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Video Compose · utopai-research | DecimalAI

Design & Mediaby utopai-research

Video Compose

Generates and prompts video clips on the filmmaking canvas.

Maintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%86%of test cases passed +73 pts

Without skill3 of 22

With skill19 of 22

16 fixed3 passed either way3 still failing0 broke

Measured on gemini-3.6-flash9/23/2026v2

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI need to trim the first 3 seconds off an existing 20-second MP4 video file on disk (`/workspace/input.mp4`) and save it as `/workspace/trimmed.mp4` without running AI video generation. Construct the command to perform this video edit.

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 utopai-research-video-compose
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("utopai-research-video-compose")
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 utopai-research-video-compose

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/utopai-research-video-compose/SKILL.md https://app.decimal.ai/s/utopai-research-video-compose/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/utopai-research-video-compose/SKILL.md

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

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

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

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

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

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

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

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

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

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/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/utopai-research-video-compose/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

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

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

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

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

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

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

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

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

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

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add utopai-research-video-compose

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("utopai-research-video-compose")
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/utopai-research-video-compose/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/utopai-research-video-compose/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

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

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add utopai-research-video-compose

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/utopai-research-video-compose/SKILL.md https://app.decimal.ai/s/utopai-research-video-compose/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/utopai-research-video-compose/SKILL.md

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

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/utopai-research-video-compose/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/utopai-research-video-compose.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 9/23/2026
Source: utopai-research/pai-pro ↗
License: NOASSERTION

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

Intent dispatcher. Patterns name trigger, call, edges, and prompt reference.

Hard defaults

Stage by default per PROJECT_AGENT.md.
Audio on by default; pass --no-audio only for explicit silent/no-audio requests. Trailer/portrait/cinematic framing is NOT a trigger; audio is the baseline, not optional polish.
Reference-to-clip default: use available character/variant/location/voice refs directly. Storyboard only if requested, hard to control, or needed for diagnosis.
Preserve scripted dialogue/VO exactly unless the user asks for rewrite.

First-use video mode

For the ask-once flow and per-mode prices, see the project PROJECT_AGENT.md § "First-use generation choices". Pass --resolution only for 480p Draft or 1080p Final.

CLI shape

node "$PAI_REPO_ROOT/server/cli/generate_video.js" --prompt "..." [--version <2.0|2.5>] [--duration <seconds>]
  [--aspect-ratio 16:9] [--resolution <480p|720p|1080p>] [--no-audio]
  [--label "..."] [--ref-source-id <id> ...] [--ref-audio-source-id <audio_id> ...]
  [--source-node-id <id>] [--shot-id <N>]

Calls go via --stage — see the project PROJECT_AGENT.md § "Draft gate".

--label defaults to truncated prompt. Use --ref-source-id for image/video refs, --ref-audio-source-id for audio refs, and --source-node-id for the authoring note. Mirror external URLs first. Do not set --shot-id during speculative/partial generation unless user asks for a reel position; story sequences assign Timeline order after planned clips land.

Match stated single-clip duration with --duration; omit for 15s default. Split or chain >15s totals.

Each clip costs real money even after staging — only stage after the user has explicitly asked for a video.

Model version (`--version`)

Omit it for 2.0 (today's model): output 4-15s, priced per output second by resolution.

--version 2.5 renders PAI Video 2.5. Differences that change how you call it:

--duration is 5-30 (not 4-15). -1 (follow-the-input edit mode) is rejected.
--resolution must be 480p, 720p, or 1080p; --aspect-ratio must be one of 16:9 | 9:16 | 4:3 | 3:4 | 1:1 | 21:9 | adaptive.
One flat price per billed-duration tier, where billed = output seconds + every reference-video second (each clip measured locally with ffprobe and rounded up). Hard cap 60 billed seconds; over that the CLI refuses before staging and names both halves. Tier prices are in the project PROJECT_AGENT.md § "Media CLIs (server/cli/)" — quote them from there, never from memory.
Refused inside an Auto run (the run's budget estimate is computed from 2.0 rates). Stage 2.5 clips outside Auto.
A 2.5 job carrying video references needs ffprobe on the host; without it the CLI fails with infra rather than guessing a price.

