Game Design Feature Prioritization
SkillMediaPrioritize game design feature options by comparing expected impact, implementation cost, strategic fit, and roadmap context. Use when choosing between competing feature ideas, deciding what to do now versus later, ranking solution paths after ideation, or identifying which option is best immediately and which is better long-term.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Game Design Feature Prioritization skill
What this skill tells your AI
The instructions your AI receives, as published by stanestane/game-design-skills-bundle in game-design-feature-prioritization/SKILL.md and read by ahel’s review.
Choose the strongest next move, not just the loudest idea.
Use this skill to compare candidate features or solution paths and decide what should happen now, later, or not at all. Keep the process practical. Rough structured judgment is better than fake precision, but explicit comparison is still useful.
Read references/family-conventions.md when you need the shared conventions for this GROW-derived skill family.
What to produce
Generate:
- Candidate list - what is being prioritized
- Evaluation matrix - impact, cost, fit, risk, timing
- Priority ranking - now, later, discard, or monitor
- Recommendation - what should happen next and why
Process
1. List the candidates
Make sure the compared items are clear and meaningfully distinct.
2. Score the options
Use rough structured scoring such as 1-5 for:
- Impact - expected player or business value
- Implementation cost - time, resources, complexity
- Strategic fit - alignment with game direction and roadmap
- Risk - uncertainty, dependency burden, fragility
- Timing - whether this is right now or later
3. Interpret in context
Do not choose purely by raw score. Also consider:
- roadmap sequencing
- enabling value for future features
- opportunity cost
- whether a slightly weaker option is smarter in the long run
4. Recommend action
For each option, classify it as:
- Do now
- Test first
- Do later
- Discard for now
Response structure
Candidates
- ...
Evaluation Matrix
| Option | Impact | Cost | Strategic Fit | Risk | Timing | Notes |
|---|
Priority Ranking
- ...
Recommendation
- ...
Fast mode
- What are we choosing between?
- Which option has the best impact-to-cost profile?
- Which option best fits the roadmap right now?
- What should we do now, later, or not at all?
Working principle
The locally optimal choice is not always the strategically best one.
Signals
- GitHub stars
- 62
- Forks
- 12
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
game-design-feature-prioritization- Source
- github.com/stanestane/game-design-skills-bundle