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Game Design Proposal Writer · dy-2026 | DecimalAI

Documentationby dy-2026

Game Design Proposal Writer

将调研、概念架构、体验诊断、验证计划、脑图/xmind/系统设计图、已有策划案和生产约束收束为商业游戏策划案、独立游戏设计案、立项评审稿、发行/投资 pitch 或 vertical slice 设计文档。适用于一句话创意先经 game-concept-architect 生成概念契约后再成案、玩法/系统脑图转可落地策划案、审核并改进已有策划案。强调证据边界、受众与商业适配、scope gate、里程碑、风险、决策请求和下一步投入条件；不替代上游创意生成或体验诊断。

Maintained

SkillScore

SkillScore59/100

Test results 59

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%59%of test cases passed +45 pts

Without skill3 of 22

With skill13 of 22

10 fixed3 passed either way9 still failing0 broke

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

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task这里有一份玩家实况录屏的视频诊断报告和用户弹幕反馈，我们想诊断游戏第二关在视觉引导和打击反馈上的问题，请帮我分析这份视频证据并输出试玩问题诊断卡片。

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-design-proposal-writer
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-design-proposal-writer")
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-design-proposal-writer

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-design-proposal-writer/SKILL.md https://app.decimal.ai/s/dy-2026-game-design-proposal-writer/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/dy-2026-game-design-proposal-writer/SKILL.md

This gets SKILL.md only. The skill's 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/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-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/dy-2026-game-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/dy-2026-game-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/dy-2026-game-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/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-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/dy-2026-game-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/dy-2026-game-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/dy-2026-game-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/dy-2026-game-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/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-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer")
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-design-proposal-writer/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/dy-2026-game-design-proposal-writer/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 20 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-design-proposal-writer

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-design-proposal-writer/SKILL.md https://app.decimal.ai/s/dy-2026-game-design-proposal-writer/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-game-design-proposal-writer/SKILL.md

This gets SKILL.md only. The skill's 20 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-design-proposal-writer/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/dy-2026-game-design-proposal-writer.json

This gets SKILL.md only. The skill's 20 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 Design Proposal Writer

使用本 skill 时，把已经存在的调研材料、创意方案、玩家承诺、竞品证据、体验诊断、ED 实验、生产约束和商业目标，整理成可以被制作人、老板、发行、投资人、合作方或独立团队成员评审的游戏策划案/独游设计案。

它不是普通 GDD 生成器，也不是把一句话创意扩写成长文的工具。创意还没有被拆出 design nucleus 时，优先提醒使用 game-concept-architect；样本体验还没有证据层时，优先提醒使用 game-experience-analyzer；需要一周体验实验时，优先提醒使用 game-experience-density-optimizer。本 skill 的核心工作是把上游产物变成一份有受众、有论证、有取舍、有风险、有下一步决策的文档。

如果用户给的是一句话创意，但明确要求“写策划案/立项案/独游设计案/pitch/GDD”，不要直接跳过上游。先调用 game-concept-architect 产出 concept brief、player-promise-contract、core loop、scope gate 和 validation plan，再用本 skill 成案。最终文档要标注“上游概念由本轮自动生成，证据等级仍为 assumption / needs_research”。

用户可在自己的环境中处理真实项目、私有项目或客户项目。准备公开仓库案例时，只能使用 synthetic、公开或明确授权的材料，并在发布前执行 Human Gate。

什么时候使用

当用户想写、改写、压缩或评审以下材料时，使用本 skill：

商业游戏策划案、立项案、产品方案、项目建议书。
独立游戏设计案、GDD、Steam/发行 pitch、Demo/Vertical Slice 方案。
给老板、制作人、发行、投资人、合作方或团队看的项目说明。
把调研报告、创意方案、竞品分析、玩家承诺和验证计划整合成一份文档。
把一句话创意先经 game-concept-architect 拆成概念契约，再整理成正式策划案。
把玩法脑图、系统设计脑图、xmind、OPML、Markdown 大纲、Word/Excel 导出的设计表，整理成可落地策划案。
审核、重写、压缩或改进已有商业策划案、独游设计案、GDD、pitch outline、vertical slice 文档。
把过长、幻想化、没有决策点的设计案改成可评审版本。
需要说明平台、商业模式、目标用户、核心循环、系统范围、制作成本、里程碑和风险。

