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game design failure loop audit
Audit and optimize game failure loops to convert player defeat into motivation and mastery.
$5
Works with the AI tools you already use
game design failure loop audit
Example session with this skill installed
Audit the failure loop for a stealth game where being spotted by a guard results in immediate 'Game Over' and reloads the last manual save point, which could be 10 minutes old.
- Read your context and instructions
- Compiled the game design failure
- Generated the UI component
I have audited the 'Detection-to-Reload' failure loop for your stealth title. The current structure heavily penalizes player experimentation by tethering progress to manual save frequency rather than encounter design.
Failure Loop Audit: Stealth Detection
| Dimension | Current State | Effect on Retry Motivation |
|---|---|---|
| Loss | Up to 10 minutes of un-checkpointed progress, including items found. | High resentment; feels like "lost time" rather than a fair price. |
| Learning | Low. The reload wipes the immediate context of the mistake. | Players repeat the same route safely instead of iterating on the fail-point. |
| Retention | Zero. All state is reverted to the manual save. | Discourages risky, creative playstyles. |
| Re-entry Speed | Slow. Requires menu navigation and loading screen. | Significant friction that breaks immersion and flow. |
Friction & Emotional Diagnosis
The loop currently functions as a
Shame/Grindback Loop. Because the punishment (10m loss) is disproportionate to the mistake (one detection), players feel dread rather than curiosity. The "Immediate Game Over" removes the "Panic/Recovery" phase, which is often the most engaging part of stealth.
Recommendations
Implement Auto-Checkpoints: Trigger a soft save at the entrance of each guarded zone to cap progress loss at < 2 minutes.
2.
Introduce an 'Alert' Phase: Instead of an instant Game Over, transition guards to a combat or search state, allowing the player to spend resources (e.g., smoke bombs) to reset the loop without reloading.
3.
Ghosting Data: Upon reload, briefly highlight the guard path that caused the detection to provide immediate educational feedback.
Next steps
- Define the resource cost for "escaping" an alert versus a full reload.
- Identify logical "zone transitions" where auto-saves can occur without stutter.
- Test if a "Last Known Position" marker helps players learn guard patterns faster.
game-design-failure-loop-audit.tsx
TSX · React component
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Players often quit games not because the challenge is too high, but because the process of failing feels unfair, tedious, or demoralizing. When the cost of failure outweighs the educational value of the attempt, you lose retention and player trust.
What it does
- Analyzes the full sequence from the moment of failure to the point of re-entry.
- Evaluates the balance between resource loss, information gain, and meta-progression retention.
- Identifies specific friction points like "dead time," tedious setup, or unclear feedback.
- Classifies the loop into archetypes like "Mastery," "Grindback," or "Shame" loops to ensure alignment with game goals.
- Provides a prioritized list of tuning changes for costs, telegraphing, and recovery pacing.
Why this beats prompting it yourself
General LLMs often suggest making games easier when they detect frustration. This skill understands that difficulty isn't the enemy, friction is. It applies specific game design frameworks to differentiate between "good" punishment that drives mastery and "bad" punishment that causes churn.
Use cases
- Tuning the run-back distance and resource loss in a Soulslike or Roguelite.
- Auditing stealth mission resets to ensure players understand why they were spotted.
- Evaluating the onboarding experience to prevent early-game bounce from simple mistakes.
- Refining puzzle reset mechanics to preserve the "aha!" moment without repetitive chores.
Known limitations
Does not perform math-heavy economy balancing or low-level code debugging. Requires a description of the game mechanics to be effective.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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