soak-test

Donchitos/Claude-Code-Game-Studios · updated Apr 16, 2026

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$npx skills add https://github.com/Donchitos/Claude-Code-Game-Studios --skill soak-test
0 commentsdiscussion
summary

### Soak Test

  • description: "Generate a soak test protocol for extended play sessions. Defines what to observe, measure, and log during long play sessions to surface slow leaks, fatigue effects, and edge cases that
  • argument-hint: "[duration: 30m | 1h | 2h | 4h] [focus: memory | stability | balance | all]"
  • allowed-tools: Read, Glob, Grep, Write
skill.md
name
soak-test
description
"Generate a soak test protocol for extended play sessions. Defines what to observe, measure, and log during long play sessions to surface slow leaks, fatigue effects, and edge cases that only appear after sustained play. Primarily used in Polish and Release phases."
argument-hint
"[duration: 30m | 1h | 2h | 4h] [focus: memory | stability | balance | all]"
user-invocable
true
allowed-tools
Read, Glob, Grep, Write

Soak Test

A soak test (also called an endurance test) is an extended play session run with specific observation goals. Unlike a smoke check (broad critical path, ~10 min) or a single-feature playtest (~30 min), a soak test runs for 30 minutes to several hours to surface:

  • Memory leaks — gradual heap growth that only appears after scene transitions
  • Performance drift — frame time degradation that worsens over time
  • State accumulation bugs — issues that only appear after N repetitions of a mechanic (inventory full, score overflow, AI state corruption)
  • Fun fatigue — mechanics that feel good in a first session but grow repetitive over extended play
  • Content exhaustion — the point where players run out of novel content

This skill generates the observation protocol and analysis harness — the human does the actual playing.

Output: production/qa/soak-test-[date]-[duration].md

When to run:

  • Polish phase — before /gate-check release
  • After fixing a memory or stability issue (regression soak)
  • When extended play has not been formally tracked

1. Parse Arguments

Duration (default: 1h):

  • 30m — short soak; suitable for testing a single mechanic or scene
  • 1h — standard soak; covers most common leak categories
  • 2h — extended soak; recommended for first full Polish soak
  • 4h — deep soak; required for games with long session design (RPGs, sims)

Focus (default: all):

  • memory — focus on heap size, object count, leak patterns
  • stability — focus on crash/freeze/hang detection
  • balance — focus on fun fatigue, content exhaustion, difficulty perception
  • all — all of the above

2. Load Context

Read:

  • .claude/docs/technical-preferences.md — engine (for engine-specific memory monitoring guidance), performance budgets (memory ceiling, target FPS)
  • design/gdd/game-concept.md — intended session length (for comparison against soak duration), core loop description
  • Most recent file in production/playtests/ — prior playtest findings (to avoid re-documenting known issues)
  • Most recent file in production/qa/qa-plan-*.md — current sprint test coverage (to understand what has been formally tested vs. what the soak covers)

Note any performance budget targets from technical-preferences.md:

  • Memory ceiling: [N MB, or "not set"]
  • Target FPS: [N, or "not set"]
  • Frame budget: [N ms, or "not set"]

3. Define Observation Checkpoints

Based on duration, generate timed checkpoints:

30m soak: T+0, T+10, T+20, T+30 1h soak: T+0, T+15, T+30, T+45, T+60 2h soak: T+0, T+20, T+40, T+60, T+80, T+100, T+120 4h soak: T+0, T+30, T+60, T+90, T+120, T+180, T+240

At each checkpoint, the observer records the observation items defined in Phase 4.


4. Generate the Soak Test Protocol

Memory / Stability observation items (if focus = memory or all)

Engine-specific monitoring guidance:

Godot 4:

  • Open Debugger → Monitors tab; track Memory → Static Memory and Object Count → Objects across checkpoints
  • Record: Static Memory (KB), Object Count, Orphan Nodes count
  • Alert threshold: Memory growth > 20% from T+0 after the first 15 minutes (some growth on load is expected; sustained growth indicates a leak)
  • Note: Performance.get_monitor(Performance.MEMORY_STATIC) returns bytes in Godot 4.6

Unity:

  • Open Memory Profiler (Window → Analysis → Memory Profiler)
  • Record: Total Reserved Memory (MB), GC Allocated (MB), Object Count at each checkpoint
  • Alert threshold: GC Allocated growing monotonically across 3+ checkpoints

Unreal Engine:

  • Use stat memory console command at each checkpoint
  • Record: Physical Memory Used (MB), Physical Memory Available
  • Alert threshold: Physical Memory Used growth > 50MB over the full soak

Stability observation items (if focus = stability or all)

At each checkpoint, note:

  • No crash, hang, or freeze occurred since last checkpoint
  • Frame rate still within target budget ([target FPS] fps)
  • Audio still playing correctly (no desync or silence)
  • All HUD elements still rendering correctly
  • Input responding as expected (no input loss or lag spike)

Balance / fatigue observation items (if focus = balance or all)

Collect subjective observations at each checkpoint:

  • Core mechanic still feels rewarding (Y/N)
  • Perceived difficulty level: [too easy / appropriate / too hard]
  • Any "I've seen this before" moments since last checkpoint? (novel content exhaustion)
  • Any moment of frustration since last checkpoint? Note cause.
  • Any moment of peak engagement since last checkpoint? Note cause.

