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Chinese Copyright Application Okooo5km · thomasmoreai | DecimalAI

Legalby thomasmoreai

Chinese Copyright Application Okooo5km

中国软件著作权申请材料生成工具。申请表直接输出 Markdown 提交，源程序/用户手册/设计说明书三份生成 LaTeX 并编译为 PDF。自动分析项目代码，生成四份材料（前后各30页共60页源程序、含页眉页脚的用户手册和设计说明书、Markdown 申请表），并做版本号一致性、模块覆盖双向核验、字数限制等信息一致性校验。适用于微信小程序、Web应用、移动App、桌面应用等各类软件项目。当用户提到软件著作权、软著申请、版权登记时必须使用此 Skill。当用户要为任何软件项目准备著作权材料、生成软著文档时也应使用。

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

32%82%of test cases passed +50 pts

Without skill7 of 22

With skill18 of 22

11 fixed7 passed either way4 still failing0 broke

Measured on gemini-3.6-flash8/5/2026v121 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskMy machine running macOS does not have XeLaTeX installed. How should the environment check step guide me to install the necessary TeX packages?

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 thomasmoreai-chinese-copyright-application-okooo5km
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("thomasmoreai-chinese-copyright-application-okooo5km")
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 thomasmoreai-chinese-copyright-application-okooo5km

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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md https://app.decimal.ai/s/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

This gets SKILL.md only. The skill's 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/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/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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("thomasmoreai-chinese-copyright-application-okooo5km")
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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/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 7 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 thomasmoreai-chinese-copyright-application-okooo5km

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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md https://app.decimal.ai/s/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

This gets SKILL.md only. The skill's 7 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/thomasmoreai-chinese-copyright-application-okooo5km/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/thomasmoreai-chinese-copyright-application-okooo5km.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/5/2026
Source: thomasmoreai/legal-skills-open ↗
License: Apache-2.0

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

SKILL.md

中国软件著作权申请材料生成

核心原则

申请表：直接输出 Markdown（.md）文件提交，不参与 LaTeX 编译。
源程序、用户手册、设计说明书：生成 .tex 文件，用 XeLaTeX 编译为 PDF。

v1.1 关键变更

申请表载体由 LaTeX→PDF 改为 Markdown（PDF 文本提取带换行造成的录入问题）
主要功能字数：200 字 → 500–1000 字（建议 500–600）
新增版本号一致性核验（信息收集阶段就要查项目里的 version 字符串与目标版本号是否一致）
新增遗留代码识别（mockData/util.js 模板/logs 等不应进入鉴别材料）
设计说明书 §4 详细设计强制 6 段结构 + 模块覆盖双向对应
源程序 PDF 必须正好 60 页，提供 --front-lines / --back-lines 微调参数

快速开始

环境检查与安装引导
收集著作权人信息与关键参数
分析项目结构和代码
生成四份 LaTeX 文档 + 编译脚本
生成截图清单
运行一致性校验

工作流程

第零步：环境检查

申请表是 Markdown，不需要 LaTeX 环境也能拿到。LaTeX 环境只用于编译三份 PDF（源程序、用户手册、设计说明书）。

执行 xelatex --version 检测。

如已安装：继续下一步。

如未安装：告知用户当前可以先生成全部 Markdown/LaTeX 源文件 + Markdown 申请表，待安装 LaTeX 后再编译三份 PDF；询问是否立即安装。用户同意后按以下命令执行：

macOS：brew install --cask basictex && sudo tlmgr update --self && sudo tlmgr install ctex xecjk fancyhdr lastpage fancyvrb tabularx enumitem float xcolor longtable collection-fontsrecommended
Linux：sudo apt install texlive-xetex texlive-lang-chinese texlive-latex-extra texlive-fonts-recommended
Windows：引导用户安装 TeX Live 完整版（https://www.tug.org/texlive/）

第一步 A：版本号一致性核验（先做）

这是上次（集享 V1.0 软著流水号 2026R11L0626727）被打回补正的首条意见：申请表填 V1.0，但 utils/config.js 写的是 version: '1.1.1'，截图角标也是 V1.1.1，被审查员第一眼抓到。在收集软件参数之前必须先做这一步。