以下请求要强触发：

写一份商业游戏策划案
写一份独游设计案
写 GDD / Game Design Document
写立项案 / 项目方案 / 产品方案 / pitch deck 文案
给发行 / 投资人 / 老板看的游戏方案
把这个创意整理成正式策划案
只有一句话创意，帮我写成策划案 / 立项案 / pitch
这个 xmind / 脑图 / 系统图帮我做成可落地策划案
审核这份策划案 / 改进这份 GDD / 帮我把已有方案改成能评审的版本
把调研和创意合成一份文档
改成能立项评审的版本
做 vertical slice 文档 / demo 方案

不适用场景

如果用户只有一句话创意，并且目标是判断创意是否成立，应优先使用 game-concept-architect。如果用户同时要求产出策划案，本 skill 应作为第二步，在 game-concept-architect 输出后接续成案。

如果用户给的是录屏、PV、截图或试玩反馈，并希望诊断体验问题，应优先使用 game-experience-analyzer。

如果用户要的是首局节奏、反馈、具身感、氛围、认知负荷、留存或一周 A/B 实验，应优先使用 game-experience-density-optimizer。

如果用户只是要宣传文案、商店页短描述、众筹页面、PR 文案或广告脚本，本 skill 可以给文档中的定位和承诺，但不应替代专门的 marketing copywriting。

项目内上游产物审核门

写案前先做一次项目内协作自检，但不要把本 skill 退化成泛品类设计回答。本 skill 只负责把项目内其他 skill 或用户已有材料收束成评审文档，不继承项目外的全局设计总师口径。

primary_category：先判断主品类/副品类、平台、商业模型和项目阶段。
core_player_action：先看玩家反复做什么，再看题材、系统和文案。
anti_pollution：商业案、独游案、Steam pitch、手游立项、Demo 文档不要互相套模板。
proof_of_play：对外 pitch 必须说明 playable build、gameplay video、demo、vertical slice、玩家反馈或指标的状态。
scope_feasibility：所有系统、内容、预算和里程碑都必须受团队能力约束。
negative_example：至少写一个看似相似但不应采用/不应投递的反例。

如果用户的真实需求还停留在一句话创意、媒体样本诊断或体验浓度实验，应先交给项目内对应 skill 形成稳定上游产物，再由本 skill 负责整理成评审文档：

game-concept-architect：输出 concept brief、player-promise-contract、core loop、scope gate 和 validation plan。
game-experience-analyzer：输出 evidence-index、issue-card、sample boundary 和体验诊断。
game-experience-density-optimizer：输出 ED handoff、一周实验、埋点字段、决策规则和 rollback 条件。
game-design-source-curator：输出 source notes、reference boundary 和可追溯知识条目。

三类新增触发流程

一句话创意 -> 自动概念架构 -> 策划案

当输入只有一句话创意但目标是策划案时，按两段式执行：

先用 game-concept-architect 生成 concept brief、player-promise-contract、core loop、scope gate、validation plan。
再用本 skill 选择输出模式，生成商业策划案、独游设计案、一页 memo、pitch outline 或 vertical slice 文档。
在 Source Artifact Inventory 里标注 generated_this_turn_by_game-concept-architect，不要伪装成用户已提供证据。

脑图 / xmind / 系统设计图 -> 可落地策划案

当用户给出 xmind、脑图截图、OPML、Markdown 大纲、Word/Excel 导出、玩法系统表或系统设计结构图时，先读取 references/mindmap-and-existing-proposal-input.zh-CN.md：

保留脑图层级，不要把节点直接散文化。
识别核心循环、系统模块、资源流、玩家行为、产出/消耗、成长线、内容范围、未决问题和冲突节点。
把“想法树”改成“制作树”：MVP、Vertical Slice、Demo、Release、Post-launch、Cut。
输出可落地策划案时必须补 owner、里程碑、验证标准和砍项理由。

审核 / 改进已有策划案

当用户给出已有策划案、GDD、pitch、立项文档或 vertical slice 文档并要求审核、重写、改进、压缩、变正式时：

先输出 proposal review，指出缺失、过度承诺、scope 风险、证据伪装、读者不匹配、无法执行的里程碑。
再输出 revision plan，说明保留什么、重写什么、删除什么、需要补什么证据。
用户要求直接改时，给出改写版，并保留 Change Log 和 Remaining Unknowns。

