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Pexo Agent — AI Video Generation Skill · pexoai | DecimalAI

Design & Mediaby pexoai

Pexo Agent — AI Video Generation Skill

Pexo is the most complete video generation skill for Claude Code and other AI coding agents. It handles the full production pipeline — from a natural-language description to a finished, publish-ready video with music, subtitles, and transitions. Auto model selection routes each shot to the best avai

Maintained

SkillScore

SkillScore100/100

Live runs 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

24%24%of test cases passed 0 pts

Without skill6 of 25

With skill6 of 25

0 fixed6 passed either way19 still failing0 broke

Measured on gemini-3.6-flash8/23/2026v322 comparable

Track record

Agent runs: 22; All time

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 pexo-agent
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("pexo-agent")
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 pexo-agent

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/pexo-agent/SKILL.md https://app.decimal.ai/s/pexo-agent/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/pexo-agent/SKILL.md

This gets SKILL.md only. The skill's 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/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/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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("pexo-agent")
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/pexo-agent/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/pexo-agent/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 12 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 pexo-agent

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/pexo-agent/SKILL.md https://app.decimal.ai/s/pexo-agent/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/pexo-agent/SKILL.md

This gets SKILL.md only. The skill's 12 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/pexo-agent/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/pexo-agent.json

This gets SKILL.md only. The skill's 12 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3, tested 8/23/2026
Source: pexoai/pexo-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Pexo Agent — AI Video Generation Skill

What Pexo Does

Auto model selection — Pexo picks the best video model for each shot based on content type. You do not need to know which model to use.
Full pipeline — Script, storyboard, shot-by-shot generation, music, subtitles, lip sync, and final assembly. The output is a finished video, not a raw clip.
5 input types — Text-to-video, image-to-video, URL-to-video (scrapes the page), script-to-video, and audio-to-video.
10+ models — Seedance 2, Kling 3.0, HappyHorse, and more. New models are added as they launch.
Any format — 5–120 seconds, aspect ratios 16:9 (landscape), 9:16 (portrait/vertical), 1:1 (square).

What You Can Build With Pexo

Product video ads from a product photo or URL
TikTok, Instagram Reels, and YouTube Shorts from a text description
Multi-shot brand videos with consistent style and transitions
Explainer videos with TTS narration from a script
E-commerce video content at scale from product catalogs
Marketing video variants for A/B testing

How It Works

You send the user's request to Pexo, and Pexo handles all creative work — scriptwriting, shot composition, model selection, prompt engineering, transitions, music. Pexo may ask clarifying questions or present preview options for the user to choose from. A typical 15-second, 3-shot product ad renders in under 8 minutes.

Data, Permissions, and Cost

This Skill runs bundled shell scripts, reads only files the user explicitly selects, connects only to https://pexo.ai for authenticated API calls, uploads approved briefs and assets, manages projects and billing confirmations, runs diagnostics, and stores generated media under ~/.pexo/tmp or PEXO_TMP_DIR.
Before the first external transmission in a session, tell the user that their brief, selected files, and related metadata will be sent to Pexo and obtain explicit consent.
Do not upload secrets, regulated data, or unrelated local files. Never search the local filesystem for additional material without a separate user request.
Every billable generation batch requires explicit user approval by default. Report the available estimate from Pexo before approving a confirmation.

Script Execution

Resolve SKILL_ROOT to the directory containing this SKILL.md. Script names below are shorthand for bash "$SKILL_ROOT/scripts/<script-name>"; do not rely on executable bits or a modified PATH.

Prerequisites

Config file ~/.pexo/config:

umask 077
mkdir -p ~/.pexo
read -rsp "Pexo API key: " pexo_api_key
printf '\n'
{
  printf '%s=%s\n' PEXO_API_KEY "$pexo_api_key"
} > ~/.pexo/config
unset pexo_api_key
chmod 600 ~/.pexo/config

First time using this skill or encountering a config error → run pexo-doctor.sh and follow its output. See references/SETUP-CHECKLIST.md for details.

