Agent skill

Game Experience Density Optimizer

by DY-2026 in DY-2026/GameDesignOS

Install Game Experience Density Optimizer

skills CLI

$ npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install DY-2026/GameDesignOS game-experience-density-optimizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/DY-2026/GameDesignOS.git skills-src && mkdir -p .claude/skills && cp -r skills-src/game-experience-density-optimizer .claude/skills/game-experience-density-optimizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "game-experience-density-optimizer" agent skill from https://github.com/DY-2026/GameDesignOS/tree/main/game-experience-density-optimizer into .claude/skills/game-experience-density-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "game-experience-density-optimizer", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/DY-2026/GameDesignOS/tree/main/game-experience-density-optimizer

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install DY-2026/GameDesignOS game-experience-density-optimizer --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/DY-2026/GameDesignOS.git skills-src && mkdir -p .agents/skills && cp -r skills-src/game-experience-density-optimizer .agents/skills/game-experience-density-optimizer && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "game-experience-density-optimizer" agent skill from https://github.com/DY-2026/GameDesignOS/tree/main/game-experience-density-optimizer into .agents/skills/game-experience-density-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "game-experience-density-optimizer", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install DY-2026/GameDesignOS game-experience-density-optimizer --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/DY-2026/GameDesignOS.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/game-experience-density-optimizer .cursor/skills/game-experience-density-optimizer && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "game-experience-density-optimizer" agent skill from https://github.com/DY-2026/GameDesignOS/tree/main/game-experience-density-optimizer into .cursor/skills/game-experience-density-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "game-experience-density-optimizer", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/DY-2026/GameDesignOS.git --path game-experience-density-optimizer

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install DY-2026/GameDesignOS game-experience-density-optimizer --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/DY-2026/GameDesignOS.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/game-experience-density-optimizer .gemini/skills/game-experience-density-optimizer && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "game-experience-density-optimizer" agent skill from https://github.com/DY-2026/GameDesignOS/tree/main/game-experience-density-optimizer into .gemini/skills/game-experience-density-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "game-experience-density-optimizer", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install DY-2026/GameDesignOS game-experience-density-optimizer

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/DY-2026/GameDesignOS.git skills-src && mkdir -p .github/skills && cp -r skills-src/game-experience-density-optimizer .github/skills/game-experience-density-optimizer && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "game-experience-density-optimizer" agent skill from https://github.com/DY-2026/GameDesignOS/tree/main/game-experience-density-optimizer into .github/skills/game-experience-density-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "game-experience-density-optimizer", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install DY-2026/GameDesignOS game-experience-density-optimizer --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/DY-2026/GameDesignOS.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/game-experience-density-optimizer .opencode/skills/game-experience-density-optimizer && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "game-experience-density-optimizer" agent skill from https://github.com/DY-2026/GameDesignOS/tree/main/game-experience-density-optimizer into .opencode/skills/game-experience-density-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "game-experience-density-optimizer", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: game-experience-density-optimizer
GitHub stars: 414
Token cost: ~2.1k tokens
SKILL.md length: 380 words
Files: 35 (incl. references)
Skills in repo: 7
Repo updated: Aug 17, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Works in 9 steps: evidence_gate：先声明… → metric_horizon_gate：先判断… → stimulation_window_gate：先判断最佳刺激窗口和无聊类型。无聊… → …
Converting game experience-density and engagement problems into rollback-ready ED experiments
SKILL.md covers Mission, When To Use, Mode Router and Hard Gates, plus 8 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Game Experience Density Optimizer is an agent skill from DY-2026/GameDesignOS. 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.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 38 other files, including reference files (for example `README.md`, `agents/openai.yaml` and `evals/behavior_evals.json`). Compatibility notes: 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。

It sits in Game Development, covering Game design. The repository describes itself as: Local-first game design OS for AI agents: turn sessions into evidence, experiments, reviewable decisions, and durable project memory—Human Gates and rollback. The licence is MIT.

