next steps to the user Run: `python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow.on_complete` — if the resolved value is non-empty, follow it as the final terminal instruction before exiting.