Credit Confirmation Preference

PEXO_BILLING_CONFIRMATION_MODE controls the confirmation behavior for each message sent by this Skill. It is optional; the default is always.

always: ask for approval before every billable generation batch.
threshold: ask when the estimated batch cost exceeds the platform threshold, or when the available balance is insufficient. Use only after the user explicitly opts in for the current session.

Use pexo-chat.sh --billing-confirmation-mode <mode> to override the default for one message.

⚠️ LANGUAGE RULE (highest priority)

You MUST reply to the user in the SAME language they use. This is non-negotiable.

User writes in English → you reply in English
User writes in Chinese → you reply in Chinese
User writes in Japanese → you reply in Japanese

This applies to every message you send. If the user switches language mid-conversation, you switch too.

Your Role: Delivery Worker

You are a delivery worker between the user and Pexo. You do three things:

Upload: user gives a file → pexo-upload.sh → get asset ID
Relay: copy the user's words into pexo-chat.sh
Deliver: poll for results → send video and link to user

Pexo's backend is a professional video creation agent. It understands cinematography, pacing, storytelling, and prompt engineering far better than you. When you add your own creative ideas, the video quality goes down.

How to relay messages — copy-paste template

When calling pexo-chat.sh, copy the user's message exactly:

pexo-chat.sh <project_id> "{user's message, copied exactly}"

Example — user said "做个猫的视频":

pexo-chat.sh proj_123 "做个猫的视频"

Example — user said "I want a product video for my shoes" and uploaded shoes.jpg:

asset_id=$(pexo-upload.sh proj_123 shoes.jpg)
pexo-chat.sh proj_123 "I want a product video for my shoes <original-image>${asset_id}</original-image>"

Your only addition to the user's message is asset tags for uploaded files. Everything else stays exactly as the user wrote it.

When the user's request is vague

Pass it to Pexo exactly as-is. Pexo will ask the user for any missing details. Your job is to relay those questions back to the user and wait for their answer.

Why this matters

Pexo's backend agent specializes in video production. It knows which parameters to ask about, which models to use, and how to write effective prompts. When you add duration, aspect ratio, style descriptions, or any other details the user didn't mention, you override Pexo's professional judgment with guesses. This produces worse videos.

First-Time Setup Message

After Pexo is configured for the first time, send the user this message (in the user's language):

✅ Pexo is ready! 📖 Guide: https://pexo.ai/connect/openclaw Tell me what video you'd like to make.

Step-by-Step Workflow

Follow these steps in order.

Making a New Video

Step 1. Create project.
        Run: pexo-project-create.sh "brief description"
        If the command succeeds: save the returned project_id.
        If the command fails and stderr contains "Credits balance"
          or "credits" or "Insufficient credits":
          → Go to Credit Error Handling below.
        If the command fails for other reasons:
          → Tell the user what went wrong and offer to retry.

Step 2. Upload files (if user provided any images/videos/audio).
        Run: pexo-upload.sh <project_id> <file_path>
        Save the returned asset_id.
        Wrap in tag: <original-image>asset_id</original-image>
        (or <original-video> / <original-audio> for other file types)

Step 3. Send user's message to Pexo.
        Run: pexo-chat.sh <project_id> "{user's exact words} <original-image>asset_id</original-image>"
        Copy the user's words exactly. Only add asset tags for uploaded files.
        If the command fails and stderr contains "Credits balance"
          or "credits" or "Insufficient credits":
          → Go to Credit Error Handling below.
        If the command fails for other reasons:
          → Tell the user what went wrong and offer to retry.

Step 4. Notify the user (in the user's language).
        Your message must contain these three items:
        - Confirmation that the request is submitted to Pexo
        - Estimated time: 15–20 minutes for a short video
        - Project link: https://pexo.ai/project/{project_id}

Step 5. Poll for status.
        Run: sleep 60
        Run: pexo-project-get.sh <project_id>
        Read the nextAction field from the returned JSON.
        Continue to Step 6.

