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기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/notque/vexjoy-agent --skill game-design명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Run the full evidence-to-live implementation workflow for large, multi-system, multi-wave, or CPU-delegated 5 Star Booker GM programs.
Classify user requests and route to the correct agent + skill. Primary entry point for all delegated work.
Structured multi-phase workflows: review, debug, refactor (tidy, clean up, untangle messy code without behaviour change), deploy, create, research.
SKILL.md 표시 중
| name | game-design |
| version | 2.1.0 |
| description | Repository-aware game design diagnosis, decisions, and validation. |
| agent | ui-design-engineer |
| user-invocable | true |
| allowed-tools | ["Read","Write","Edit","Bash","Grep","Glob"] |
| routing | {"triggers":["game design","improve this game","game improvement","game design audit","game design report","core loop","game feel","player motivation","game balance","game economy","game onboarding","first-time experience","game pitch","game design document","game prototype","game scope","game progression","game fairness","game diagnostic","retention","churn","engagement"],"not_for":"Implementing a game in Phaser or Three.js, generating game art, or QA automation without a design question.","pairs_with":["game-pipeline","phaser-gamedev","threejs-builder","decision-helper"],"complexity":"Medium","category":"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.
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.
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.
Start from repository evidence. Read the target repository's governing instruction files first. Search its installed skills and agents for game, product, UI, implementation, analytics, and research guidance; load every applicable local instruction and record its authority before drawing conclusions. Then use file search and code inspection to find design documents, player-facing copy, rules and state, UI, configuration and tables, tests, analytics schemas, issues, ownership, and recent changes. Separate facts into observed, documented, measured, and inferred.
Ask only what the repository cannot answer:
Load every module that could materially change the recommendation, its player-risk assessment, or validation plan. Do not stop at the smallest topical match. Add adjacent modules when the player path crosses their domain.
| Signal | Load modules |
|---|---|
| Promise, fantasy, loops, goals, feature coherence | core-loop-and-pillars.md, capabilities/08-game-design-craft-critique.md where relevant, audit-and-red-team.md |
| Ideation, novelty, removal, reuse, blocked choice | creative-and-options.md, core-loop-and-pillars.md, planning-and-production.md |
| Player motives, personas, values, inclusion | player-and-social.md, cognition-and-choice.md, social-and-competitive.md when others matter |
| Information, prompts, bias, causality, randomness | cognition-and-choice.md, fairness-and-failure.md, pacing-and-return.md |
| Failure, difficulty, fairness, recovery | fairness-and-failure.md, core-loop-and-pillars.md, pacing-and-return.md |
| First-time experience, flow, friction, goals, session close, return | pacing-and-return.md, fairness-and-failure.md, cognition-and-choice.md |
| Co-op, competition, guilds, social sessions, rank | social-and-competitive.md, player-and-social.md, progression-and-economy.md |
| Rewards, currencies, battle passes, KPIs | progression-and-economy.md, player-and-social.md, fairness-and-failure.md |
| Pitch, mood, emotional direction, prototype, design doc | emotion-and-presentation.md, artifacts-and-prototyping.md, planning-and-production.md |
| Scope, sequence, estimates, staffing, decisions | planning-and-production.md, core-loop-and-pillars.md, audit-and-red-team.md |
| Full game/repository health report | Read capability-matrix.md, every domain module, then full-diagnostic.md. |
The module contains card-level protocols. Read all cards within each selected module. Full diagnostic mode is intentionally greedy: every card is mandatory.
Trace: cue → interpretation → choice → system response → cost/reward → feedback → next intention. For a failure path, include expectation, information, retry, and learning. For social play, include invitation, coordination, conflict, absence, rejoin, recognition, and abuse recovery.
For every material finding, state the player consequence, evidence, competing explanations, confidence, severity, and the smallest change that would distinguish the leading explanations. Do not replace a player outcome with a framework label, funnel metric, or author preference.
Offer two to five genuinely distinct options where a choice remains. For each option state:
Prefer reversible experiments when evidence is weak. Reject dark patterns: hidden costs, coercive scarcity, shame, forced social exposure, punitive absence, harassment incentives, or confusion used as retention.
For a full review, follow references/full-diagnostic.md exactly. The output must include an evidence inventory, full matrix coverage ledger, severity-ranked findings, detailed fixes, ownership, validation, unknowns, and a sequenced 30/60/90-day plan. A missing artifact is a finding; it does not excuse skipping a module.
Before returning, verify that every finding has repository/player evidence or is explicitly an inference, every recommendation has a validation plan and owner, and the reference coverage matches the request. State what would change the recommendation.
| Failure | Recovery |
|---|---|
| Repository contains no player evidence | Report the evidence gap; propose a bounded observation or instrumentation plan. |
| Framework conflicts with player evidence | Keep the evidence; use the framework only to generate competing hypotheses. |
| Scope is too broad to implement | Sequence cuts and reversible tests; do not hide the risk in a single priority score. |
| User asks for a full report but the game is not runnable | Audit available intent and system evidence, label limits, and prioritize a playable proof. |
This is an independent, original synthesis informed by an assessment of Stanestane/game-design-skills-bundle at commit 85d5c6545afd0988de5ef1ee7d95e67edd8f5a7b. The assessed repository declares no license. No upstream files or wording are included. references/capability-matrix.md records capability coverage for maintenance.