强制顺序

在写任何完整策划案前，必须按下面顺序完成。用户要求短稿时可以压缩，但不要跳过边界、证据、scope 和决策门。

proposal intake：确认文档受众、使用场景、输出模式、平台、商业模式、项目阶段和材料来源。
source artifact inventory：列出已有材料，例如 concept brief、player-promise-contract、validation plan、evidence-index、issue-card、ed-handoff、market/source notes、production profile。
case visibility：记录可见性、输出去向和是否需要脱敏。它只用于输出管理，不限制用户在本地环境处理真实项目。
document purpose：明确这份文档要推动什么决策，而不是只追求完整。
evidence and assumption boundary：把事实、引用、用户提供信息、模型推断、assumption 和 unknown 分开。
proposal mode selection：选择商业游戏策划案、独游设计案、一页决策 memo、发行 pitch outline 或 vertical slice design doc。
proposal spine：先写一句话定位、玩家承诺、核心循环、目标玩家、平台/商业假设和差异化边界。
scope and production gate：区分 MVP、Vertical Slice、Demo/公开试玩、Release、Post-launch、暂不开发和建议砍掉。
risk and validation narrative：写清最大风险、验证动作、通过/失败标准、下一步投入条件。
document assembly：再按选定模板写成正式文档。
quality gate：检查是否可评审、可执行、可删减、可验证，且没有把未知写成确定事实。

默认工作流

读取 templates/proposal-intake.md，建立输入边界。若用户没有提供足够材料，不要停下；先产出 assumption draft，并最多提出 3 个会改变文档方向的问题。
读取 references/proposal-intake-router.zh-CN.md，判断输出模式。
如果用户给出上游产物，先抽取可复用 contract：玩家承诺、核心循环、验证计划、证据索引、问题卡、ED 实验、scope gate。不要改写成无法追溯的散文。
如果用户给的是脑图/xmind/OPML/Markdown 大纲/系统图/已有策划案，读取 references/mindmap-and-existing-proposal-input.zh-CN.md，先做结构提取或 review，再成案。
读取 references/evidence-assumption-boundary.zh-CN.md，把每个关键判断标成 provided、derived、external_evidence、assumption、unknown 或 needs_research。
读取 references/commercial-game-proposal-framework.zh-CN.md 或 references/indie-design-dossier-framework.zh-CN.md，根据文档目标建立章节结构。
读取 references/audience-business-scope-gate.zh-CN.md，检查目标玩家、平台、商业模式和 scope 是否互相支持。
当任务涉及发行、投资、平台、Steam、孵化器、pitch deck、store page 或外部提交时，读取 references/publisher-platform-proof-gate.zh-CN.md，补齐 proof of play、publisher/platform fit、ask、budget、timeline 和 recheck gate。
读取 references/scope-and-milestone-gates.zh-CN.md，输出里程碑和下一步投入条件。
读取 references/pitch-document-quality-gate.zh-CN.md，执行最终文档门。
按任务选择模板：

templates/commercial-game-proposal.md
templates/indie-design-dossier.md
templates/one-page-decision-memo.md
templates/publisher-pitch-outline.md
templates/vertical-slice-design-doc.md
templates/proposal-evidence-ledger.md
templates/milestone-gate-plan.md
templates/risk-register.md
templates/existing-proposal-review.md

输出模式

模式	默认触发	交付重点
`commercial_product_proposal`	商业游戏、手游、网游、小游戏、F2P、内部立项、老板评审	产品定位、目标玩家、核心体验、系统与商业闭环、平台渠道、制作成本、指标、风险和立项决策
`indie_design_dossier`	独立游戏、Steam、主机/PC 买断、solo/small team、发行 pitch	创作命题、玩家幻想、可卖点、最小内容策略、vertical slice、制作边界、社区/发行验证和风险
`one_page_decision_memo`	用户要求快速判断、老板只看一页、会前材料	结论、为什么值得看、最大风险、最小验证、需要什么决策
`publisher_pitch_outline`	发行、投资、合作方、比赛/孵化器	可被外部人快速理解的 pitch 结构、卖点证明、demo 计划、团队可信度和请求
`vertical_slice_design_doc`	Demo、first playable、vertical slice、下一阶段制作	切片目标、功能边界、体验路径、资产/系统清单、里程碑、测试和 Go/No-Go
`proposal_review_and_rewrite`	审核/改进已有策划案、GDD、pitch、立项文档	问题诊断、证据/范围/读者/执行性修正、改写计划、可选改写版

