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lubanskill · leoyeai | DecimalAI

Databy leoyeai

lubanskill

操作Luban游戏配置表，支持枚举、Bean、数据表的增删改查。【强制使用场景】当用户提到以下任意关键词时必须使用此技能：配置表、数据表、道具表、技能表、奖励表、活动表、Excel表、xlsx、枚举、Bean、字段、数据行、表结构、导表、Luban、游戏配置、修改配置、改表、新增道具、添加技能、策划配置、游戏数据、配置数据。即使用户没有明确说"Luban"，只要是编辑游戏配置数据，也要使用此技能。

Never made a case worse

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

23%82%of test cases passed +59 pts

Without skill5 of 22

With skill18 of 22

13 fixed5 passed either way4 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v119 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskAn Excel file is named `#Item-道具表.xlsx` in the Luban datas folder. What table name and table comment are automatically derived from this filename?

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 leoyeai-luban-skill
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("leoyeai-luban-skill")
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 leoyeai-luban-skill

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/leoyeai-luban-skill/SKILL.md https://app.decimal.ai/s/leoyeai-luban-skill/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-luban-skill/SKILL.md

This gets SKILL.md only. The skill's 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/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/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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("leoyeai-luban-skill")
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/leoyeai-luban-skill/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-luban-skill/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 4 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 leoyeai-luban-skill

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/leoyeai-luban-skill/SKILL.md https://app.decimal.ai/s/leoyeai-luban-skill/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-luban-skill/SKILL.md

This gets SKILL.md only. The skill's 4 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/leoyeai-luban-skill/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-luban-skill.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/8/2026
Source: LeoYeAI/openclaw-master-skills ↗
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

SKILL.md

luban_skill

帮助 AI 高效操作 Luban 游戏配置表，支持枚举、Bean、数据表的增删改查。

前置条件

确保 Python 3.8+ 已安装
安装依赖：pip install openpyxl

使用方式

python scripts/luban_helper.py <command> --data-dir <项目的Datas目录>

注意：PowerShell 中使用分号 ; 作为命令分隔符，不要使用 &&。

PowerShell JSON 参数问题

PowerShell 处理 JSON 字符串参数会有转义问题。推荐使用 --file 参数从文件读取：

# 方法1：从JSON文件读取（推荐）
python scripts/luban_helper.py row add TbItem --file item.json --data-dir ...

# 方法2：使用Python脚本调用
python -c "import json; ..."

AI 操作工作流

⚠️ 操作前确认机制（强制）

所有写入/修改/删除操作在执行前，必须明确询问用户确认。

确认信息必须包含：

目标文件：完整的 Excel 文件路径
操作类型：添加/修改/删除字段/数据行/枚举/Bean 等
具体内容：详细的操作参数

各类操作确认模板

添加字段时：

我将执行以下操作：
- 目标文件：tables/datas/#Item-道具表.xlsx
- 操作：添加新字段
- 字段名：soul
- 字段类型：string
- 字段注释：灵魂
- 分组：c (客户端)

确认执行吗？(是/否)

添加数据行时：

我将执行以下操作：
- 目标文件：tables/datas/#Item-道具表.xlsx
- 操作：新增数据行
- 数据内容：
  - id: 3007
  - name: 魔丸
  - type: Consumable
  - quality: 5
  - ...

确认执行吗？(是/否)

删除字段/数据时：

⚠️ 警告：此操作不可逆！
- 目标文件：tables/datas/#Item-道具表.xlsx
- 操作：删除字段 "price"
- 影响：该字段的所有数据将被永久删除

确认执行吗？(是/否)

模糊指令时的确认：

场景	需要确认的内容
"加个道具"	确认表名、道具名、具体字段值
"加个字段"	确认表名、字段名、字段类型
"删除xxx"	确认删除目标、影响范围、二次确认
"修改xxx"	确认修改内容、影响的数据行

多文件选择：

找到多个可能的目标表：
1. TbItem (道具表) - #Item-道具表.xlsx
2. TbSkill (技能表) - #Skill-技能表.xlsx

请选择要操作的表？

首次操作项目时

执行 table list 了解项目有哪些表
执行 enum list 和 bean list 了解类型系统
根据用户需求定位具体表

修改数据前

先用 table get 或 field list 确认表结构
用 row get 查询现有数据，避免主键冲突
执行写入/修改/删除前，明确询问用户确认（目标文件 + 操作内容）
执行修改后用 validate 验证

