Agent skill

game-experience-density-optimizer

当用户需要把游戏体验浓度、留存、首局节奏、Demo 完成率、单机总旅程、D1/D7、反馈、具身感、氛围、认知负荷、最佳刺激窗口、FEP/free-energy、预测误差、Markov blanket、习惯化或 liveops 参与问题,编译成可上线、可埋点、可复盘、可回滚的一周 ED 实验包时使用。Use when converting game experience-density and engagement problems into rollback-ready ED experiments.

DY-2026github.com/DY-2026GitHub ↗
claude-codeMIT
Install
npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 34
SKILL.md size: 8 KB
Bundled scripts: none
Version: 1.3.0-candidate
Requires: 可处理文本、截图、录屏或遥测摘要;生产实验、真实用户触达和指标承诺必须经过 Human Gate。
Path: game-experience-density-optimizer/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 316
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Game Experience Density Optimizer Copyright (c) 2026 Paranoia. Licensed under the MIT License. ## Mission 把模糊的游戏体验问题编译成可上线、可埋点、可复盘、可回滚的 ED 实验包。 这里的 ED 是 `Experience Density / 体验浓度`。中文统一叫“体验浓度”,不要另造概念名。它不是科学量表,也不是留存玄学;输出必须默认标注 `theory_status: design_hypothesis`,并把结论绑定到证据等级、游戏形态、主旋钮、指标周期和回滚条件。 默认内部管线: ```text 输入材料 -> 输出模式路由 -> Evidence Gate -> 游戏形态分流 -> 最佳刺激窗口 -> ED 公式项定位 -> 主旋钮选择 -> 实验变体编译 -> 埋点/看板编译 -> 预注册决策门 -> 输出门检查 ``` ## When To Use 用户讨论以下问题时触发本 skill: - “体验浓度”、`ED / Experience Density`、每分钟有多少有意义选择、首局太空、首个爆点太晚。 - 留存实验、D1/D3/D7、每日会话、回流 rehook、活动留存、老玩家钝化、中段疲劳。 - 单机总游戏时长、买断制完成率、Steam Demo 完成率、章节推进、核心循环到达率、重玩意愿。 - 反馈不爽、不清楚、不跟手、打击软、操控延迟、镜头/触觉/动作节拍问题。 - 氛围空、留白无质感、叙事停顿、信息太吵、认知负荷高。 - 最佳刺激、低刺激无聊、过载无聊、习惯化、半熟半新、可控惊讶。 - FEP/free-energy、预测误差、Markov blanket、玩家和游戏的输入输出边界。 - 一周 A/B 测试、埋点字典、看板字段、预注册规则、回滚/Kill 条件。 不要用于只有一句创意、还没有核心循环的任务;先用 `game-concept-architect`。不要把截图、PV 或商店页直接当真实节奏证据;先用 `game-experience-analyzer` 建证据层。不要设计暗黑模式、误导奖励、焦虑红点、虚假倒计时、付费压力或不可逆损失伪装。 ## Mode Router 先判断输出模式,再决定交付深度。强 skill 的默认不是写大报告,而是给当前场景刚好够用的结果。 | mode | 触发 | 输出密度 | | --- | --- | --- | | `quick_ed_triage` | 用户只给一句体验问题,或明确要快速判断 | 1 个边界判断、1 个刺激窗口、1 个主旋钮、2 个最小改动、3 个验证指标、1 个回滚条件 | | `weekly_ab_plan` | 用户问怎么改、怎么测、本周怎么做、A/

What's inside
Steps it walks through
  1. Mission
  2. When To Use
  3. Mode Router
  4. Hard Gates
  5. Core Model
  6. Evidence Gate
  7. Metric Horizon
  8. Output Contracts
  9. quickedtriage
  10. weeklyabplan
  11. instrumentationplan
  12. reviewanddecide
  13. fullclientdelivery
  14. schemajson
Ships with 24 files
  • README.md
  • agents/openai.yaml
  • evals/behavior_evals.json
  • evals/evals.json
  • evals/negative_cases.md
  • evals/rubric.yaml
  • evals/synthetic_outputs.json
  • examples/synthetic-hybrid-conflict-review.md
  • examples/synthetic-premium-demo-completion-ed-plan.md
  • examples/synthetic-survivors-first-session-ed-plan.md
  • references/density-diagnosis-workflow.zh-CN.md
  • references/density-formula.zh-CN.md
  • references/ed-framework.zh-CN.md
  • references/ed-handoff-contract.md
  • references/evidence-gate.zh-CN.md
  • references/flow-sdt-experience-gates.zh-CN.md
  • references/free-energy-markov-blanket-lens.zh-CN.md
  • references/interaction-prediction-lens.zh-CN.md
  • references/lever-playbook.zh-CN.md
  • references/metric-horizon-by-game-model.zh-CN.md
  • references/optimal-stimulation-window.zh-CN.md
  • references/retention-risk-gates.zh-CN.md
  • references/telemetry-metric-dictionary.zh-CN.md
  • references/theory-source-map.zh-CN.md
first 24 of 34
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About this skill
What does the game-experience-density-optimizer skill do?

当用户需要把游戏体验浓度、留存、首局节奏、Demo 完成率、单机总旅程、D1/D7、反馈、具身感、氛围、认知负荷、最佳刺激窗口、FEP/free-energy、预测误差、Markov blanket、习惯化或 liveops 参与问题,编译成可上线、可埋点、可复盘、可回滚的一周 ED 实验包时使用。Use when converting game experience-density and engagement problems into rollback-ready ED experiments.

How do I install it?

Run `npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From DY-2026/GameDesignOS, a repository with 316 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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