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Video Creation Collaborator · anbeime | DecimalAI

Design & Mediaby anbeime

Video Creation Collaborator

影品智创多智能体协同视频创作管理工具,提供11个智能体结构化分工、5阶段协同流程、质量管控标准与数据反馈机制,解决生图失真、视频合成瑕疵等问题,确保输出统一可控

SkillScore

SkillScore88/100

Test results 88

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%88%of test cases passed +52 pts

Without skill9 of 25

With skill22 of 25

13 fixed9 passed either way3 still failing0 broke

Measured on gemini-3.6-flash8/10/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task分镜脚本 `./scripts/storyboard.json` 已经校验通过，现在需要生成 UTF-8 编码的 SRT 字幕文件 `./output/subtitles/subtitle.srt`。请写出执行该操作的命令行指令。

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-video-creation-collaborator
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-video-creation-collaborator")
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-video-creation-collaborator

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-video-creation-collaborator/SKILL.md https://app.decimal.ai/s/anbeime-video-creation-collaborator/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/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-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/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-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator")
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-video-creation-collaborator/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator

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-video-creation-collaborator/SKILL.md https://app.decimal.ai/s/anbeime-video-creation-collaborator/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/anbeime-video-creation-collaborator/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-video-creation-collaborator/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/anbeime-video-creation-collaborator.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 8/10/2026
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

影品智创 - 多智能体协同视频创作管理

任务目标

本Skill用于: 规范化和指导10个智能体的协同创作流程,解决生图失真(多肢体/畸形)、视频合成瑕疵等问题
能力包含:
- 11个智能体结构化分工(文案创作/故事策划/脚本创作/分镜导演/分镜画师/字幕师/配音师/音效师/视频工程师/质检/数据反馈)
- 5阶段协同流程管理(需求承接→内容创作→生图创作→音频字幕→视频合成→全流程质检→数据迭代)
- 质量管控与闭环反馈(前置质检拦截、数据沉淀迭代)
- 素材生成支持(图片/音频/字幕/音乐)
- 技术检测工具支持(图片质量、视频质量、音画同步)
触发条件: 用户需要进行短视频创作,或需要规范化多智能体协同流程

前置准备

依赖说明:

opencv-python>=4.8.0
pillow>=10.0.0
moviepy>=1.0.3
numpy>=1.24.0

非标准文件/文件夹准备:

# 创建工作目录
mkdir -p ./input/{materials,scripts}
mkdir -p ./output/{drafts,storyboards,final}
mkdir -p ./cache/{images,audio}

操作步骤

标准流程(5个阶段,10个智能体)

第一阶段: 需求承接与内容框架搭建

触发节点: 用户输入核心需求(商品信息+视频需求)

步骤1: 文案创作师智能体(首节点)

职责: 基于商品信息提炼核心卖点,生成适配短视频的文案
输入: 商品品类、核心卖点、目标受众、视频风格、时长
输出:
- 核心Slogan(8-12字,简洁有力)
- 分镜适配文案(3-5句,每句4-8字)
质量标准: 无歧义、无夸大,贴合视频调性
下一级触发: 立即触发【故事策划师】、【字幕师】(暂存)

步骤2: 故事策划师智能体

职责: 基于卖点、文案,设计适配短视频时长的场景化故事线
输入: Slogan、分镜文案、商品信息、视频时长、风格
输出:
- 场景化故事线(镜头拆分、场景/动作标注)
- 镜头数量(30秒8-10个、45秒12-15个、60秒15-18个)
质量标准: 逻辑连贯,时长严格匹配,无无关场景
下一级触发: 立即触发【脚本创作师】

步骤3: 脚本创作师智能体

职责: 将故事线转化为标准化、可执行的分镜脚本
输入: 故事线、文案、视频时长、分辨率、比例
输出:
- 标准化分镜脚本(镜头序号、时长、场景描述、画面动作、文案、音效备注)
- 画面动作描述(明确人体肢体姿态,规避模糊表述)
质量标准: 时长精准分配,画面动作具体,格式统一
下一级触发: 立即触发【分镜导演】、【分镜画师】、【字幕师】、【音效师】