如果用户说商业游戏策划案，默认使用 commercial_product_proposal。如果用户说独游设计案，默认使用 indie_design_dossier。如果用户说只要一页或先给老板看，默认使用 one_page_decision_memo。如果用户说给发行或投资人看，默认使用 publisher_pitch_outline。如果用户说做 demo 或 vertical slice，默认使用 vertical_slice_design_doc。

如果用户说“审核/改进/重写已有策划案/GDD/pitch”，默认使用 proposal_review_and_rewrite，除非用户明确只要最终改写版。

Case Visibility

case_visibility 只帮助 agent 管理输出边界，不限制用户在本地环境处理真实、私有或客户项目。

可选字段：

case_visibility: private_user_work | public_repo_example | public_article | client_confidential | synthetic_case | unknown
output_destination: private_notes | repo_example | public_post | client_delivery | publisher_pitch | internal_review | unknown
redaction_required: true | false | unknown

当 output_destination=repo_example 时，仓库 examples、assets、showcases、eval cases 只能使用 synthetic cases、公开材料或明确 cleared materials，并在发布前执行 Human Gate。

证据规则

没有来源时，不要写成市场事实。
没有用户提供团队规模、周期、预算或技术能力时，不要承诺大型在线、开放世界、实时多人、长期 live ops、大量剧情分支或高精度资产量产。
没有当前平台、商店、发行、竞品、买量或政策证据时，只能写成 assumption 或 needs_research。
发行方、投资方、孵化器和平台规则会变化；真实投递前必须重新检查目标页面，状态写成 verified_current、needs_recheck 或 unknown。
对外 pitch 不能只靠概念图、设定或 AI 视觉稿。没有 playable build、gameplay video、demo、vertical slice、玩家反馈或指标时，必须把 proof 写成 missing。
外部调研不是固定动作。只有当调研结果会改变定位、目标玩家、平台/商业模型、scope gate、风险等级或 Go/No-Go 时，才建议执行。
文档可以有愿景，但愿景必须和可验证路径分开。
参考游戏只能作为行为结构、受众动机、体验节奏或生产边界的参照；不要复制设定、术语、阵营、剧情、关卡或商业表达。
脑图、xmind 和系统图只说明“结构关系”，不自动等于已验证设计。节点必须转成玩家行为、系统责任、scope、风险和验证项。

商业游戏策划案硬规则

商业游戏策划案必须让评审者知道三件事：为什么值得做、做成什么算成立、下一步要花多少钱或多少人力去验证。

受众不能只写年龄、性别或泛二次元/泛休闲；必须写玩家行为、动机、付出意愿、拒绝点和相邻产品。
商业模式不能先行吞掉核心体验；广告、IAP、订阅、买断、赛季、付费皮肤、UGC 或 LiveOps 都要说明和玩家承诺的关系。
系统列表必须回连核心循环，不允许堆功能名。
指标不能只写 DAU、留存、收入；必须说明这些指标如何验证玩家承诺、循环强度或商业假设。
立项建议必须包含最大风险、最小验证、通过标准、失败标准和下一步投入条件。

独游设计案硬规则

独游设计案必须保护创作锋利度和制作边界。不要把商业游戏的全系统模板套进小团队项目。

先写创作命题、玩家幻想、可卖点和玩法承诺，再写系统清单。
目标玩家要写成细分动机和拒绝点，例如喜欢短局解谜中的顿悟、喜欢低压探索中的环境叙事、拒绝刷数值或强操作压力。
内容策略必须说明少量资产如何产生足够变化，而不是承诺大量关卡、剧情、角色或区域。
美术和音频方向要写 production boundary：哪些风格可以用小团队做，哪些会拖垮。
Demo/Vertical Slice 要证明最独特的体验，而不是做一个正式版小切片。
发行、社区、愿望单、展会、众筹、媒体、创作者传播等内容必须标注为当前证据或待调研，不得写成确定机会。