On failure the limits blob is model-scoped: read min_output_sec / max_output_sec and max_billed_sec off the returned JSON rather than assuming 2.0's numbers.

Reference caps

2.0 (video-generation) — ≤9 image refs, ≤3 audio refs, ≤3 video refs; video refs cap at 15s aggregate.

2.5 — ≤30 image refs, ≤10 audio refs, ≤10 video refs; reference video caps at 30s aggregate, and every reference second also counts toward the 60s billed-duration cap.

Both: audio/video refs must be 1.8s-15.2s each (the shared reference-upload step's window), and audio refs need an image or video anchor — on 2.5 that one is checked only after credits are frozen, so the CLI refuses it locally. Read durations from workflow.json; on failure use the returned limits + sent, which are model-scoped.

Reference roles — vocabulary

Prompt wording binds each ref role:

Role	Flag	Wording in prompt
Character identity	`--ref-source-id` (image)	"the character in @Image1"
Location / setting	`--ref-source-id` (image)	"the location shown in @Image1"
Opening frame	`--ref-source-id` (image)	"opening frame @Image1, …"
Closing frame	`--ref-source-id` (image)	"closing on the frame from @Image1"
Source clip — continue (next clip in a chain)	`--ref-source-id` (video)	Default = hard cut: "Hard cut from @Video1: open on a NEW camera angle; do not match its final frame." Same-shot ("Continue from @Video1 … maintain camera position") only for an authored held beat / oner / explicit user request — see `references/video-extension.md`
Source clip — transform	`--ref-source-id` (video)	"Re-render @Video1 in …"
Camera-move source	`--ref-source-id` (video)	"camera moves match @Video1"
Action source	`--ref-source-id` (video)	"action choreography matches @Video1"
VFX template	`--ref-source-id` (video)	"use the visual-effects template from @Video1"
Voice / timbre anchor	`--ref-audio-source-id`	"Use @Audio1 as voice/timbre reference. Speak once, no echo."

Prompt-language conventions

Ref syntax: @Image1 / @Video1 / @Audio1, positional by flag order. Every @ImageN/@VideoN/@AudioN MUST have a matching --ref-source-id/--ref-audio-source-id flag — the CLI rejects a mismatch (bad_args) before generating. Mentioning the same ref many times is fine; only the highest index per kind needs a flag.
Spoken text: include script/shot/user dialogue/VO verbatim; do not summarize, translate, shorten, polish, or invent.
Dialogue scenes: keep the shot/script dialogue in the prompt; use one approved voice sample per speaker as a timbre anchor. Bind each quoted line to the intended character and the matching @AudioN reference. Do not generate per-line audio refs unless the user explicitly wants separate final audio.
Final audio exception: if an audio node is the approved narration/line read, use audio_result.data.text verbatim. If it is just a character voice sample, do not replace the shot dialogue with the sample text.
Add dialogue guards for model-spoken lines: "each line spoken exactly once, no echo, no repeated reads." Add phonetic spelling for names or words likely to slur.
One camera move, one action speed, concrete sound/music (No Music if none). Use exact terms: locked off, handheld, subtle, slow dolly in, slow orbit, whip pan, speed ramp.
Avoid conflicts ("static camera" + "orbit shot").
For brand / MV / ad work, end the prompt with a negative line: "no captions, watermarks, distortion, stretching."
For polish on a single-shot clip: see references/video-single-shot.md.

Patterns

Pick the one that fits. Source lookup follows PROJECT_AGENT.md.

Storyboard guard: storyboard images route to Pattern 7 / references/video-multi-shot.md, never generic I2V/opening-frame wording.

1. Standalone T2V

Triggers: fresh clip unrelated to canvas content. Call: node "$PAI_REPO_ROOT/server/cli/generate_video.js" --prompt "..."; omitted flags default to 15s, 16:9, 720p, audio on. Add --resolution 480p or --resolution 1080p only if the chosen video mode requires it. Edges: none. For the bracket scaffold and slot-by-slot construction when the user wants polish: see references/video-single-shot.md.