第二阶段: 视觉内容创作与质检

触发节点: 分镜脚本确认

步骤4: 分镜导演智能体

职责: 规划每个镜头的画面细节、光影、构图,为分镜画师提供精准依据
输入: 分镜脚本、商品细节图(如有)、视频风格
输出:
- 镜头画面规范(构图/光影/色调/肢体规范/商品细节)
- 负面规避清单(禁止多余肢体、畸形、虚化等)
质量标准: 构图清晰,光影均匀,肢体规范,商品细节明确
下一级触发: 立即触发【分镜画师】

步骤5: 分镜画师智能体

职责: 基于画面规范,生成高质量、无瑕疵的分镜图片
输入: 画面描述、分镜脚本、商品信息
输出: 1080P合格分镜图片(单张对应单个镜头,命名规范)
质量标准:
- 人体结构正常(无多余肢体/畸形/重影)
- 商品细节精准(无变形/模糊)
- 画质细腻(1080P,无颗粒感)
负面规避: 禁止多余肢体、手部畸形、商品变形、画面噪点等
下一级触发:
- 图片生成后,触发【视频工程师】素材预处理
- 同时触发【质检智能体】分镜图片专项质检

第三阶段: 音频字幕创作(并行执行)

触发节点: 分镜脚本确认(可并行启动,无需等待视觉内容)

步骤6: 配音师智能体

职责: 根据分镜文案生成高质量旁白/配音
输入: 分镜文案、视频风格、情感基调
输出:
- 旁白文本(与分镜文案对应,可适当口语化)
- 音色建议(活力男声/专业女声/稳重男声/亲切女声等)
- 语速要求(中等/稍快/稍慢)
- 情感标注(积极/专业/沉稳/亲切等)
质量标准: 语音自然流畅,情感贴合场景,无明显机械感
下一级触发: 输出同步至【音效师】,调用audio_generator.py生成音频文件

步骤7: 字幕师智能体

职责: 创作适配画面的字幕,确保显示效果与可读性
输入: 分镜文案、分镜脚本、视频风格、画面比例
输出:
- 字幕信息包(文本+对应镜头时长+叠加位置)
- 格式规范(字体/字号/颜色/位置)
质量标准: 无错别字,字数适配(每秒1-2字),避开商品主体
下一级触发: 输出同步至【视频工程师】

步骤8: 音效师智能体

职责: 推荐适配的背景音乐与场景音效,确保音画协调
输入: 分镜脚本、视频风格、时长、核心场景
输出:
- 音效方案包(背景音乐+场景音效+时间节点+音量参数)
- 音效名称、风格描述、时长、音量建议
质量标准: 风格贴合,节奏匹配,音量适中,无版权问题
下一级触发: 输出同步至【视频工程师】

第四阶段: 视频合成与成品质检

触发节点: 分镜图片质检合格

步骤9: 视频工程师智能体

职责: 基于各智能体输出素材,完成高质量视频合成
输入: 合格分镜图片、字幕信息、音效方案
输出: 合成后的MP4成品视频
质量标准:
- 素材预处理(调用质量检测脚本,识别瑕疵)
- 合成参数(1080P, 25fps, 8Mbps,无黑边/卡顿)
- 转场效果(淡入淡出,0.3-0.5秒,过渡自然)
- 音画同步(音频偏移≤0.1秒)
下一级触发: 成品输出后立即触发【质检智能体】

步骤10: 质检智能体(全流程穿插)

职责: 对各智能体输出物进行全环节质检,拦截瑕疵,推动整改
输入: 各环节输出物、质量标准、负面规避清单
输出:
- 分环节质检报告(合格/不合格,瑕疵类型,整改建议)
- 质检台账(瑕疵数据、整改结果、合格率)
质检维度:
- 内容层(文案/故事线/脚本/字幕)
- 视觉层(分镜图片/成品视频)
- 音频层(背景音乐/音效/音画同步)
- 格式层(比例/脚本格式/视频参数)
处理规则:
- 轻微瑕疵(文案语序微调)→直接反馈优化
- 重大瑕疵(多肢体/画面撕裂)→拦截输出,要求重制
- 前置质检→分镜图片/成品视频强制质检,不合格驳回
下一级触发:
- 不合格→触发对应智能体重做
- 合格→触发【数据反馈智能体】