操作流程：

先问用户「这次软著要申请的版本号是多少」（首次申请通常 V1.0）。

然后扫描项目里所有版本号字符串：

# 配置文件中的 version 字段
grep -rEn '"version"\s*:\s*"[0-9]+(\.[0-9]+)+"' <项目路径> \
  --include='*.js' --include='*.ts' --include='*.json' \
  --exclude-dir=node_modules --exclude-dir=miniprogram_npm

# 页面 footer / 关于页里硬编码的版本号
grep -rEn 'V[0-9]+\.[0-9]+|版本.*[0-9]+\.[0-9]+' \
  <项目路径>/pages <项目路径>/components <项目路径>/src 2>/dev/null

如果用户已经提供了运行截图目录，提醒用户检查截图右下角/关于页/启动页是否带版本号。
把扫描结果以表格形式列出来：所有出现位置 + 对应版本字符串。
如果发现不一致（任何位置的版本号 ≠ 用户拟申请的版本号），必须停下来，让用户裁决：「最终申请使用哪个版本号？」并明确告知用户：在提交申请前必须把所有出现位置统一（含重新截图）。
全部一致才能继续下一步。

详见 requirements.md。

第一步：信息收集

分两轮收集信息。

第一轮：著作权人信息

先检查项目根目录或用户工作目录下是否存在 copyright-owner.json。

如已存在：读取文件内容，向用户展示并确认是否有变更。用户确认无误则直接使用，跳到第二轮。

如不存在：首次使用，需引导用户提供信息并保存。先问著作权人是个人还是企业，然后收集对应信息。

个人需提供：姓名、身份证号、地址、邮编、联系人、电话、邮箱。

企业需提供完整工商信息（参考 requirements.md「著作权人信息」章节）：公司全称、统一社会信用代码、企业类型、法定代表人、注册资本、住所地址、登记机关、营业执照登记日期、联系人、电话、邮箱。

收集完成后将信息保存为 copyright-owner.json，格式如下：

{
  "type": "企业",
  "name": "星辰科技（北京）有限公司",
  "credit_code": "91110108MA01EXAMPLE",
  "enterprise_type": "有限责任公司",
  "legal_representative": "张三",
  "registered_capital": "伍佰万元整",
  "address": "北京市海淀区中关村大街1号",
  "registration_authority": "北京市海淀区市场监督管理局",
  "license_date": "2022年6月15日",
  "contact": "",
  "phone": "",
  "email": ""
}

此文件后续申请时直接复用，著作权人信息只需填一次。

第二轮：软件关键参数

自动分析项目后预填建议值，让用户确认：

参数	说明
软件全称	必须以"软件"二字结尾（如"智慧记账软件"），有辨识度，所有文档中完全一致。用户提供的名称若未以"软件"结尾，自动补上
版本号	V1.0（首次申请统一使用 V1.0）
开发完成日期	软件实际完成开发的日期
首次发表日期	软件首次上线/发布的日期（≥ 开发完成日期）
源程序量	项目实际总代码行数（可自动统计后让用户确认）

日期约束：开发完成日期 ≤ 首次发表日期 < 申请日期。生成时自动校验，不合规则提醒用户修改。

第二步：项目分析与源程序生成

运行 scripts/analyze_and_generate_source.py 完成项目分析和源程序 .tex 生成：

python3 scripts/analyze_and_generate_source.py <项目路径> \
  --name "软件全称" \
  --version "V1.0" \
  --owner "著作权人名称" \
  --date "2025年4月30日" \
  --output copyright-materials/

脚本自动完成：

检测项目类型（微信小程序/Web/Node.js 等）
遍历代码文件，排除 node_modules 等无关目录
识别遗留代码候选项（mockData、util.js 模板、logs 等），输出到 JSON 的 legacy_candidates 字段
统计有效代码行数（排除空行和纯注释行），输出按文件类型的行数分布
按文件优先级排序，去除空行后提取前30页 + 后30页代码，生成源程序 .tex 文件
输出 project_analysis.json 供后续文档生成参考

遗留代码排除规则（默认识别 + 用户裁决）：

脚本会扫描以下模式作为遗留代码候选项，列出但不自动排除：

mockData*.js / mock*.js / fakeData*.js — 仅供开发期使用的假数据
utils/util.js — 微信小程序默认模板的 formatTime 样板（条件：文件 < 1KB 或仅含模板函数）
pages/logs/** — 微信开发者工具默认模板的日志页
空文件、占位文件、临时调试代码

