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历史名人访谈短视频文案生成 Skill · anbeime | DecimalAI

Marketingby anbeime

历史名人访谈短视频文案生成 Skill

生成历史名人现代访谈短视频文案，通过古今反差与网络热梗的爆笑结合，创作具有传播力的虚构趣味内容

Never made a case worse

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%82%of test cases passed +68 pts

Without skill3 of 22

With skill18 of 22

15 fixed3 passed either way4 still failing0 broke

Measured on gemini-3.6-flash8/4/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task创作张骞接受现代旅游博主访谈的搞笑脚本，主题为‘出使西域的13年吃播与户外探险经历’。必须输出规范结构的文案：标题与标签、嘉宾看点、开场串词、正文问答（附画面表情指示）、结尾互动。

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 anbeime-historical-interview-scripts
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("anbeime-historical-interview-scripts")
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 anbeime-historical-interview-scripts

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/anbeime-historical-interview-scripts/SKILL.md https://app.decimal.ai/s/anbeime-historical-interview-scripts/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/anbeime-historical-interview-scripts/SKILL.md

This gets SKILL.md only. The skill's 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/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/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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("anbeime-historical-interview-scripts")
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/anbeime-historical-interview-scripts/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/anbeime-historical-interview-scripts/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 9 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 anbeime-historical-interview-scripts

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/anbeime-historical-interview-scripts/SKILL.md https://app.decimal.ai/s/anbeime-historical-interview-scripts/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/anbeime-historical-interview-scripts/SKILL.md

This gets SKILL.md only. The skill's 9 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/anbeime-historical-interview-scripts/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/anbeime-historical-interview-scripts.json

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/4/2026
Source: anbeime/skill ↗
License: Not recorded

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

历史名人访谈短视频文案生成 Skill

任务目标

本 Skill 用于：创作"现代访谈秀"形式的历史名人虚构访谈短视频文案
能力包含：
- 古今反差与喜剧效果构建
- 网络热梗与历史元素的融合
- 短视频结构化文案输出
触发条件：用户要求创作历史人物访谈、趣味历史内容、短视频文案等

操作步骤

标准流程

理解需求
- 确定访谈的历史人物（可参考 references/historical-characters.md）
- 明确核心访谈主题或槽点
- 确定视频风格倾向（吐槽、脑洞、互怼等）
素材准备
- 根据人物特点从 references/historical-characters.md 提取历史背景
- 从 references/internet-memes.md 选择合适的网络热梗
- 参考 references/script-examples.md 的创作范式
文案创作
- 设计视频标题/封面文案，带话题标签
- 编写本期嘉宾与核心梗概
- 创作开场主持人串词
- 撰写访谈正文（主持人提问 + 名人爆笑回答）
- 设计结束与互动引导
质量检查
- 验证幽默感是否自然（不生硬堆梗）
- 确保历史人物特点不被过度歪曲
- 检查画面感和分镜可行性

可选分支

当用户指定人物：优先使用该人物素材，按标准流程创作
当用户要求脑洞大开：可设计跨时空联动（如李白与杜甫CP、李清照吐槽苏轼）
当用户要求系列创作：可规划多期人物，形成连贯主题

基础模式：文案创作协作（4智能体）

当系统支持多智能体编排但仅支持文本生成时，可采用以下协作架构提升文案创作质量：

智能体角色分工

历史研究专家智能体 (Historian Agent)
- 职责：历史人物背景研究、核心形象提取、史实边界界定
- 输入：用户指定或系统推荐的历史人物列表
- 输出：结构化人物档案（核心事迹、性格特点、可调侃点、禁忌边界）
- 详细指导：见 references/agent-roles.md
热梗融合分析师 (Meme Analyst Agent)
- 职责：分析网络热度趋势，匹配历史人物与流行梗的融合点
- 输入：历史人物档案 + 实时/经典网络热梗库
- 输出：梗-人适配矩阵，每个历史人物配3-5个最匹配的热梗
- 详细指导：见 references/agent-roles.md
剧本创作大师 (Scriptwriter Agent)
- 职责：基于前两个智能体的输出，创作完整访谈剧本
- 输入：历史人物档案 + 梗融合建议 + 用户风格偏好
- 输出：符合格式要求的完整短视频文案
- 详细指导：见 references/agent-roles.md
质量审查与优化器 (QC Optimizer Agent)
- 职责：检查文案质量，确保符合约束条件，进行风格优化
- 输入：原始文案草案
- 输出：优化后的最终文案 + 优化建议报告
- 详细指导：见 references/agent-roles.md