第五阶段: 数据沉淀与迭代优化

触发节点: 全流程质检合格

步骤11: 数据反馈智能体

职责: 收集分析全流程数据,输出迭代建议,优化智能体提示词与协同逻辑
输入: 质检台账、创作耗时数据、用户反馈
输出:
- 数据统计报告(合格率、高频瑕疵、创作耗时、整改率)
- 迭代优化建议(针对高频瑕疵优化提示词、调整协同流程)
- 数据沉淀(瑕疵类型-优化方案-效果验证闭环)
分析维度:
- 瑕疵分析(TOP3高频瑕疵,定位根因)
- 效率分析(识别流程瓶颈)
- 优化效果(验证迭代有效性)
下一级触发: 输出同步至技能开发端,更新智能体配置,完成迭代闭环

并行协同与闭环管控

并行执行

字幕师、音效师可在脚本创作师输出分镜脚本后并行启动,无需等待视觉内容完成
效率提升: 减少等待时间,整体创作周期缩短

闭环管控

所有输出物需经质检智能体校验合格后方可进入下一环节
重大瑕疵直接拦截,避免问题流转
跟踪整改结果,确保100%解决

迭代联动

数据反馈智能体的优化建议直接作用于各智能体底层配置
**形成"创作-质检-优化"**持续迭代机制

质量检测工具使用

图片质量检测

# 调用图片质量检测脚本
python scripts/image_quality_checker.py --image ./cache/images/shot_001.jpg --resolution 1920x1080

检测维度:

肢体异常(多余肢体/畸形/重影)
画面质量(模糊/噪点/变形)
商品细节(纹理/轮廓/按键)
分辨率/比例

视频质量检测

# 调用视频质量检测脚本
python scripts/video_quality_checker.py --video ./output/final/video.mp4 --resolution 1920x1080 --duration 30 --fps 25

检测维度:

分辨率/时长/帧率/码率
画面质量(卡顿/撕裂/转场瑕疵)
音频质量(清晰度/杂音)
音画同步(偏移≤0.1秒)
字幕遮挡检测

素材生成与视频合成流程

素材生成流程

1. 图片素材生成

触发时机: 分镜画师智能体完成画面设计后

执行步骤:

# 准备分镜脚本JSON文件
cat > ./scripts/storyboard.json << 'EOF'
{
  "shots": [
    {
      "shot_id": "L01",
      "description": "智能手表特写,手指滑动屏幕",
      "duration": 3.0,
      "resolution": "1920x1080"
    }
  ],
  "style": "科技风,冷色调,清晰锐利"
}
EOF

# 调用图片生成脚本
python scripts/image_generator.py \
  --storyboard ./scripts/storyboard.json \
  --output ./output/images \
  --style "科技风,冷色调"

输出规范:

路径: ./output/images/shot_{shot_id}.jpg
格式: JPG, 1080P (1920x1080)
质量要求: 无肢体异常,商品细节清晰,画质细腻

参考: references/asset-specifications.md - 图片素材规范

2. 音频素材生成

触发时机: 配音师智能体完成旁白文本,音效师智能体完成音效方案后

执行步骤:

# 生成旁白音频
python scripts/audio_generator.py \
  --mode narration \
  --text "腕上未来,触手可及" \
  --voice "professional_male" \
  --output ./output/audio/narration_S01.wav

# 生成背景音乐
python scripts/music_generator.py \
  --style "科技风,轻快" \
  --duration 30 \
  --output ./output/audio/background_music.wav

# 混合音频(旁白+背景+音效)
python scripts/audio_generator.py \
  --mode mix \
  --narration ./output/audio/narration_S01.wav \
  --background ./output/audio/background_music.wav \
  --effects ./output/audio/sound_effects/ \
  --output ./output/audio/merged_audio.wav

输出规范:

路径: ./output/audio/
格式: WAV (44.1kHz, 16bit/24bit)
质量要求: 无杂音,音量平衡,音质清晰

参考: references/asset-specifications.md - 音频素材规范

3. 字幕素材生成

触发时机: 字幕师智能体完成字幕设计后

执行步骤:

# 准备分镜脚本
cat > ./scripts/storyboard.json << 'EOF'
{
  "shots": [
    {
      "shot_id": "L01",
      "duration": 3.0,
      "text": "腕上未来"
    },
    {
      "shot_id": "L02",
      "duration": 3.0,
      "text": "触手可及"
    }
  ]
}
EOF

# 生成SRT字幕
python scripts/subtitle_generator.py \
  --storyboard ./scripts/storyboard.json \
  --format srt \
  --output ./output/subtitles/subtitle.srt

输出规范:

路径: ./output/subtitles/subtitle.srt 或 .ass
格式: SRT/ASS (UTF-8编码)
质量要求: 无错别字,字数适配,位置无遮挡

参考: references/asset-specifications.md - 字幕素材规范

视频合成流程

触发时机: 所有素材(图片/音频/字幕)准备完成并质检合格后

执行步骤:

# 准备项目配置
cat > ./scripts/project_config.json << 'EOF'
{
  "images_dir": "./output/images",
  "audio_file": "./output/audio/merged_audio.wav",
  "subtitle_file": "./output/subtitles/subtitle.srt",
  "shots": [
    {
      "shot_id": "L01",
      "duration": 3.0,
      "transition": "fade"
    },
    {
      "shot_id": "L02",
      "duration": 3.0,
      "transition": "cut"
    }
  ],
  "width": 1920,
  "height": 1080,
  "fps": 25,
  "duration": 30,
  "bitrate": "8000k"
}
EOF

# 验证项目配置
python scripts/video_compositor.py \
  --config ./scripts/project_config.json \
  --output ./output/temp/validate.mp4 \
  --validate_only

# 合成最终视频
python scripts/video_compositor.py \
  --config ./scripts/project_config.json \
  --output ./output/final/final_video_$(date +%Y%m%d).mp4

输出规范:

路径: ./output/final/final_video_{日期}.mp4
格式: MP4 (H.264编码)
参数: 1080P, 25fps, 8Mbps
质量要求: 无卡顿,音画同步≤0.1秒,字幕同步无延迟

参考: references/asset-specifications.md - 视频输出规范

质量检测流程

1. 图片质量检测

python scripts/image_quality_checker.py \
  --image ./output/images/shot_L01.jpg \
  --resolution 1920x1080 \
  --check_limb_anomaly \
  --check_blur \
  --check_deformation

2. 音频质量检测

python scripts/audio_quality_checker.py \
  --audio ./output/audio/narration_S01.wav \
  --sample_rate 44100 \
  --check_noise

3. 视频质量检测

python scripts/video_quality_checker.py \
  --video ./output/final/final_video_20240122.mp4 \
  --resolution 1920x1080 \
  --duration 30 \
  --fps 25 \
  --check_sync

参考: references/quality-standards.md - 质检标准

资源索引

必要脚本:
- scripts/image_generator.py - 图片生成脚本(根据分镜脚本生成1080P图片)
- scripts/audio_generator.py - 音频生成脚本(TTS旁白+背景音乐混合)
- scripts/subtitle_generator.py - 字幕生成脚本(SRT/ASS格式)
- scripts/music_generator.py - 背景音乐生成脚本
- scripts/video_compositor.py - 视频合成主脚本(整合图片/音频/字幕)
- scripts/image_quality_checker.py - 图片质量检测工具(肢体异常/模糊/变形)
- scripts/video_quality_checker.py - 视频质量检测工具(含音画同步)
领域参考:
- references/agent-prompts.md - 11个智能体的详细提示词模板
- references/quality-standards.md - 质检标准与负面规避清单
- references/asset-specifications.md - 素材生成与输出规范(图片/音频/字幕/视频)
- references/data-analysis.md - 数据统计与迭代优化模板
输出资产:
- assets/templates/storyboard-template.md - 分镜脚本标准模板
- assets/reports/quality-report-template.md - 质检报告模板