Claude 在拿到 legacy_candidates 后，必须列出来询问用户是否排除，确认后通过 --exclude-pattern 参数重新运行脚本。这是为了避免源代码鉴别材料里出现"文档里写不出来对应模块"的代码（上次集享被打回的第二大原因）。

排版经验值（10pt + \small Verbatim + 上下2cm左右1.5cm边距，基于实测校准）：

参数	值	说明
标准页容量	50行/页	满页代码行数
首页容量	40行	标题区域占约10行空间
后30页首页	48行	分隔标题占约2行空间
前30页目标	1490行	40 + 29×50（可用 `--front-lines` 覆盖）
后30页目标	1498行	48 + 29×50（可用 `--back-lines` 覆盖）
总目标	2988行	去除空行后的代码行数

代码量不足2988行时，脚本使用全部代码，不强凑60页。

脚本输出的 total_lines_effective 即为申请表中的「源程序量」。

60 页硬约束（重要）：源程序 PDF 编译完后必须正好 60 页（除非项目代码本就不足 60 页）。多 1 页或少 1 页都属于格式不合规。编译后用 pdfinfo <软件全称>源程序.pdf | grep Pages 核对；如不对，调整 --front-lines / --back-lines 参数（默认 1490 / 1498，每次 ±2~5 行试探）重新生成并编译。

第三步：生成四份文档

所有文档的 LaTeX 编写规范见 references/requirements.md。每份文档的模板见 references 目录下对应的 .tex 模板文件。

输出文件夹与命名

输出目录：copyright-materials/

生成文档前先创建目录结构：

mkdir -p copyright-materials/images

文件命名规范为「软件全称 + 文档类型」，举例：

智慧记账软件著作权登记申请表.md（Markdown 直接提交，不编译）
智慧记账软件源程序.tex / .pdf
智慧记账软件用户手册.tex / .pdf
智慧记账软件设计说明书.tex / .pdf
build_all.sh
images/（图片目录，用户后续在此放入截图）

3.1 著作权登记申请表（Markdown）

模板：application-form-template.md

载体：直接生成 .md 文件，不参与 LaTeX 编译。申请表本身没有格式硬要求，Markdown 表格 + 段落即可。

格式要求：

用 Markdown 表格组织字段（| 字段 | 内容 |）
主要功能段落写在表格外，分段展开
分为：基本信息、著作权人信息、开发者信息、软硬件环境信息、软件基础信息、软件技术特点、软件分类、备注
申请表字段有严格字数限制（详见 requirements.md「字数限制」章节），生成后必须校验

字数限制速查：

硬件环境/操作系统/开发工具/运行平台/支撑环境：各 ≤50 字符
开发目的/面向领域：各 ≤50 字
主要功能：500–1000 字（建议 500–600 字以减少出错）
技术特点：≤100 字

3.2 源程序

由第二步的脚本自动生成，无需手动编写。生成规则详见 requirements.md「源程序文档要求」。

3.3 用户手册

模板：user-manual-template.tex

内容结构：

软件简介（概述 + 主要特点）
功能概述（功能列表 + 适用场景）
安装与使用说明（系统要求 + 进入方式 + 登录说明）
主要功能说明（每个功能的操作步骤，配截图）
注意事项
技术支持

截图要求：

定义 \screenshot{文件名}{标题} 宏，宽度 0.35\textwidth
对尚未提供的截图，LaTeX 中仍放置 \screenshot 调用
同时输出截图清单（见第四步）

3.4 设计说明书

模板：design-doc-template.tex

内容结构：

软件概述（简介 + 开发背景 + 设计目标）
需求分析（功能/性能/安全需求）
总体设计（系统架构 + 模块划分 + 技术选型 + 运行环境）
详细设计（各模块按 6 段结构展开，配截图）
数据结构设计
接口设计（内部接口 + 外部 API 列表）
界面设计（配截图）
安全设计
测试设计

