Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

中小企業與家族企業管理（SME & Family Business Management） · asgard-ai-platform | DecimalAI

Registry skillby asgard-ai-platform

中小企業與家族企業管理（SME & Family Business Management）

SME and family-business management for Taiwan's industrial structure (98% SME, 70%+ family-controlled listed). Integrates four pillars: resource-constrained decision-making, family governance (Tagiuri-Davis three circles), four-layer succession, and professionalization.

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

82%100%of test cases passed +18 pts

Without skill18 of 22

With skill22 of 22

4 fixed18 passed either way0 still failing0 broke

Measured on gemini-3.6-flash8/28/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskA founder of a chemical component maker evaluates whether his son should become CEO purely based on quarterly sales quotas achieved last year. How should this succession decision be analyzed?

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 asgard-ai-platform-biz-sme-management
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("asgard-ai-platform-biz-sme-management")
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 asgard-ai-platform-biz-sme-management

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/asgard-ai-platform-biz-sme-management/SKILL.md https://app.decimal.ai/s/asgard-ai-platform-biz-sme-management/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/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/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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("asgard-ai-platform-biz-sme-management")
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/asgard-ai-platform-biz-sme-management/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/asgard-ai-platform-biz-sme-management/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 asgard-ai-platform-biz-sme-management

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/asgard-ai-platform-biz-sme-management/SKILL.md https://app.decimal.ai/s/asgard-ai-platform-biz-sme-management/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/asgard-ai-platform-biz-sme-management/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/asgard-ai-platform-biz-sme-management/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/asgard-ai-platform-biz-sme-management.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 8/28/2026
Source: asgard-ai-platform/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

中小企業與家族企業管理（SME & Family Business Management）

定位

為什麼台灣 EMBA 特別需要這個 skill

台灣 98% 企業為中小企業、超過 70% 上市櫃為家族控制
多數 EMBA 學員本身是 SME 老闆、家族企業二代或配偶
主流商管理論以跨國大企業為樣本（Porter、Kotler 皆如是），直接套用會失真
台灣特有：一代創辦人仍健在、二代歸國、專業經理人介入 — 三方動態

與相近 Asgard skill 的邊界

biz-porters-five-forces、biz-swot 等策略工具 — 通用、需搭配本 skill 調整
grad-strat-agency — 代理理論，可支援家族治理分析
grad-strat-stakeholder — 利害關係人，家族企業中「家族」即關鍵利害人
algo-hr-* — 通用人資工具，SME 版需降維
本 skill — 補齊「資源限制 + 家族動態」兩個主流理論不處理的現實條件

何時使用

觸發條件

公司員工數 10–200 人、營收 1–50 億、創辦人仍在位
家族內（父子、兄弟、姑嫂）股權／經營權爭議
二代留學歸國準備接班
專業經理人與家族成員衝突
外部投資人（PE/VC）介入家族企業引發治理改革
IPO 前夕的家族企業專業化
成大、政大、逢甲 EMBA 相關個案與期末報告

不適用

跨國集團治理 → Asgard grad-governance + 本 repo biz-corporate-governance
純新創（尚未進入家族化）→ Asgard ux-lean-startup
薪酬技術設計 → Asgard algo-hr-compensation
併購估值 → 本 repo fin-m-and-a（待開發）

IRON LAW — SME 與家族企業三條鐵律

IRON LAW 1：SME 不是「小型大企業」
中小企業面臨的限制（資金、人才、資訊、品牌）是結構性的，
不能直接套用大企業的最佳實務。
錯誤處方：給 30 人公司導 SAP、設 COO／CFO／CMO 三層 C-level。
正確處方：優先序排程、一人多角設計、外包替代內建。

IRON LAW 2：家族企業的「系統」有三個圓
家族系統（情感）× 所有權系統（財產）× 事業系統（績效）
三圓相交處是家族企業獨有張力。忽略其中任一圓的分析都是殘缺的。
典型陷阱：純用績效邏輯評估「兒子接班」。

IRON LAW 3：傳承不是「選人」是「設計制度」
把接班視為挑兒子或找專業經理人 = 錯把人當制度。
完整傳承 = 所有權傳承 + 治理結構傳承 + 經營權傳承 + 文化傳承
四者可分離，且節奏不同（通常所有權最快、文化最慢）。

Rationalization Table — 當 Claude 想「本案例外」時，先自問

可能想	但 Iron Law 仍適用，因為
「30 人公司也要導入完整 COSO + SAP 才專業」	SME 限制是結構性；必須給精簡版框架並標註「未精簡 = 組織癱瘓」
「接班分析只談『長子 vs. 專業 CEO』」	必須同時分析家族、所有權、事業三圓；忽略任一圓的建議殘缺
「結論寫『王董長子是最佳接班人』就完成傳承規劃」	傳承含所有權 / 治理 / 經營 / 文化四層；選人只是經營層一步，其餘三層必須設計制度

