game-experience-density-optimizer

game-experience-density-optimizer is a skill for Codex from DY-2026/GameDesignOS. It costs 124 tokens per session (3,852 once invoked), scanned A, original, MIT.

A guide for turning game-experience questions into short, measurable experiments. Experience density means how many meaningful choices or events a player encounters over time.

In plain words
What is it for?
Use it to plan experiments for retention, demo completion, pacing, feedback, atmosphere, cognitive load, or player engagement.
Why use it?
It helps replace vague concerns such as a slow opening or tiring middle section with testable changes, tracking measures, and rollback conditions.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to plan experiments for retention, demo completion, pacing, feedback, atmosphere, cognitive load, or player engagement.

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Install with agentmods
npx agentmods add skills/dy-2026/gamedesignos/game-experience-density-optimizer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer
Clone the repo
git clone --depth 1 https://github.com/DY-2026/GameDesignOS

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for game-experience-density-optimizer

README.md
[![agentmods](https://agentmods.dev/badge/skills/dy-2026/gamedesignos/game-experience-density-optimizer/github.svg)](https://agentmods.dev/skills/dy-2026/gamedesignos/game-experience-density-optimizer)
Your own site
<a href="https://agentmods.dev/skills/dy-2026/gamedesignos/game-experience-density-optimizer"><img src="https://agentmods.dev/badge/skills/dy-2026/gamedesignos/game-experience-density-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for game-experience-density-optimizer

Your own site · 80×15
<a href="https://agentmods.dev/skills/dy-2026/gamedesignos/game-experience-density-optimizer"><img src="https://agentmods.dev/badge/skills/dy-2026/gamedesignos/game-experience-density-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,852 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00124 $0.03852
Opus 5 $0.00062 $0.01926
Sonnet 5 $0.00025 $0.00770
Haiku 4.5 $0.00012 $0.00385

Measured 10d ago against content hash dc672f33e479, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

game-experience-density-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

game-experience-density-optimizer/SKILL.md · 230 lines

How it starts

The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

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/B 测试、留存实验、实验方案 A/B 或 A/B/C/D 变体、埋点、看板、决策门、owner、回滚
instrumentation_plan 用户重点问埋点、看板、指标口径、数据接线 事件字典、字段、触发时机、过滤器、数据质量门、隐私边界
review_and_decide 用户提供实验结果、指标变化、复盘材料 先查负向门和数据质量,再决定 amplify / iterate / observe / rollback / kill
full_client_delivery 用户要求客户交付、团队方案、完整文档、正式报告 展开完整 19 模块,附 handoff checklist、QA、风险门
schema_json 用户要求 agent 消费、自动化验证、结构化输出 输出符合 templates/experiment-plan.schema.json 的 JSON,保留证据和 unknown 字段

如果用户没有说明模式:一句话问题默认 quick_ed_triage;出现“本周、实验、A/B、怎么测、留存方案”默认 weekly_ab_plan;出现“完整、交付、客户、团队评审”默认 full_client_delivery

Hard Gates

所有输出必须经过这些门:

  1. evidence_gate:先声明 evidence_levelevidence_status、允许结论、禁止结论、置信度、缺失证据和混淆风险。读取 references/evidence-gate.zh-CN.md
  2. metric_horizon_gate:先判断 game_metric_modelpremium_single_playermobile_liveopshybridunknown。单机/买断制默认总旅程指标;手游/liveops 才默认 D1/D7。
  3. stimulation_window_gate:先判断最佳刺激窗口和无聊类型。无聊不自动等于刺激不足。
  4. density_formula_gate:把问题落到 CLPSFEBARMD/min,并说明为什么。
  5. one_primary_lever_gate:每个变体只能有一个主旋钮,最多一个不影响归因的辅助动作。
  6. instrumentation_gate:没有埋点/看板/复盘口径的方案不能说已可验证。
  7. decision_rule_gate:成功、观察、回滚、Kill 条件必须在实验前写死。
  8. ethics_gate:不得用暗黑模式或纯数值膨胀伪装体验优化。
  9. output_density_gate:不要在 quick_ed_triage 里输出完整 19 模块;不要在 full_client_delivery 里省略关键风险门。

Read the full file on GitHub · 230 lines

Files

What ships with it

33 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 230 lines · 124 tokens per session scan A dc672f33e479

Subscribe to this mod's changes

game-experience-density-optimizer is a skill published in the GitHub repository DY-2026/GameDesignOS (383 stars, last pushed 23d ago), licensed MIT. It adds 124 tokens to every session and 3,852 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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