§4 详细设计的硬规则（双向对应 + 6 段结构）：

这是上次（集享 V1.0 软著）被打回补正的第二大原因。详见 requirements.md 设计说明书与源代码对应规则。

双向对应：每个在源代码鉴别材料中出现的模块，在 §4 详细设计中必须有对应小节；反之亦然。
小节命名：每个 \subsection 标题应与项目实际目录或文件名对应（如 pages/index、utils/api.js、utils/marketHelper.js），不要起架空的中文名。
6 段结构：每个模块按以下 6 段展开（不再是旧版的「模块功能 + 处理流程」两段）：
1. 模块职责
2. 输入与输出
3. 关键算法与处理逻辑
4. 状态与缓存管理
5. 与其他模块的依赖关系
6. 处理流程

参考 design-doc-template.tex 已内置 6 段结构示例。

第四步：截图管理

分析项目代码（页面文件、弹窗组件、模态框等），生成截图清单 screenshot-list.md：

| 编号 | 文件名 | 描述 | 触发方式 | 使用位置 |
|------|--------|------|----------|----------|
| 01 | 图1-首页空状态.png | 首页空状态 | 首次进入首页 | 用户手册§3、设计说明书§4.1 |
| 02 | 图2-搜索结果列表.png | 搜索结果 | 输入关键词搜索 | 用户手册§4.1、设计说明书§4.1 |

LaTeX 文档中 \screenshot{文件名}{标题} 宏已内置 images/ 路径前缀，用户只需将截图放到 images/ 目录，文件名与清单中一致即可，编译时自动找到。

交付截图任务给用户

文档初版生成完毕后，暂停自动流程，向用户输出以下操作指引（将 <输出目录> 替换为实际路径）：

文档初版已全部生成完毕，接下来需要你手动截取软件界面截图。

截图保存路径：<输出目录>/images/

请按照上方截图清单，逐一截取对应的软件界面截图，保存到该目录。文件名与清单中的「文件名」列保持一致（如 图1-首页.png）。

截图添加完成后回复我一声，我会重新编译所有文档生成最终 PDF。

等用户确认截图完成后，再继续执行第五步（编译）和第六步（校验）。

第五步：编译脚本

生成 build_all.sh，模板见 references/build-script-template.sh。

脚本要点：

设置 PATH 包含 TeX 路径
DOCS 数组只编译 3 项（用户手册、设计说明书、源程序），申请表是 Markdown 不参与编译
每个 .tex 文件编译两遍（确保页码引用正确）
清理 .aux/.log/.out 等临时文件
输出每个 PDF 的文件大小

编译完成后，用 pdfinfo <软件全称>源程序.pdf | grep Pages 核对源程序 PDF 是否正好 60 页。若不是 60 页，调整 --front-lines / --back-lines 参数重新生成 .tex 后再编译。

第六步：一致性校验

生成完所有文档后，自动校验以下内容并输出校验报告：

校验项	校验位置
版本号一致性（首项）	项目源代码（package.json / config.js / 关于页 footer）、运行截图、申请表、各文档页眉与正文
软件全称	申请表、源程序页眉和首页、用户手册标题/页眉、设计说明书标题/页眉
版本号	所有文档的页眉和正文中
源程序量	申请表中的数字 = 源程序文档首页标注的数字
著作权人名称	每个文档首页
日期	各文档中引用的日期一致
主要功能字数	500 ≤ 字数 ≤ 1000（从申请表 Markdown 解析）
其他字段字数	开发目的/面向领域 ≤50 字、技术特点 ≤100 字、软硬件环境字段 ≤50 字符
模块覆盖（双向）	设计说明书 §4 各模块 ↔ 源程序前30页+后30页中出现的代码文件
源程序页数	`pdfinfo` 显示正好 60 页（除非项目代码不足 60 页）

字段从 Markdown 解析：申请表是 Markdown，校验脚本通过解析表格行（| 字段 | 内容 |）和「软件的主要功能」段落正文来取数；与旧版 LaTeX 解析逻辑不同。

校验通过则输出"✓ 一致性校验通过"，否则列出不一致项。

统一 LaTeX 排版规范

以下规范适用于申请表、用户手册、设计说明书（源程序文档例外，有专门规范）：

页面设置

\documentclass[a4paper,12pt]{article}
\usepackage[UTF8,heading=true]{ctex}
\usepackage[top=2.5cm,bottom=2.5cm,left=2.5cm,right=2.5cm]{geometry}