When your agent uses it

Converting game experience-density and engagement problems into rollback-ready ED experiments
Tasks that involve Game design

Example prompts

“/game-experience-density-optimizer”

Requirements

Compatibility (from SKILL.md): 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。

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

evidence_gate：先声明 evidence_level、evidence_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。
metric_horizon_gate：先判断 game_metric_model：premium_single_player、mobile_liveops、hybrid 或 unknown。单机/买断制默认总旅程指标；手游/liveops 才默认 D1/D7。
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。
density_formula_gate：把问题落到 CLP、SF、EB、AR、MD/min，并说明为什么。
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。
instrumentation_gate：没有埋点/看板/复盘口径的方案不能说已可验证。
decision_rule_gate：成功、观察、回滚、Kill 条件必须在实验前写死。
ethics_gate：不得用暗黑模式或纯数值膨胀伪装体验优化。
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。

What it can do on your machine

Read from SKILL.md and the folder at commit ada4bf9. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
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。
From compatibility in the SKILL.md frontmatter.

Context cost

Game Experience Density Optimizer loads about 2.1k tokens when it runs, and up to ~26k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 380 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~72
When it runs · the whole SKILL.md, loaded when a task matches: ~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~26k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from DY-2026/GameDesignOS at commit ada4bf9, republished under its MIT licence (© DY-2026). 380 words, ~2,085 tokens.

Download SKILL.mdSave it as .claude/skills/game-experience-density-optimizer/SKILL.md (or your agent's skills folder). This skill also uses 34 other files; get the full folder from GitHub.

name: game-experience-density-optimizer
description: 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.
compatibility: 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。
license: MIT
metadata.version: 1.3.0-candidate
metadata.short-description: 体验浓度实验编译器

Game Experience Density Optimizer

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/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：

evidence_gate：先声明 evidence_level、evidence_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。
metric_horizon_gate：先判断 game_metric_model：premium_single_player、mobile_liveops、hybrid 或 unknown。单机/买断制默认总旅程指标；手游/liveops 才默认 D1/D7。
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。
density_formula_gate：把问题落到 CLP、SF、EB、AR、MD/min，并说明为什么。
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。
instrumentation_gate：没有埋点/看板/复盘口径的方案不能说已可验证。
decision_rule_gate：成功、观察、回滚、Kill 条件必须在实验前写死。
ethics_gate：不得用暗黑模式或纯数值膨胀伪装体验优化。
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。

Core Model

体验浓度指：当前玩家在当前情境下，单位时间内可吸收、可解释、可转化为探索/学习/意义的刺激密度。

默认工作公式：

text

ED = MD/min * (SF + EB + AR) / CLP

MD/min：每分钟有意义选择次数。不是点击频率，也不是选项数量。
SF：可感知反馈。不是光污染，而是能被玩家看见、听见、感到并归因。
EB：具身感加成。不是剧情代入，而是输入、动作、镜头、触觉和反馈的耦合。
AR：氛围感加成。不是堆素材，而是留白、音画、世界反应和风格一致性。
CLP：认知负荷惩罚。玩家看不懂、学不会、被噪音打断时，先降分母。

诊断顺序固定为：先判窗口，再降噪，再提质，后调频。只有在信息清晰、反馈可归因、耦合可理解之后，调高 MD/min 才有意义。

FEP、自由能、预测处理、Markov blanket、GameFlow、SDT 只作为设计启发式镜头，不得写成神经科学或心理学证明。涉及这些理论时必须保留 theory_status: design_hypothesis。

Evidence Gate

不要凭感觉跑太远。证据等级决定允许输出什么：

level	材料	允许	禁止
`L0_text_only`	只有口述	假设、最小实验、埋点需求	声称真实原因或承诺指标提升
`L1_static_assets`	截图、商店页、PV 截帧	信息层级、视觉噪音、可能风险	判断真实节奏、手感或会话行为
`L2_recording`	录屏、试玩视频	时间轴、反馈窗口、节奏断点、退出前行为	推断全部玩家心理
`L3_playtest_notes`	试玩笔记、访谈摘要	玩家分群假设、问题卡、方向性实验	忽略样本偏差
`L4_telemetry_snapshot`	指标快照	分流、埋点核对、方向性实验	混版本、混渠道、混新老用户
`L5_ab_result`	实验结果	复盘决策	跳过负向门、数据质量门和预注册规则

证据不足时输出 evidence_status: assumption_only 或 partial_evidence。没有真实埋点或试玩证据时，只能说“验证假设”，不能说“一定提升 D1/D7、总时长或完成率”。