删除操作前

用 ref 检查引用关系
提醒用户确认风险
危险操作需要二次确认

智能推断指南

用户说	推断命令	必须确认的内容
"查一下屠龙刀"	`row get TbItem --field name --value "屠龙刀"`	无需确认（只读）
"道具表加个字段"	`field add TbItem <字段名> --type ...`	必须确认：目标文件、字段名、类型、注释
"删除道具1001"	`row get TbItem --field id --value 1001` 确认后删除	必须确认：目标文件、删除的数据行
"看看品质枚举"	先 `enum list` 找到匹配项，再 `enum get`	无需确认（只读）
"加个新道具"	`row add TbItem --data '{"id":...}'`	必须确认：目标文件、所有字段值

重要原则：

只读操作（list/get/query/search）：无需确认，直接执行
写入操作（add/update/delete）：必须明确询问用户确认，包括目标文件和具体操作内容

常见错误速查

错误	原因	解决方案
找不到表	表名不带模块或拼写错误	用 `table list` 确认完整表名
主键冲突	添加的数据 id 已存在	先 `row get` 检查是否已存在
类型错误	数据格式不匹配字段类型	用 `table get` 确认字段类型
引用约束	删除被引用的枚举/Bean	用 `ref` 检查引用关系
找不到枚举	未带模块名	用 `enum list` 确认完整名称

常见场景速查

场景	命令
查看项目有哪些表	`table list`
查看表结构	`table get TbItem`
查看所有枚举	`enum list`
查看枚举详情	`enum get test.EItemQuality`
查看所有 Bean	`bean list`
查询 id=1001 的数据	`row get TbItem --field id --value 1001`
按条件查询数据	`row query TbItem --conditions '{"type":"Weapon"}'`
添加新字段	`field add TbItem price --type int --comment 价格`
删除字段（危险）	`field delete TbItem price`
创建新表	`table add test.TbEquip --fields "id:int,name:string"`
创建纵表	`table add test.TbConfig --fields "key:string,value:int" --vertical`
添加数据行	`row add TbItem --data '{"id":1001,"name":"宝剑"}'`
添加数据行（从文件）	`row add TbItem --file item.json`
导出表数据	`export TbItem --output backup.json`

智能插入：添加数据行时自动按 ID 顺序插入到合适位置，而非追加到末尾。

ID 最大 → 追加到末尾
ID 在中间 → 插入到合适位置

命令概览

命令	功能	示例
`enum list/get/add/update/delete`	枚举操作	`enum get test.EQuality`
`bean list/get/add/update/delete`	Bean操作	`bean get test.RewardItem`
`table list/get/add/update/delete`	表操作	`table get TbItem`
`field list/add/update/delete/disable/enable`	字段操作	`field add TbItem desc --type string`
`row list/get/query/add/update/delete`	数据行操作	`row get TbItem --field id --value 1`
`batch fields/rows`	批量操作	`batch rows TbItem --data '[...]'`
`export/import`	导入导出	`export TbItem --output backup.json`
`validate`	验证表数据	`validate TbItem`
`ref`	引用检查	`ref test.RewardItem`
`template`	配置模板	`template create item TbEquip`
`rename/copy/diff`	表管理	`rename TbItem TbItemNew`
`auto`	自动导入表	`auto create #Item --fields "..."`
`alias`	常量别名	`alias add GOLD 10000`
`tag`	数据标签	`tag add TbItem 2 dev`
`variant`	字段变体	`variant add TbItem name zh`
`multirow`	多行结构	`multirow TbReward items`
`type`	类型系统操作	`type list`, `type suggest quality --context item`
`type info <name>`	查询类型详情	`type info "list<int>"`
`type list`	列出所有类型	`type list --category enum`
`type validate <name>`	验证类型	`type validate "list<int>"`
`type suggest <field>`	建议字段类型	`type suggest drops --context monster`
`type search <keyword>`	搜索类型	`type search Quality`
`type guide`	类型使用指南	`type guide --topic container`
`cache`	缓存管理	`cache build`
`pref`	用户偏好	`pref set prefer_auto_import true`

核心操作示例

枚举操作

# 列出所有枚举
python scripts/luban_helper.py enum list --data-dir DataTables/Datas

# 查询枚举详情
python scripts/luban_helper.py enum get test.ETestQuality --data-dir DataTables/Datas

# 新增枚举
python scripts/luban_helper.py enum add test.EWeaponType --values "SWORD=1:剑,BOW=2:弓,STAFF=3:法杖" --comment "武器类型" --data-dir DataTables/Datas