页眉页脚

\pagestyle{fancy}
\fancyhf{}
\fancyhead[C]{软件全称V1.0\quad 文档类型}
\fancyfoot[C]{第 \thepage\ 页\quad 共 \pageref{LastPage} 页}
\renewcommand{\headrulewidth}{0.5pt}
\renewcommand{\footrulewidth}{0.5pt}
\fancypagestyle{plain}{\pagestyle{fancy}}

页眉格式：软件全称V版本号文档类型（如：智慧记账软件V1.0 用户手册）页脚格式：第 X 页共 XX 页 页眉页脚均有分隔线（0.5pt）

中文排版

\setlength{\parindent}{2em}          % 段首缩进 2 字符
\setlength{\parskip}{0.3em}
\renewcommand{\baselinestretch}{1.3}  % 1.3 倍行距

列表

\begin{itemize}[leftmargin=2em]
\begin{enumerate}[leftmargin=2em]

截图宏

在文档 preamble 中设置图片搜索路径，并定义截图宏：

\graphicspath{{images/}}

\newcommand{\screenshot}[2]{%
  \begin{figure}[H]
    \centering
    \includegraphics[width=0.35\textwidth]{#1}
    \caption{#2}
  \end{figure}
}

调用时只需 \screenshot{图1-首页.png}{首页}，LaTeX 会自动在 images/ 目录下查找图片文件。

文档首页结构

每份文档首页统一格式：

\begin{center}
  {\LARGE\bfseries 软件全称\quad 文档类型}
  \vspace{0.8em}
  {\large 著作权人：公司全称}
  \vspace{0.3em}
  软件版本：V1.0\qquad 发布日期：YYYY年M月D日
\end{center}

特殊项目类型处理

微信小程序

软件分类：移动应用软件——小程序
运行平台：iOS、Android、Windows、macOS、HarmonyOS（微信客户端）
运行支撑环境：微信客户端（支持小程序的任意版本）
技术特点类型选择：小程序

Web 应用

软件分类：应用软件——Web应用
运行平台：Windows、macOS、Linux（浏览器）
运行支撑环境：Chrome/Firefox/Safari 等浏览器

移动 App

软件分类：移动应用软件——App
运行平台：根据实际支持列出（iOS/Android/HarmonyOS 等）

跨平台项目

Electron 应用：Windows、macOS、Linux
Flutter/React Native：iOS、Android（如支持桌面端则加上 Windows、macOS、Linux）

LaTeX 环境依赖

环境检查与安装流程见「第零步」。

注意：申请表是 Markdown，不需要 LaTeX，可独立完成。LaTeX 仅用于源程序、用户手册、设计说明书三份 PDF 的编译。

必需宏包：ctex、xecjk、fancyhdr、lastpage、fancyvrb、tabularx、enumitem、float、xcolor、longtable、graphicx、hyperref、array、geometry

编译命令：xelatex -interaction=nonstopmode 文件名.tex（运行两遍）

参考文档

申请要求详细说明
申请表 Markdown 模板
源程序 LaTeX 模板
用户手册 LaTeX 模板
设计说明书 LaTeX 模板
编译脚本模板

About this skill

Automatic

Legal & Contracts pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 10/8/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

thomasmoreai / legal-skills-open

cn/ip/skills/chinese-copyright-application-okooo5km/SKILL.md

View on GitHub ↗

License MITImported 10/8/2026Version v1Bundled 7 files · scripts

Illustrative Want to see what thomasmoreai/chinese-copyright-application-okooo5km 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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中国软件著作权申请材料生成

核心原则

v1.1 关键变更

快速开始

工作流程

第零步：环境检查

第一步 A：版本号一致性核验（先做）

第一步：信息收集

第一轮：著作权人信息

第二轮：软件关键参数

第二步：项目分析与源程序生成

第三步：生成四份文档

输出文件夹与命名

3.1 著作权登记申请表（Markdown）

3.2 源程序

3.3 用户手册

3.4 设计说明书

第四步：截图管理

交付截图任务给用户

第五步：编译脚本

第六步：一致性校验

统一 LaTeX 排版规范

页面设置

页眉页脚

中文排版

列表

截图宏

文档首页结构

特殊项目类型处理

微信小程序

Web 应用

移动 App

跨平台项目

LaTeX 环境依赖

参考文档

SkillSafety

Source

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