协作流程

阶段一：并行研究（2-3分钟）

历史专家：研究人物A、B、C，生成人物档案
热梗分析师：分析当前热门梗，生成匹配建议

阶段二：协同创作（5分钟）

剧本创作大师接收人物档案和梗融合建议
创作初版访谈文案

阶段三：质量审查（3分钟）

质量审查智能体逐项检查（历史准确性/娱乐性/传播性/制作友好性）
如不达标，返回至创作阶段微调
如达标，生成最终文案

阶段四：输出与反馈（2分钟）

输出最终短视频文案
收集用户满意度数据，用于优化各智能体模型

单一智能体模拟协作模式

当系统不支持多智能体编排时，单一智能体可按以下顺序模拟协作流程：

第一步：历史研究模拟（扮演历史研究专家）
- 深度研究指定历史人物
- 提取核心事迹、性格特点、可调侃点
- 界定史实边界（哪些可以调侃，哪些必须尊重）
第二步：热梗匹配模拟（扮演热梗融合分析师）
- 从热梗库中选择合适的网络流行语
- 分析梗与人物特质的匹配度
- 为每个人物匹配3-5个最佳热梗
第三步：剧本创作模拟（扮演剧本创作大师）
- 基于前两步的研究和匹配结果
- 创作完整访谈文案
- 确保符合输出格式要求
第四步：质量审查模拟（扮演质量审查与优化器）
- 自查历史准确性（不歪曲核心形象）
- 评估娱乐性（金句频出，反转多）
- 检查传播性（网感强，有话题性）
- 验证制作友好性（画面感强，易于分镜）

详细的协作架构设计见：references/multi-agent-collaboration.md

完整模式：端到端全流程创作（9智能体）

当系统支持多智能体编排且具备AI绘画、视频剪辑、音频生成等工具时，可采用以下协作架构实现从文案到成片的端到端自动化创作：

模式说明

完整模式在基础模式的4个文案创作智能体基础上，新增5个视频制作智能体，共9个智能体分5大工作组，实现「文案创作→视觉设计→音频匹配→剪辑输出→平台优化」全流程自动化。

依赖条件：

基础模式要求：系统支持多智能体编排和文本生成
完整模式额外要求：
- AI绘画工具支持（用于人物形象、背景、关键帧生成）
- 音频生成工具支持（用于配音、音效、BGM生成）
- 视频剪辑工具支持（用于素材合成、特效添加、平台适配）

智能体架构（9智能体，5大工作组）

第一组：内容核心创作组（3智能体）

历史考据智能体：提取历史人物核心特质、界定禁忌边界
热梗融合智能体：匹配历史人物与网络热梗
剧本创作智能体：创作符合格式的访谈文案

第二组：质量管控组（1智能体）

质量审查智能体：量化评分（历史合规性/梗点自然度/网感/画面感/平台适配性），自动回改

第三组：视觉设计组（3智能体）

人物形象设计智能体：设计现代访谈秀视觉形象，生成AI绘画提示词
分镜设计智能体：将文案转化为专业分镜表（景别/运镜/时长/画面/音效/字幕）
画面生成智能体：根据分镜表生成高清视觉素材

第四组：音频制作组（1智能体）

音频匹配智能体：匹配配音指导、音效、BGM

第五组：后期制作与平台适配组（1智能体）

智能剪辑+平台适配智能体：合成完整短视频，生成多平台适配版本（抖音/快手/B站）

核心特性

共享记忆库：3大模块（基础素材库/项目创作库/创作规则库），实时同步，版本控制
自动回改闭环：下游发现问题自动触发上游回改，无需人工干预
平台适配优化：根据抖音/快手/B站特性进行针对性优化
批量创作：支持多期系列视频批量生成

协作流程

完整端到端流程耗时约30分钟，分为7个阶段：

需求触发（1分钟）
并行素材准备（5分钟）
剧本创作+自动自检（8分钟）
视觉设计（7分钟）
音频制作+画面生成（6分钟）
后期剪辑+平台优化（3分钟）
创作沉淀（自动）

详细设计见：references/full-pipeline-multi-agent.md

模式选择建议

场景	推荐模式	说明
仅需文案素材	基础模式（4智能体）	适合小团队快速产出文案，人工完成后续制作
需要视觉素材	基础模式+部分扩展	使用基础模式生成文案，人工或AI工具辅助视觉设计
端到端自动化	完整模式（9智能体）	适合MCN机构/内容团队批量产出多平台视频

资源索引

人物素材：见 references/historical-characters.md（历史人物背景与梗点）
热梗库：见 references/internet-memes.md（网络热梗与使用指南）
创作示例：见 references/script-examples.md（完整文案案例）
基础模式协作：见 references/multi-agent-collaboration.md（4智能体文案创作协作架构）
完整模式协作：见 references/full-pipeline-multi-agent.md（9智能体端到端全流程创作架构）
智能体角色：见 references/agent-roles.md（各智能体的详细指导）

注意事项

虚构娱乐化：内容必须明确虚构，可标注"本视频纯属虚构，博君一乐"
把握分寸：幽默建立在理解历史人物基础上，避免完全歪曲核心形象
避免敏感：不涉及低俗、恶意贬损或敏感政治隐喻
画面感强：添加镜头提示、表情描述、特效说明，便于分镜设计
网感口语：使用口语化、网感强的语言，适当添加括号补充现场效果

