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Game Experience Density Optimizer · dy-2026 | DecimalAI

Industryby dy-2026

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。

Maintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

45%86%of test cases passed +41 pts

Without skill10 of 22

With skill19 of 22

10 fixed9 passed either way2 still failing1 broke

Measured on gemini-3.6-flash8/21/2026v1

Track record

Agent runs: 0; All time
Forks: 1

See one task, with and without the skill

One test taskIn our atmospheric horror game, playtesters state that walking through the abandoned space station feels visually flat, silent, and disconnected from the dark environment, making exploration boring. Identify the core formula item to boost and propose two minimal tweaks.

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add dy-2026-game-experience-density-optimizer
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install dy-2026-game-experience-density-optimizer

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/dy-2026-game-experience-density-optimizer/SKILL.md https://app.decimal.ai/s/dy-2026-game-experience-density-optimizer/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/dy-2026-game-experience-density-optimizer/SKILL.md

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add dy-2026-game-experience-density-optimizer

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("dy-2026-game-experience-density-optimizer")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add dy-2026-game-experience-density-optimizer

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md https://app.decimal.ai/s/dy-2026-game-experience-density-optimizer/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-experience-density-optimizer/SKILL.md

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/dy-2026-game-experience-density-optimizer/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/dy-2026-game-experience-density-optimizer.json

This gets SKILL.md only. The skill's 23 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/21/2026
Source: dy-2026/gamedesignos ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

references/

templates/

SKILL.md

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：

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：

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

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。

When it activates

Automatically, when a task matches. Use when converting game experience-density and engagement problems into rollback-ready ED experiments.

About this skill

Automatic

Product Management pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

dy-2026 / gamedesignos

game-experience-density-optimizer/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 23 files

Illustrative Want to see what dy-2026/game-experience-density-optimizer would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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SkillScore

Safety

What it changes

Track record

Use this skill

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

When it activates

SkillSafety

Source

Related skills

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