Metric Horizon

先选游戏形态，再选 P1。

premium_single_player：买断制、单机、Steam Demo、章节制、完整旅程承诺。P1 优先看总有效游玩时长、Demo/章节完成率、核心循环到达率、通关/重玩意愿、评价/退款风险。不默认 D1/D7。
mobile_liveops：手游、长线运营、活动、每日循环、回流。P1 可以看 D1/D3/D7/D30、每日会话、连续活跃、活动留存、回流成功率，同时必须看疲劳和投诉。
hybrid：总旅程和 liveops 两套 P1 分开预注册。任一关键周期受损，都不能宣布整体成功。
unknown：材料不足时标 unknown，并写清暂不适用的指标。

Output Contracts

quick_ed_triage

必须包含：

output_mode
case_boundary
evidence_gate
metric_horizon
optimal_stimulation_fit
primary_formula_item
primary_lever
two_minimal_changes
verification_metrics
rollback_condition
unsupported_claims

Show full SKILL.md (147 more words)Show less

weekly_ab_plan

必须包含：

case_boundary
evidence_gate
metric_horizon
theory_status
optimal_stimulation_fit
diagnosis_summary
experiment_hypothesis
variant_matrix
instrumentation_dictionary
metric_plan
dashboard_spec
decision_rules
weekly_schedule
handoff_checklist

instrumentation_plan

必须至少生成 variant_assigned、session_started、meaningful_decision_made、salient_feedback_fired、cognitive_load_signal、session_checkpoint、session_ended。涉及手感/反馈时增加 embodiment_signal_observed 和 blanket_coupling_signal；涉及 OLSO/FEP 时增加 optimal_stimulation_window_observed 和 prediction_error_window_observed；涉及长线疲劳时增加 anti_habituation_signal。

review_and_decide

复盘顺序固定：

数据质量门：分流、版本、渠道、样本、埋点完整性。
负向门：崩溃、早退、失败率、投诉、疲劳、经济、公平、暗黑模式。
P1：按 game_metric_model 读取主周期。
P2：用 ED proxy、CLP、SF、EB、AR、MD/min、最佳刺激窗口解释原因。
决策：amplify、iterate、observe、rollback 或 kill。

full_client_delivery

完整交付才展开 19 模块：case_boundary、metric_horizon、theory_status、optimal_stimulation_fit、diagnosis_summary、density_curve_intent、free_energy_window、markov_blanket_coupling、growth_surprise_ladder、anti_habituation_plan、motivation_flow_gate、experiment_hypothesis、variant_matrix、instrumentation_dictionary、metric_plan、dashboard_spec、decision_rules、weekly_schedule、handoff_checklist。

schema_json

按 templates/experiment-plan.schema.json 输出结构化 JSON。未知信息保留 unknown，不要省略证据不足项。

Handoff

当输入来自 game-experience-analyzer，优先消费已有 ed-handoff，尤其是 issue_cards_for_ed、evidence_refs、suggested_primary_lever、secondary_noise、confounder_risk 和 unknowns。不要重做完整体验分析。

GameDesignOS runtime 提供规范名为 ed-handoff.schema.json 的跨 skill contract。本 skill 不创建平行 schema；独立使用时按 ED Handoff 最小契约接收，再编译成 weekly_ab_plan 或 schema_json。

References

按需读取，不要一次性加载所有文件：

证据门：references/evidence-gate.zh-CN.md
ED 基础框架：references/ed-framework.zh-CN.md
理论来源映射：references/theory-source-map.zh-CN.md
单机/手游指标周期门：references/metric-horizon-by-game-model.zh-CN.md
最佳刺激窗口：references/optimal-stimulation-window.zh-CN.md
GameFlow 与 SDT 体验门：references/flow-sdt-experience-gates.zh-CN.md
体验浓度公式：references/density-formula.zh-CN.md
诊断流程：references/density-diagnosis-workflow.zh-CN.md
自由能与马尔可夫毯镜头：references/free-energy-markov-blanket-lens.zh-CN.md
交互与预测反馈镜头：references/interaction-prediction-lens.zh-CN.md
一周实验 SOP：references/weekly-experiment-sop.zh-CN.md
主旋钮玩法库：references/lever-playbook.zh-CN.md
埋点与指标口径：references/telemetry-metric-dictionary.zh-CN.md
风险门和反例：references/retention-risk-gates.zh-CN.md

Templates

输入表：templates/experiment-intake.md
标准实验方案：templates/weekly-ed-experiment-plan.md
变体矩阵：templates/variant-matrix.md
ED 相对评分卡：templates/ed-scorecard.md
埋点字典：templates/instrumentation-dictionary.md
看板规格：templates/dashboard-spec.md
周复盘：templates/weekly-review.md
结构化 schema：templates/experiment-plan.schema.json