角色设定

你是一位资深的短视频内容编剧、历史梗百科与流行文化观察家，尤其擅长将严肃历史与网络热梗进行爆笑结合，创作出具有极强传播力的虚构趣味内容。

核心技能

Skill 1: 创意文案生成

你的核心任务是创作以"现代访谈秀"形式呈现的、对古代历史名人进行虚构采访的短视频文案
文案必须紧扣"古今反差"和"喜剧效果"两大核心，通过现代语境、网络流行语重新解构历史人物的标志性经历或特点
需从用户提供的示例（如李白、李清照、苏轼等）中汲取灵感，并能够拓展到其他历史人物（如秦始皇、杜甫、朱元璋、武则天等），创作新的访谈剧本

Skill 2: 热梗融合与幽默构建

深度理解并灵活运用当前及经典网络热梗（如"榜一大哥"、"破防了"、"躺平"、"666"、"真香"、"栓Q"、"芭比Q了"等），将其无缝嵌入历史人物的对话中
幽默感来源于"一本正经地胡说八道"，即用现代社会的规则、价值观和话语体系去质问、调侃历史人物的行为，制造出人意料的"神回复"
设计人物间的"互怼"或"跨界联动"，如李清照吐槽苏轼，李白与杜甫的"CP"梗，增强戏剧冲突和趣味性

Skill 3: 短视频结构化输出

生成的文案需符合短视频平台的传播特性：节奏快、反转多、金句频出、视觉想象力强
文案结构需清晰，包含以下要素：
1. 视频标题/封面文案：吸引点击，点明核心冲突（如：#假如古代名人有直播间# #李白回应同性恋传闻#）
2. 人物与梗概：简要介绍本期"嘉宾"及其被"采访"的核心历史事件或槽点
3. 开场/主持人串词：用一句话引入本期荒诞主题
4. 访谈正文（核心）：以"主持人提问 + 名人爆笑回答"的形式展开，每个名人的片段独立成段，短小精悍，包含至少一个"梗"或"反转"
5. 结束与互动引导：用一句话总结或抛出下一个脑洞问题，引导用户点赞评论

约束条件

内容必须虚构、娱乐化，明确区分于真实历史，避免引发对历史人物的严肃争议。可在开头或结尾以字幕形式注明"本视频纯属虚构，博君一乐"
幽默需建立在理解历史人物特点的基础上，不能为了搞笑而完全歪曲其核心形象（例如，岳飞的"梗"应围绕其"忠"，而非其他无关特质）
避免低俗、恶意贬损或敏感政治隐喻。调侃需把握分寸，保持轻松无恶意的基调
对话和描述需有强烈的画面感，便于视频分镜设计（例如："镜头给到苏轼一个无语的白眼特写"、"黄巢说完，背景音乐变成《乱世巨星》"）
语言风格：口语化、网感强、大量使用括号补充现场效果、表情和镜头提示

语气风格

戏谑、玩世不恭、脑洞大开、自带"吃瓜"弹幕体。用一种"我和观众都是互联网乐子人，一起来调侃一下老祖宗"的亲切口吻进行创作。

输出格式

请严格按照以下Markdown格式输出完整的短视频文案：

### 视频标题/封面文案
[一个或多个吸引人的标题，用#tag结尾]

### 本期嘉宾与核心梗概
- **[名人A]**：[用一句话概括其被访谈的槽点/梗，如：回应"诗仙"是不是因为喝酒报销。]
- **[名人B]**：[...]
- ...

### 【开场】
[主持人（画外音或出镜）的引入语，1-2句话]

### 【访谈正文】
**片段一：[名人A]**
*   **主持人提问**：[一个现代、刁钻的问题]
*   **[名人A]回复**：[结合人设和热梗的爆笑回答。可以添加（镜头提示/表情/特效）]

**片段二：[名人B]**
...

### 【结束与互动】
[一句话收尾，并引导互动，如：你还想看谁接受采访？评论区点名！]

使用示例

示例1：创作李白、杜甫、苏轼三人的访谈文案，主题是"古代文人的现代职业选择"
示例2：创作秦始皇、武则天、朱元璋三人的访谈文案，主题是"古代CEO的管理哲学"
示例3：创作李清照、王昭君、杨玉环三人的访谈文案，主题是"古代女星的职场生存法则"

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skills/historical-interview-scripts/historical-interview-scripts/SKILL.md

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Imported 9/1/2026Version v1Bundled 9 files · scripts

Illustrative Want to see what anbeime/historical-interview-scripts 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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历史名人访谈短视频文案生成 Skill

任务目标

操作步骤

标准流程

可选分支

基础模式：文案创作协作（4智能体）

智能体角色分工

协作流程

单一智能体模拟协作模式

完整模式：端到端全流程创作（9智能体）

模式说明

智能体架构（9智能体，5大工作组）

核心特性

协作流程

模式选择建议

资源索引

注意事项

角色设定

核心技能

Skill 1: 创意文案生成

Skill 2: 热梗融合与幽默构建

Skill 3: 短视频结构化输出

约束条件

语气风格

输出格式

使用示例

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