# 删除枚举
python scripts/luban_helper.py enum delete test.EWeaponType --data-dir DataTables/Datas

Bean 操作

# 列出所有 Bean
python scripts/luban_helper.py bean list --data-dir DataTables/Datas

# 查询 Bean 详情
python scripts/luban_helper.py bean get test.TestBean1 --data-dir DataTables/Datas

# 新增 Bean
python scripts/luban_helper.py bean add test.Weapon --fields "attack:int:攻击力,speed:float:攻击速度" --parent Item --comment "武器" --data-dir DataTables/Datas

表操作

# 列出所有表
python scripts/luban_helper.py table list --data-dir DataTables/Datas

# 查询表详情
python scripts/luban_helper.py table get test.TbItem --data-dir DataTables/Datas

# 新增配置表（默认自动导入格式）
python scripts/luban_helper.py table add test.TbItem --fields "id:int:道具ID,name:string:道具名称" --comment "道具表" --data-dir DataTables/Datas

# 创建纵表（单例表）
python scripts/luban_helper.py table add test.TbGlobalConfig --fields "guild_open_level:int:公会开启等级,bag_init_size:int:初始格子数" --comment "全局配置" --vertical --data-dir DataTables/Datas

字段操作

# 列出表的所有字段
python scripts/luban_helper.py field list test.TbItem --data-dir DataTables/Datas

# 添加字段（分组自动推断）
python scripts/luban_helper.py field add test.TbItem desc --type "string" --comment "道具描述" --data-dir DataTables/Datas

# 删除字段（危险操作，需确认）
python scripts/luban_helper.py field delete test.TbItem desc --data-dir DataTables/Datas

# 禁用/启用字段
python scripts/luban_helper.py field disable test.TbItem desc --data-dir DataTables/Datas
python scripts/luban_helper.py field enable test.TbItem desc --data-dir DataTables/Datas

分组自动推断规则：

c (客户端): name, desc, icon, image, model, effect, sound, ui 等
s (服务器): server, logic, damage, hp, mp, exp, level, rate 等
cs (两者): id, 其他无法明确判断的字段

数据行操作

# 列出数据行
python scripts/luban_helper.py row list test.TbItem --data-dir DataTables/Datas

# 按字段值查询
python scripts/luban_helper.py row get TbItem --field id --value 1004 --data-dir DataTables/Datas

# 多条件查询
python scripts/luban_helper.py row query TbItem --conditions '{"type":"Weapon","quality":5}' --data-dir DataTables/Datas

# 添加数据行
python scripts/luban_helper.py row add test.TbItem --data '{"id":1001,"name":"宝剑","count":1}' --data-dir DataTables/Datas

自动导入表

Luban 支持文件名以 # 开头的 Excel 文件自动导入为表。

命名规则：

#Item.xlsx → 表名 TbItem，记录类型 Item
#Item-道具表.xlsx → 表名 TbItem，注释 道具表
reward/#Reward.xlsx → 表名 reward.TbReward

# 列出自动导入的表
python scripts/luban_helper.py auto list --data-dir DataTables/Datas

# 创建自动导入表
python scripts/luban_helper.py auto create #Item --fields "id:int:ID,name:string:名称" --data-dir DataTables/Datas

Excel 结构说明

数据表结构

| ##var   | id  | name   | count   |
| ##type  | int | string | int     |
| ##      | 道具ID | 道具名称 | 堆叠数量  |
| ##group | c   | c      | c       |  ← 可选

纵表结构（单例表）

| ##column |          |          |         |
| ##var    | ##type   | ##       | ##group |
| key      | string   | 配置键    | c       |
| value    | int      | 配置值    | s       |

enums.xlsx 结构

full_name 有值 = 枚举定义开始
full_name 为空 = 上一枚举的枚举项
*items 列（H列开始）= 枚举项数据

beans.xlsx 结构

full_name 有值 = Bean 定义开始
full_name 为空 = 上一 Bean 的字段
*fields 列（J列开始）= 字段数据

支持的类型

基本类型

bool byte short int long float double string text datetime

容器类型

array<T> list<T> set<T> map<K,V>

可空类型

在类型后加 ?：int? string? MyBean?