注意事项

智能体协同: 严格按照11个智能体的触发顺序和协同规则执行
素材规范: 所有素材生成必须遵循asset-specifications.md中的规范
视频合成: 合成前必须确保所有素材(图片/音频/字幕)就位并质检合格
质量优先: 所有输出物必须经质检智能体校验,重大瑕疵直接拦截
闭环迭代: 数据反馈智能体的优化建议必须应用于下一轮创作
技术检测: 视频合成前必须调用质量检测脚本,素材不合格不得合成
数据沉淀: 质检台账和创作数据必须完整记录,用于迭代优化

使用示例

示例1:完整创作流程

# 用户请求: "为智能手表创作30秒短视频,科技风,目标年轻人"

# 智能体执行流程:
1. 文案创作师: 输出Slogan "腕上未来,触手可及" + 分镜文案
2. 故事策划师: 输出9个镜头的故事线
3. 脚本创作师: 输出标准化分镜脚本(时长、场景、动作、文案)
4. 分镜导演: 输出9个镜头的画面规范 + 负面规避清单
5. 分镜画师: 生成9张1080P分镜图片(调用质量检测脚本)
6. 字幕师: 输出字幕信息包(并行执行)
7. 音效师: 输出音效方案包(并行执行)
8. 质检智能体: 检测分镜图片,发现第3张图片肢体异常,反馈分镜画师重绘
9. 分镜画师: 重新生成第3张图片,质检合格
10. 视频工程师: 合成视频(调用视频质量检测脚本)
11. 质检智能体: 检测成品视频,音画同步0.08秒,合格
12. 数据反馈: 输出数据统计报告 + 迭代建议

示例2:图片质量检测

from scripts.image_quality_checker import check_image_quality

result = check_image_quality(
    image_path="./cache/images/shot_003.jpg",
    expected_resolution=(1920, 1080),
    check_limb_anomaly=True,
    check_blur=True,
    check_deformation=True
)

if not result['overall']:
    print(f"发现瑕疵: {result['issues']}")
    # 触发分镜画师重绘
else:
    print("图片质量合格,可用于合成")

示例3:质检闭环示例

质检智能体检测:
- 分镜图片: 合格率92%(10张图片,8张合格,1张轻微瑕疵,1张重大瑕疵)
  - 重大瑕疵: shot_007.jpg 发现多余肢体,驳回分镜画师重绘
  - 轻微瑕疵: shot_003.jpg 轻微模糊,反馈优化

成品视频: 合格
- 分辨率: 1920x1080 ✓
- 帧率: 25fps ✓
- 音画同步: 0.08秒 ✓
- 字幕遮挡: 无 ✓

数据反馈智能体:
- 高频瑕疵TOP3: 
  1. 肢体异常(占比8%) → 优化分镜导演/画师提示词
  2. 轻微模糊(占比6%) → 调整画质标准
  3. 字幕位置偏差(占比4%) → 优化字幕师模板
- 效率瓶颈: 分镜画师重绘耗时过长 → 调整前置质检规则

About this skill

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Source

Imported

anbeime / skill

skills/video-creation-collaborator/video-creation-collaborator/SKILL.md

View on GitHub ↗

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

Illustrative Want to see what anbeime/video-creation-collaborator 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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anbeime/product-video-creator多智能体协同的商品视频创作流水线，支持从商品信息到成视频的全流程创作：文案生成、故事策划、脚本创作、分镜设计、图片生成、字幕创作、音效推荐及视频合成；适用于电商商品宣传、品牌推广、社交媒体营销等场景Close match+32 pts pass-rate lift

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影品智创 - 多智能体协同视频创作管理

任务目标

前置准备

操作步骤

标准流程(5个阶段,10个智能体)

第一阶段: 需求承接与内容框架搭建

第二阶段: 视觉内容创作与质检

第三阶段: 音频字幕创作(并行执行)

第四阶段: 视频合成与成品质检

第五阶段: 数据沉淀与迭代优化

并行协同与闭环管控

并行执行

闭环管控

迭代联动

质量检测工具使用

图片质量检测

视频质量检测

素材生成与视频合成流程

素材生成流程

1. 图片素材生成

2. 音频素材生成

3. 字幕素材生成

视频合成流程

质量检测流程

1. 图片质量检测

2. 音频质量检测

3. 视频质量检测

资源索引

注意事项

使用示例

示例1:完整创作流程

示例2:图片质量检测

示例3:质检闭环示例

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