2. Animate a canvas image (I2V)

Triggers: animate/make video/put motion on a specific canvas image_result. Source: named image_result; storyboard mosaics route through Storyboard guard. Call: node "$PAI_REPO_ROOT/server/cli/generate_video.js" --prompt "..." --ref-source-id <image.id>. Edges: { from: <source.id>, to: video_<N>, kind: "derived" } — emitted by the CLI. Anchor wording: opening-frame default (opening frame @Image1) or closing-frame (closing on the frame from @Image1); both use --ref-source-id. For slot-by-slot construction and the opening- vs closing-frame phrasing: see references/video-single-shot.md.

3. Compose with canvas characters / locations

Triggers: video of character, character in setting, character action in location. Source: character / location image_result nodes (cap from §Reference caps). Call: node "$PAI_REPO_ROOT/server/cli/generate_video.js" --prompt "..." --ref-source-id <char1.id> --ref-source-id <char2.id> .... Edges: { from: <char.id>, to: video_<N>, kind: "derived" } — one per --ref-source-id.

Prefer exact shot refs: current wardrobe/state character sheet, detailed location, same-location variant. For single-shot composition and adjacent-role wording: see references/video-single-shot.md. For ≥2 internal shots in one render: see references/video-multi-shot.md.

4. Extend a canvas clip

Triggers: continue/extend/what happens after/scene follows existing video_result. Source: any canvas video_result node — agent-generated or user-uploaded (data.metadata.source is "pai" for generated and "user_upload" for dropped). Call: node "$PAI_REPO_ROOT/server/cli/generate_video.js" --prompt "..." --ref-source-id <source_video.id>. Edges: { from: <source_video.id>, to: video_<N>, kind: "derived" }. Boundary defaults to a HARD CUT (clip 2 opens on a new angle — this avoids the same-shot seam morph). If the whole sequence fits ≤15s, render ONE multi-shot clip (Pattern 7) instead of chaining. Same-shot continuation is the exception (authored held beat / story-required oner / explicit user request). For the hard-cut + same-shot prefixes, the ≤15s guard, the sub-intent decision tree, and sequencing across linked calls: see references/video-extension.md.

5. Edit a canvas clip

Triggers: re-render/restyle/add/remove/swap/change/rewrite existing video_result. Creative edits use generate_video.js; ffmpeg is for mechanical ops. Source: any canvas video_result node — agent-generated or user-uploaded. Call: node "$PAI_REPO_ROOT/server/cli/generate_video.js" --prompt "..." --ref-source-id <source_video.id>. Edges: { from: <source_video.id>, to: video_<N>, kind: "derived" }. For the Restyle / Partial / Replace / Re-plot decision tree and per-mode templates: see references/video-editing.md.

6. Voice-driven clip

Triggers: have character say/narrate, use character voice. Source: any canvas audio_result node — agent-generated or user-uploaded. Call: node "$PAI_REPO_ROOT/server/cli/generate_video.js" --prompt "..." --ref-audio-source-id <audio_id>. Often combined with character image refs for face + voice — pass both --ref-source-id <character_id> (for the character image) and --ref-audio-source-id <audio_id> (for the voice). Prompt:

Character voice sample: The character in @Image1 says exactly: "...". Use @Audio1 as the voice/timbre reference only. Speak the quoted line exactly once, no echo, no repeated reads.
Multiple speakers: bind each visual ref and audio ref explicitly, e.g. The character in @Image1 uses @Audio1 and says exactly: "..."; the character in @Image2 uses @Audio2 and replies exactly: "...". Each line is spoken exactly once, no echo, no repeated reads.
Approved final narration/line read: use audio_result.data.text exactly and bind it with Use @Audio1 for timing, cadence, and voice. Keep the words unchanged.
No audio node: preserve requested dialogue verbatim as [Character] says exactly: "..."; add the once/no-echo guard and phonetic spellings for risky words.
Audio uploads without data.text: sound/timing only; do not invent transcript.
Images identify speakers; spoken words come from audio text, script/shot note, or user dialogue.

Edges: depends on which character refs attach (one kind: "derived" per ref).

7. Multi-shot / brand / ad / MV

Triggers: ≥2 shots inside one render, ad/MV/brand framing, or ≥10s with multiple movements. Call: node "$PAI_REPO_ROOT/server/cli/generate_video.js" --prompt "..." [--ref-source-id <image|video.id> ...] [--ref-audio-source-id <audio.id> ...]. Edges: as per the underlying pattern (3, 4, 5) for any refs attached. For the 4-section scaffold (timeline / effects inventory / density map / energy arc) and how to populate the timeline from canvas script shot notes or storyboard mosaic panels: see references/video-multi-shot.md.