5. Generate the Protocol Document

# Soak Test Protocol

> **Date**: [date]
> **Duration**: [duration]
> **Focus**: [memory | stability | balance | all]
> **Engine**: [engine]
> **Generated by**: /soak-test

---

## Pre-Session Setup

Before starting the soak:

- [ ] Game is running from a **fresh launch** (not resumed from a prior session)
- [ ] All background applications closed (minimise OS memory interference)
- [ ] Performance monitoring tool open and recording:
  - **Godot**: Debugger → Monitors tab → Memory section visible
  - **Unity**: Memory Profiler window open
  - **Unreal**: `stat memory` ready in console
- [ ] Soak target confirmed: [session design intent from game concept]
- [ ] Prior known issues to watch for: [from most recent playtest / qa-plan]

---

## Baseline (T+0) — Record Before Playing

| Metric | Baseline Value |
|--------|---------------|
| Memory / Heap | [record before first frame of gameplay] |
| Object Count | [record] |
| FPS (first 30 seconds) | [record] |
| [Engine-specific metric] | [record] |

---

## Checkpoint Log

### T+[N] minutes

**Memory / Stability** *(if applicable)*:

| Metric | Value | Δ from Baseline | Alert? |
|--------|-------|-----------------|--------|
| Memory / Heap | | | |
| Object Count | | | |
| FPS | | | |
| Crashes / Hangs | | | |

**Stability checks**:
- [ ] No crash or hang since last checkpoint
- [ ] Frame rate within budget ([N] fps target)
- [ ] Audio correct
- [ ] HUD rendering correctly
- [ ] Input responding correctly

**Balance / Fatigue** *(if applicable)*:
- Core mechanic still rewarding: Y / N
- Difficulty perception: too easy / appropriate / too hard
- Notable moments: [note any peak engagement or frustration]
- Content exhaustion signs: Y / N — [describe]

**Free observations**:
*(Note anything unexpected observed since the last checkpoint)*

---

[Repeat Checkpoint Log section for each timed checkpoint]

---

## Post-Session Analysis

### Memory Trend

| Checkpoint | Memory | Δ/hr extrapolated |
|------------|--------|-------------------|
| T+0 | | |
| [T+N] | | |

**Leak detected?** Y / N
**Estimated time to OOM at current rate**: [N hours / not applicable]

### Stability Summary

Total crashes: [N]
Total hangs: [N]
Worst FPS observed: [N] fps at [checkpoint]
Performance degradation: stable / mild / severe

### Balance / Fatigue Summary

Fun curve: [engaged throughout / fatigue onset at T+N / repetitive from start]
Content exhaustion point: [never / at T+N / early]
Difficulty arc: [appropriate / too easy throughout / difficulty spike at T+N]

### Issues Found

| ID | Severity | Checkpoint | Description |
|----|----------|------------|-------------|
| SOAK-001 | S[1-4] | T+[N] | [description] |

---

## Verdict: PASS / PASS WITH CONCERNS / FAIL

**PASS**: No leaks detected, stability maintained, fun factor consistent
**PASS WITH CONCERNS**: Minor drift or fatigue noted; addressable in Polish
**FAIL**: Memory leak confirmed, stability breach, or severe fun fatigue

---

## Sign-Off

- **Tester**: [name] — [date]
- **QA Lead review**: [name] — [date]

6. Write Output

Present the protocol summary in conversation, then ask:

"May I write this soak test protocol to production/qa/soak-test-[date]-[duration].md?"

Write only after approval.

After writing:

"Protocol written. To run the soak:

  1. Open the file and follow the Pre-Session Setup checklist
  2. Record each checkpoint as you play
  3. Complete the Post-Session Analysis section when done
  4. File bugs from 'Issues Found' to production/qa/bugs/
  5. Run /bug-triage sprint after the session to integrate any S1/S2 issues

If the verdict is FAIL, run /smoke-check again after fixing the issues."


Collaborative Protocol

  • This skill generates a protocol — humans run it — never attempt to run a soak test automatically. The observations require a human observer.
  • Duration should match the game's session design — a 5-minute game doesn't need a 4h soak; a city-builder might. Use judgment and ask if unclear.
  • First soak should be all focus — narrow focus (memory-only) is for regression soaks after a specific fix, not the first pass
  • Ask before writing — always confirm before creating the protocol file
how to use soak-test

How to use soak-test on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add soak-test
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/Donchitos/Claude-Code-Game-Studios --skill soak-test

The skills CLI fetches soak-test from GitHub repository Donchitos/Claude-Code-Game-Studios and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/soak-test

Reload or restart Cursor to activate soak-test. Access the skill through slash commands (e.g., /soak-test) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.533 reviews
  • Zaid Kapoor· Dec 8, 2024

    soak-test fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Charlotte Garcia· Nov 23, 2024

    soak-test is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Zaid Farah· Nov 19, 2024

    soak-test reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kiara Torres· Oct 14, 2024

    Keeps context tight: soak-test is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kiara Rahman· Oct 6, 2024

    Registry listing for soak-test matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Henry Perez· Sep 25, 2024

    Useful defaults in soak-test — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Yash Thakker· Sep 13, 2024

    We added soak-test from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sophia Malhotra· Sep 5, 2024

    I recommend soak-test for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sofia Bhatia· Aug 24, 2024

    Useful defaults in soak-test — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Aisha Srinivasan· Aug 16, 2024

    I recommend soak-test for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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