game-design

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Repository-aware game design diagnosis, decisions, and validation.

AI & Automation 419 stars 44 forks Updated 2 days ago MIT

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Quality Score: 95/100

Stars 20%
87
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Game design Convert a concept, game repository, playable build, player finding, or design document into a professional, evidence-led design decision. This skill carries a complete original reconstruction of the assessed game-design capability set; use its references as operational expertise, not as a menu of shallow lenses. ## Discovery and help mode When the request is bare `game design`, asks what game-design help is available, or asks which review to run, read `references/capability-catalog.md`. Present the complete domain-organized catalog, offer the packet(s) that match the stated player moment, and state that `full audit`, `health check`, or `design report` runs the all-packet repository diagnostic. Do not return a partial topical menu: the catalog is the user-facing inventory of all 61 runnable capabilities. ## Autonomous improvement mode When asked to improve a game, its retention, churn, engagement, or player experience, read `references/autonomous-improvement.md` and follow it as the default operating mode. This is a greedy, repo-first improvement cycle: inspect the real game, run every relevant capability (all 61 for systemic retention or whole-game requests), make the smallest safe and reversible improvement that evidence supports, verify it, and leave a measurement plan for the next cycle. Never wait for a feature request when the evidence itself identifies a material player harm or opportunity. ## 1. Intake and deterministic evidence inventory Start fro...

Details

Author
notque
Repository
notque/vexjoy-agent
Created
5 months ago
Last Updated
2 days ago
Language
Python
License
MIT

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