类型系统操作指南

Skill 提供强大的类型系统支持，帮助快速查找和使用正确的类型。

列出所有可用类型

# 列出所有类型（基本类型 + 容器类型 + 枚举 + Bean）
python scripts/luban_helper.py type list --data-dir DataTables/Datas

# 只列出基本类型
python scripts/luban_helper.py type list --category basic --data-dir DataTables/Datas

# 只列出枚举
python scripts/luban_helper.py type list --category enum --data-dir DataTables/Datas

# 只列出 Bean
python scripts/luban_helper.py type list --category bean --data-dir DataTables/Datas

验证类型是否有效

# 验证单个类型
python scripts/luban_helper.py type validate "list<int>" --data-dir DataTables/Datas

# 验证枚举类型
python scripts/luban_helper.py type validate "test.EQuality" --data-dir DataTables/Datas

根据字段名建议类型

# 通用建议
python scripts/luban_helper.py type suggest "item_id" --data-dir DataTables/Datas

# 怪物上下文建议
python scripts/luban_helper.py type suggest "drops" --context monster --data-dir DataTables/Datas

# 道具上下文建议
python scripts/luban_helper.py type suggest "quality" --context item --data-dir DataTables/Datas

支持的上下文：item(道具), skill(技能), monster(怪物), quest(任务), general(通用)

搜索类型

# 搜索包含关键词的类型
python scripts/luban_helper.py type search Quality --data-dir DataTables/Datas

# 只搜索枚举
python scripts/luban_helper.py type search Quality --category enum --data-dir DataTables/Datas

# 只搜索 Bean
python scripts/luban_helper.py type search Reward --category bean --data-dir DataTables/Datas

查看类型使用指南

# 查看完整类型指南
python scripts/luban_helper.py type guide --data-dir DataTables/Datas

# 查看容器类型指南
python scripts/luban_helper.py type guide --topic container --data-dir DataTables/Datas

# 查看可空类型指南
python scripts/luban_helper.py type guide --topic nullable --data-dir DataTables/Datas

# 查看枚举使用指南
python scripts/luban_helper.py type guide --topic enum --data-dir DataTables/Datas

查询类型详情

# 查询 list<int> 的详细信息
python scripts/luban_helper.py type info "list<int>" --data-dir DataTables/Datas

# 查询枚举详情
python scripts/luban_helper.py type info "test.EQuality" --data-dir DataTables/Datas

字段类型推断规则

Skill 内置智能类型推断，根据字段名自动建议类型：

字段名模式	建议类型	说明
`*_id`, `id`	int, long	ID 字段
`name`, `title`, `desc`	string	名称/描述
`count`, `num`	int	数量
`price`, `cost`	int, long	价格/成本
`quality`, `level`, `grade`	int	品质/等级
`rate`, `ratio`, `*prob`	float, double	概率/比率
`time`, `duration`	int, datetime	时间
`is_`, `has_`, `can_*`	bool	布尔标志
`icon`, `image`, `*model`	string	资源路径
`s`, `_list`, `*ids`	list	列表
`x`, `y`, `z`, `pos*`	float	坐标

危险操作说明

以下操作需要二次确认：

操作	说明
`field delete`	删除字段会同时删除该字段的所有数据
`row delete`	删除数据行不可恢复
`enum delete`	删除被引用的枚举会导致错误
`bean delete`	删除被引用的 Bean 会导致错误

使用 --force 可跳过确认，但请谨慎使用。

注意事项

枚举/Bean 名称：需要包含模块名，如 test.EWeaponType
删除前检查：删除前确认没有其他地方引用该枚举/Bean
路径问题：确保 --data-dir 指向正确的 Datas 目录

详细文档

完整命令文档：references/commands.md
需求参考文档：references/REFERENCE.md

About this skill

Automatic

Data Engineer pack

SkillSafety

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Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/luban-skill/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 4 files · scripts

Illustrative Want to see what leoyeai/luban_skill 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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luban_skill

前置条件

使用方式

PowerShell JSON 参数问题

AI 操作工作流

⚠️ 操作前确认机制（强制）

各类操作确认模板

首次操作项目时

修改数据前

删除操作前

智能推断指南

常见错误速查

常见场景速查

命令概览

核心操作示例

枚举操作

Bean 操作

表操作

字段操作

数据行操作

自动导入表

Excel 结构说明

数据表结构

纵表结构（单例表）

enums.xlsx 结构

beans.xlsx 结构

支持的类型

基本类型

容器类型

可空类型

类型系统操作指南

列出所有可用类型

验证类型是否有效

根据字段名建议类型

搜索类型

查看类型使用指南

查询类型详情

字段类型推断规则

危险操作说明

注意事项

详细文档

SkillSafety

Source

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