最低合格输出

commercial_product_proposal 至少包含：

## Case Visibility
## Proposal Intake
## Source Artifact Inventory
## Executive Summary
## Product Positioning
## Target Player and Desire
## Player Promise
## Core Loop and Key Systems
## Platform and Business Fit
## Scope Gate
## Production Feasibility
## Milestone and Gate Plan
## Metrics and Validation Plan
## Risk Register
## Decision Request
## Evidence and Assumption Ledger

indie_design_dossier 至少包含：

## Case Visibility
## Proposal Intake
## Source Artifact Inventory
## Creative Thesis
## Store-Page Promise
## Target Player and Niche
## Player Verbs and Core Loop
## Design Pillars
## Content Minimalism Strategy
## Art and Audio Direction Boundary
## Vertical Slice Plan
## Production Plan
## Release and Community Validation
## Risk Register
## Next Investment Decision
## Evidence and Assumption Ledger

one_page_decision_memo 至少包含：

## Decision Memo
## Why This, Why Now
## Core Promise
## Biggest Assumptions
## Minimum Validation
## Scope and Cost Boundary
## Recommendation
## Decision Needed

publisher_pitch_outline 至少包含：

## Pitch Goal
## One-Line Pitch
## Market/Reference Boundary
## Player Promise
## Proof of Play
## Publisher / Platform Fit
## Demo or Vertical Slice Plan
## Production Credibility
## Ask, Budget, and Timeline
## Risk and Validation
## Materials Checklist
## Recheck Before Submission

vertical_slice_design_doc 至少包含：

## Slice Goal
## Experience Path
## Must-Prove Assumptions
## Build Scope
## Feature Priority
## Asset and Content Scope
## Milestones
## Playtest Protocol
## Go/No-Go Criteria
## Handoff Checklist

proposal_review_and_rewrite 至少包含：

## Review Target
## Reader and Decision Fit
## Structure Diagnosis
## Evidence and Assumption Problems
## Scope and Production Risks
## Missing Proof / Missing Decisions
## Revision Plan
## Suggested Rewrite
## Change Log
## Remaining Unknowns

输入不足时的处理

如果缺失信息很多，不要直接停下。先写低置信度版本，并明确标注 assumption 和 unknown。只有当缺失信息会改变文档模式或关键判断时，才提出澄清问题。最多问三个问题。

优先追问这些会改变文档方向的问题：

文档受众是谁：内部老板、制作人、发行、投资人、团队成员，还是自己梳理？
目标平台和商业模式是否已定？
团队规模、周期、预算或当前项目阶段是什么？

如果用户要求继续，就带着明确假设往前推进。

输出门

最终输出前检查：

是否先写文档目标，再写章节内容。
如果输入是一句话创意，是否先形成 game-concept-architect 上游产物，再进入本 skill 成案。
如果输入是脑图/xmind/系统图，是否保留层级并转成可执行 scope、里程碑和验证项。
如果输入是已有策划案，是否先 review 再改写，而不是直接润色。
是否区分已提供事实、外部证据、推断、assumption、unknown 和 needs_research。
是否从玩家承诺和核心循环组织文档，而不是从功能清单堆起。
商业案是否说明目标玩家、商业模式、平台渠道、制作成本、指标和立项请求。
独游案是否保护创作命题、最小内容策略、制作边界和 demo 验证。
是否有 scope gate，而不是把所有想法都塞进正式版。
是否有里程碑、owner、成功标准、失败标准和下一步投入条件。
是否说明最大风险和最小验证。
是否避免没有证据的市场断言、收入承诺、愿望单承诺或买量判断。
对外 pitch 是否说明发行方/平台匹配、proof of play、ask、budget、timeline 和投递前重新检查项。
是否能被目标读者在 5 分钟内理解项目为什么值得继续看。

About this skill

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dy-2026 / gamedesignos

game-design-proposal-writer/SKILL.md

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License MITImported 8/3/2026Version v1Bundled 20 files

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Game Design Proposal Writer

什么时候使用

不适用场景

项目内上游产物审核门

三类新增触发流程

一句话创意 -> 自动概念架构 -> 策划案

脑图 / xmind / 系统设计图 -> 可落地策划案

审核 / 改进已有策划案

强制顺序

默认工作流

输出模式

Case Visibility

证据规则

商业游戏策划案硬规则

独游设计案硬规则

最低合格输出

输入不足时的处理

输出门

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