框架一：資源受限下的策略選擇

SME 常見資源缺口

資源維度	大企業	SME	因應策略
資金	多元融資	銀行 + 自有	預現金流先於追成長
人才	分工專業	一人多角	關鍵職位以外外包
品牌	強	弱	策略聚焦（niche）、B2B 優於 B2C
資訊	情報系統	老闆的行業網絡	善用產業公會、同儕社群
通路	自建或主導	依附大通路	平台議價 + 建替代通路
法遵	法務團隊	老闆兼任	外部顧問 + 分段遵循

資源受限的策略原則

先活著，再談成長：現金流 > 損益表
聚焦取勝：利基市場、單點做深
槓桿外部：通路、品牌、技術借用他人
快速試錯：決策成本低是 SME 唯一優勢
拒絕模仿大企業：組織結構、KPI、會議頻率都要減配

適用工具（與其他 skill 組合）

策略：Asgard biz-swot、biz-blue-ocean（海洋要藍但要小）
商模：Asgard ops-business-model-canvas — SME 用得上的少數模板
定位：Asgard biz-stp — 聚焦是 SME 生存關鍵
財務：Asgard biz-breakeven、biz-financial-ratios — 先顧活命線

框架二：家族企業三圓模型（Tagiuri & Davis）

      家族（Family）
         / \
        /   \
       /     \
   所有權 ─── 事業
   (Ownership)(Business)

七個位置（每個成員在圖中可能落在不同位置）

家族成員但不持股、不任職（如成年未入家族事業的子女）
持股但非家族、非員工（外部投資人、策略夥伴）
員工但非家族、非持股（專業經理人）
家族成員 + 持股（家族股東，不任職）
家族成員 + 員工（二代未持股任職）
員工 + 持股（員工持股計畫受益人）
家族 + 所有權 + 員工（三圓交集，通常是創辦人或接班人）

分析應用：

衝突診斷：指出衝突雙方在圖中的位置，理解動機差異
治理設計：七類人對應不同的決策參與機制（家族委員會、股東會、董事會）
傳承規劃：接班人目標 = 讓下一代「適當比例」移入第 7 位置

框架三：二代接班四層傳承

Layer 1：所有權傳承（Ownership Transfer）
  - 贈與稅、遺產稅規劃
  - 閉鎖型家族控股、信託
  - 分年移轉 vs. 一次移轉
  - 通常 3–5 年完成

Layer 2：治理結構傳承（Governance Transition）
  - 家族憲章（Family Constitution）制定
  - 家族委員會、董事會、管理層三層分離
  - 獨立董事、薪酬委員會
  - 通常 1–3 年建置

Layer 3：經營權傳承（Management Transfer）
  - 接班人培育計畫（內部輪調、外部歷練）
  - 雙 CEO 過渡、副手制
  - 專業經理人並行
  - 通常 5–10 年

Layer 4：文化／無形資產傳承（Cultural Legacy）
  - 創辦人精神、核心價值
  - 關鍵客戶關係、供應商信任
  - 隱性知識（tacit knowledge）
  - 無確定終點，持續演化

診斷提問

目前四層進度各在哪裡？
節奏是否失衡（常見：所有權急、治理慢）？
接班人在各層的角色為何？

框架四：家族憲章核心條款

家族憲章（Family Constitution／Family Charter）是家族成員共識性文件，不是法律文件但具備自律約束力。

標準條款

家族願景與價值觀：為什麼家族要繼續經營事業？
家族就業政策：成員進入家族事業的條件（學歷、外部歷練、能力要求）
股權政策：股份可否轉讓？家族外轉讓的限制？回購機制？
分紅政策：獲利如何分配？盈餘保留比例？
治理結構：家族委員會、董事會、管理層職責分離
接班機制：接班人遴選程序、評估標準、過渡期設計
衝突解決：家族內爭議仲裁機制（家族委員會 → 外部調解 → 法律）
婚姻與繼承：配偶權利、離婚情境、隔代繼承原則
修憲機制：憲章本身的修改門檻與程序

台灣實務差異

歐美家族憲章歷史悠久（如 Rothschild, Mulliez）
台灣家族企業多為第一代仍健在，憲章通常第一代主導草擬
執行力取決於創辦人權威 + 專業顧問介入
常見失敗：只簽字，從未真正執行或修訂

工作流程

根據個案性質跳過不適用步驟；以下為完整候選路徑，非必跑清單。

Step 1：基本盤掃描
  - 規模（員工、營收、股東結構）
  - 家族結構（代數、關鍵成員、股權）
  - 事業階段（創立、成長、成熟、傳承）

Step 2：辨識核心議題
  - 資源受限型：策略聚焦、現金流
  - 家族動態型：三圓衝突、接班爭議
  - 治理轉型型：專業化、IPO 準備
  - 傳承規劃型：四層整合