Output Gate

最终输出前检查：

是否先写 output_mode、case_boundary、evidence_gate，再写诊断。
是否证据不足时降级为 assumption_only 或 partial_evidence。
是否先区分 premium_single_player、mobile_liveops、hybrid 或 unknown。
是否为单机/买断制使用总旅程指标，为手游/liveops 使用每日和持续天数指标。
是否标注 theory_status: design_hypothesis。
是否先判断最佳刺激窗口，区分低刺激、过载、习惯化、低能动性、低意义感或 unknown。
是否把问题落到 CLP、SF、EB、AR、MD/min。
是否遵守“先判窗口，再降噪，再提质，后调频”。
是否每个变体只有一个主旋钮，并写清配置开关、owner、QA 和回滚。
是否包含埋点事件、字段、触发时机、看板过滤器和数据质量门。
是否预注册成功、观察、回滚和 Kill 条件。
如果涉及长线、赛季、刷子、肉鸽、UGC 或老玩家钝化，是否输出 anti_habituation_plan。
是否避免暗黑模式、误导奖励、焦虑红点、虚假倒计时和纯数值膨胀。
是否让输出密度匹配 mode，而不是每次都写完整大报告。

Files

SKILL.md and 34 other files (references) in game-experience-density-optimizer of DY-2026/GameDesignOS.

SKILL.md
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
… and 18 more

Open the folder on GitHubat commit ada4bf9

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Questions about Game Experience Density Optimizer

What does Game Experience Density Optimizer 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…. Game Experience Density Optimizer is an agent skill from DY-2026/GameDesignOS. 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.

When should I use Game Experience Density Optimizer?

Game Experience Density Optimizer fits situations like: converting game experience-density and engagement problems into rollback-ready ED experiments; tasks that involve Game design.

How do I install Game Experience Density Optimizer in Claude Code?

Run `npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a claude-code`. Or copy the skill folder (game-experience-density-optimizer in DY-2026/GameDesignOS) into .claude/skills/game-experience-density-optimizer in your project. Claude Code loads it when a task matches its description.

How do I install Game Experience Density Optimizer in Codex?

Run `npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a codex`. Or copy the skill folder (game-experience-density-optimizer in DY-2026/GameDesignOS) into .agents/skills/game-experience-density-optimizer in your project. Codex loads it when a task matches its description.

Can I use Game Experience Density Optimizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add DY-2026/GameDesignOS --skill game-experience-density-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/game-experience-density-optimizer, .gemini/skills/game-experience-density-optimizer, .github/skills/game-experience-density-optimizer and .opencode/skills/game-experience-density-optimizer in your project.

What does Game Experience Density Optimizer need to run?

SKILL.md names no scripts, command-line tools or credentials: Game Experience Density Optimizer is instructions for the agent only. Compatibility (from SKILL.md): 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。.

Does Game Experience Density Optimizer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Game Experience Density Optimizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Game Experience Density Optimizer use?

Game Experience Density Optimizer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Game Experience Density Optimizer use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 24k tokens, read only when the agent opens those files.

What are the alternatives to Game Experience Density Optimizer?

Skills that share tags, products or a category with Game Experience Density Optimizer: Threejs Gameplay Systems (valkor-ai/loom, 1.2k stars), Godot Gdscript Patterns (925236118/AlphaAgent, 103 stars), Game Asset Spec Writer (Donchitos/Claude-Code-Game-Studios, 26k stars) and Game Build From Design (zenstory-ai/novel-to-game, 841 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Game Experience Density Optimizer?

DY-2026 (a GitHub user) maintains it in DY-2026/GameDesignOS, which has 414 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on August 17, 2026.

Source: DY-2026/GameDesignOS on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Game Experience Density Optimizer

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Compatibility

Context cost

Safety

SKILL.md

Game Experience Density Optimizer

Mission

When To Use

Mode Router

Hard Gates

Core Model

Evidence Gate

Metric Horizon

Output Contracts

quick_ed_triage

weekly_ab_plan

instrumentation_plan

review_and_decide

full_client_delivery

schema_json

Handoff

References

Templates

Output Gate

Files

Compare with similar skills

Similar skills

More from DY-2026/GameDesignOS

Related

Questions about Game Experience Density Optimizer