Common combinations

Cross-pattern asks route to one primary reference:

Combo	Primary reference	Extra refs to attach
Character + voice-over	(Pattern 6 inline)	character image
Music video with characters	`video-multi-shot.md`	character images + audio (Pattern 6 wording)
Restyle preserving identity	`video-editing.md` (Restyle)	source video + character image
Multi-clip chained sequence	`video-extension.md`	source video for each link
Compose with camera-move from reference	`video-single-shot.md`	character images + camera-move video ref
Render one script shot from canvas	Pattern 1, 2, or 3 by shot content (no dispatch — translate the shot note body to slot rules; preserve dialogue/VO verbatim)	character / variant refs + location / variant refs + voice anchors if the shot involves them
Render a continuous script span (>15s total) as a dependent sequence	`video-extension.md` (script-driven chain; hard-cut handoffs by default — keep a link same-shot only for an unbroken oner the viewer must read as one motion)	source video per link + character refs (mandatory under hard cut) for identity
Render a short script (≤15s total) as one piece	`video-multi-shot.md` (cross-skill source)	character image refs locked across shots
Render a storyboard mosaic as one 15s video (every panel becomes a shot block)	`video-multi-shot.md` (storyboard cross-skill source; required for `image_result.subtype === "storyboard"`)	mosaic image + character / location image refs that authored the mosaic

Sequence dispatch guidance

For multiple clips, prefer hybrid dispatch: independent for separate scenes/time jumps/wardrobe-state changes/montage; chain only same continuous action/location/lighting/wardrobe-state/emotional beat. Do not chain across breaks where continuity is undesirable.

If a budget-aware caller needs savings, lower video resolution before shortening runtime. Do not suggest dropping material character variants, location variants, or voice anchors that preserve continuity.

After the CLI returns

For draft-stage JSON, one sentence with the price/status — see the project PROJECT_AGENT.md § "Draft gate". For terminal results, run the alignment check (frames via extract_frames.js) before the next-step recommendation — see the project PROJECT_AGENT.md § "Prompt alignment check". --ref-source-id flags drive provenance edges; they're captured in the draft argv and materialize on the real video_result after the user fires.

Failure hints

Video-specific message hints:

asset_rejected with "DownloadFailed" — failed_url was unreachable; swap.
asset_rejected with "DurationTooLong" / "DurationTooShort" — failed_url's duration is outside 1.8s–15.2s. Swap it or trim with ffmpeg.
bad_args with "reference_audio cannot be the only reference input" — add an image or video ref alongside the audio.
bad_args with "invalid video duration, exceeds 15s" — sum of video refs breached limits.max_total_video_sec. Read each video_result.data.duration from canvas and drop refs until the sum is ≤15s.

When it activates

Automatically, when a task matches. Use when the user asks to generate, render, animate, continue, restyle, edit, shoot, or compose a video clip; render script or shot notes as video; animate a storyboard, starting frame, image, character, location, or reference; use image, video, audio, storyboard, starting-frame, or voice refs; compose an ad, brand film, product promo, music-video shot, or video sequence; or before calling generate_video.js. Owns video CLI flags, refs,

About this skill

Automatic

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SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 9/18/2026

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LLM judge · does the skill do what it claims

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LLM judge · policy & harmful-content review

Source

Imported

utopai-research / pai-pro

skills/video-compose/SKILL.md

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License NOASSERTIONImported 9/18/2026Version v2Bundled 4 files

Illustrative Want to see what utopai-research/video-compose 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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SkillScore

Safety

What it changes

Track record

Use this skill

Hard defaults

First-use video mode

CLI shape

Model version (--version)

Reference caps

Reference roles — vocabulary

Prompt-language conventions

Patterns

1. Standalone T2V

2. Animate a canvas image (I2V)

3. Compose with canvas characters / locations

4. Extend a canvas clip

5. Edit a canvas clip

6. Voice-driven clip

7. Multi-shot / brand / ad / MV

Common combinations

Sequence dispatch guidance

After the CLI returns

Failure hints

When it activates

SkillSafety

Source

Related skills

More in the registry

Model version (`--version`)