Step 3：套用對應框架
  - 資源受限 → 框架一
  - 家族衝突 → 框架二（三圓）
  - 傳承 → 框架三（四層）+ 框架四（憲章）

Step 4：介入設計（分時程）
  - 短期（6 個月）：治理痛點止血
  - 中期（1–3 年）：結構調整
  - 長期（5–10 年）：文化與接班

Step 5：風險與限制聲明
  - 家族情感無法全面理性化
  - 外部專家介入有 backfire 風險
  - 台灣特有法稅框架需專業顧問配合

Output Format

# SME／家族企業分析：{公司名稱／個案標題}

## 一、基本盤
- 規模、員工、營收
- 家族結構（圖）
- 事業階段

## 二、核心議題診斷
- 主要張力：資源 / 家族 / 治理 / 傳承
- 各議題優先序（為何）

## 三、框架分析
### 資源受限面（若適用）
### 三圓分析（若家族衝突）
### 四層傳承盤點（若傳承規劃）
### 家族憲章檢核（若治理議題）

## 四、介入方案
### 短期（6 個月）
### 中期（1–3 年）
### 長期（5–10 年）

## 五、組織／治理變革建議
- 家族委員會、董事會、管理層三層設計
- 獨董角色
- 專業經理人配比

## 六、風險與侷限
- 情感因素
- 法稅專業界面
- 本分析的資訊侷限

Examples

正確應用

情境：中部某隱形冠軍家族企業（精密機械、營收 18 億、員工 140 人），創辦人 68 歲，長女海外 MBA 歸國任副總 5 年，次子國內本科任生產主管 12 年，兄妹姊弟關係和諧但對未來發展方向意見不同。

診斷：

框架一：資源受限 → 聚焦現有利基（歐系車廠零件），非盲目拓展
框架二：三圓 → 長女位置偏「家族 + 所有權」，次子偏「家族 + 員工」，張力來自未來方向
框架三：所有權層已規劃（閉鎖控股 + 信託），治理層未啟動，經營層尚未設計雙副手架構
框架四：家族憲章尚未制定 → 當務之急

建議

短期：聘家族顧問展開憲章起草（6 個月）、設家族委員會
中期：導入 2 位獨董、長女 + 次子各主導一條事業線（雙軌試煉）
長期：三年後由家族委員會 + 獨董共同評估接班型態

正確之處：四框架都用、台灣稅務結構納入考量、兄妹姊弟未二元化。

錯誤應用

直接建議「找外部 CEO」跳過家族動態分析 → 未用三圓框架
用 Porter 五力完整套入 30 人公司 → 違反 IRON LAW 1
以財務 ROI 評估「女兒 vs. 兒子接班」 → 忽略家族系統
推薦家族憲章但不提台灣稅法、公司法限制 → 實務落差

Gotchas

家族情感不能用財務邏輯解：「公平」在家族中不等於「平等分配」，也不等於「按績效分配」，需先定義
台灣稅法快速變動：遺產稅率、贈與稅額度、閉鎖型公司規定每隔數年修正，必須與會計師／律師合作
二代「歸國就是接班」的假設要警惕：許多二代回國是為陪伴父母，未必願意接班，硬推會反彈
專業經理人「過渡期」常被做成「長期抗戰」：缺乏明確時程與條件，3 年變 10 年，兩敗俱傷
家族憲章不是法律文件：無法強制執行，成敗取決於家族共識與創辦人權威，過度依賴文件是誤解
「隱形冠軍」標籤的陷阱：台灣 SME 常自豪於隱形冠軍地位，卻因此忽略傳承、ESG、數位化等長期議題
小公司也要做治理：SME 容易覺得治理是大公司事，但家族企業治理失敗常在「還小」時種下因

References

台灣家族企業生態與統計 → references/tw-family-business-landscape.md
二代接班規劃實務指南（含稅法與閉鎖型公司）→ references/succession-playbook.md
家族憲章完整條款模板 → references/family-constitution-template.md
成大／政大 EMBA 家族企業課程脈絡 → references/emba-family-courses.md
延伸：Asgard grad-strat-agency（代理理論）、grad-strat-stakeholder（利害關係人）、ops-business-model-canvas（商模畫布）、ops-talent-strategy（本 repo，人才策略）、biz-corporate-governance（本 repo，治理實務）

When it activates

Automatically, when a task matches. Use for sub-200-employee strategy, capital-constrained prioritization, family-vs-professional-manager conflict, founder handover, family-constitution design, or SME financing limits. Triggers: 『二代接班』『家族企業』『中小企業』『家族憲章』『家族辦公室』『傳承規劃

About this skill

Automatic

HR & People Ops pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

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

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

asgard-ai-platform / skills

biz-sme-management/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 4 files

Illustrative Want to see what asgard-ai-platform/biz-sme-management would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

中小企業與家族企業管理（SME & Family Business Management）

定位

何時使用

IRON LAW — SME 與家族企業三條鐵律

Rationalization Table — 當 Claude 想「本案例外」時，先自問

框架一：資源受限下的策略選擇

SME 常見資源缺口

資源受限的策略原則

適用工具（與其他 skill 組合）

框架二：家族企業三圓模型（Tagiuri & Davis）

框架三：二代接班四層傳承

框架四：家族憲章核心條款

工作流程

Output Format

Examples

正確應用

錯誤應用

Gotchas

References

When it activates

SkillSafety

Source

Related skills

More in the registry