game design prototyping companion

The problem

Game design prototypes often result in branching paths where valid ideas are discarded or forgotten. Teams lose critical institutional knowledge when they only document the "winning" iteration and erase the context of dead ends or parked concepts.

What it does

• Logs specific prototype nodes including the core question, simulation method, and key findings.
• Tracks branching evolution by categorizing results as baseline, promising, dead ends, or parked.
• Documents explicit backtrack logic to identify when a previously abandoned path should be revisited.
• Generates visual SVG branch maps from JSON data to map out the evolution of game mechanics over time.
• Preserves design memory by treating rejected paths as intelligence rather than failures.

Frameworks & tools

Python (for SVG generation scripts), JSON (for branch-map data structures), and SVG for visualization.

Why this beats prompting it yourself

Standard LLMs tend to focus only on the current solution, often losing the history of why certain paths were abandoned. This skill enforces a tree-based documentation structure that treats design history as a searchable asset, ensuring you never repeat the same failed experiment twice.

Use cases

• Mapping the evolution of a combat system from turn-based to real-time.
• Tracking failed UI experiments to prevent future design regression.
• Managing "what-if" scenarios for complex narrative branching.
• Onboarding new designers by showing the technical history of a mechanic.

Known limitations

Requires a specific JSON format for visual map generation and relies on the presence of a local Python execution environment for SVG rendering.