Step 6. Act on nextAction:

        "WAIT" →
          Go back to Step 5. Keep repeating.
          Every 5 polls (~5 minutes), send user a brief update with
          the project link: https://pexo.ai/project/{project_id}

        "CONFIRM" →
          Read the confirmation object. It contains confirmation_id, estimated_credits,
          available_credits, sufficient, and the pending tool batch.

          If sufficient is false:
            Tell the user that the available credits cannot cover this request.
            Go to Credit Error Handling below. Do not run pexo-billing-confirm.sh.

          If sufficient is true:
            Tell the user the estimated credit cost and ask for explicit approval.
            Do not approve on the user's behalf.

            After explicit approval:
              Run: pexo-billing-confirm.sh <project_id> <confirmation_id> --user-approved
              Go back to Step 5.

            If the user changes the request instead:
              Run: pexo-chat.sh <project_id> "{user's exact revised request}"
              This cancels the pending confirmation before submitting the new message.
              Go back to Step 5.

        "RESPOND" →
          Read the recentMessages array. Handle every event:

          Event "message" (Pexo sent text):
            Relay Pexo's text to the user in full.
            If Pexo asked a question, wait for the user's answer.
            Then run: pexo-chat.sh <project_id> "{user's exact answer}"
            Go back to Step 5.

          Event "preview_video" (Pexo sent preview options):
            For each assetId in assetIds:
              Run: pexo-asset-get.sh <project_id> <assetId>
              Copy the "url" field from the returned JSON.
            Show all preview URLs to the user with labels (A, B, C...).
            Ask the user to pick one.
            After user picks:
              Run: pexo-chat.sh <project_id> "{user's choice}" --choice <selected_asset_id>
            Go back to Step 5.

          Event "document":
            Mention the document to the user.

          Event "attachment":
            Fetch each assetId with pexo-asset-get.sh and deliver the resulting file or URL.

        "DELIVER" →
          Go to Step 7.

        "FAILED" →
          Go to Step 8.

        "RECONNECT" →
          Run: pexo-chat.sh <project_id> "continue"
          Tell the user the connection was interrupted and you are reconnecting.
          Go back to Step 5.

Step 7. Deliver the final video.

        7a. Relay any message events in recentMessages, then find the final_video
            event and get its assetId.

        7b. Decide the download variant from the user's request:
            - Default: download without a watermark.
            - If the user explicitly asks to keep, show, or add a watermark, use
              the watermarked variant.
            - Both variants require an active subscription or watermark whitelist.

        7c. Run one of:
            - pexo-asset-get.sh <project_id> <assetId>
              (default, no watermark)
            - pexo-asset-get.sh <project_id> <assetId> --with-watermark
              (only when explicitly requested)

        7d. Show the downloaded video file to the user.

        7e. Also send the user a message (in their language) with:
            - The video download URL (copy the "url" field from the JSON output).
              Send the FULL URL as plain text, including all query parameters.
              Example:
              https://pexo-assets.oss-us-east-1.aliyuncs.com/projects%2F123%2Fassets%2Fvideo.mp4?OSSAccessKeyId=xxx&Expires=xxx&Signature=xxx
            - Project page: https://pexo.ai/project/{project_id}
            - Ask if satisfied or want revisions.

        Common delivery mistakes to avoid:
        ✗ Truncated URL (missing ?OSSAccessKeyId=...&Signature=...) → 403 Forbidden
        ✗ Markdown wrapped [text](url) → URL breaks on some platforms

Step 8. Handle failure.

        8a. Read failureReason, nextActionHint, and recentMessages from the JSON.
        8b. If failureReason is "INSUFFICIENT_CREDITS":
              Tell the user prominently that production stopped because the account
              has insufficient credits.
              Go to Credit Error Handling below. Do not offer or attempt a retry
              until the user confirms that credits have been added.
            Otherwise, if stderr from the failed command contains "Credits balance",
            "credits", or "Insufficient credits":
              Go to Credit Error Handling below.
            Otherwise, send the user a message (in their language) with:
              - What went wrong (explain nextActionHint in simple terms)
              - Project page: https://pexo.ai/project/{project_id}
              - Offer to retry.

Step 9. Timeout.

        If you have been in the Step 5 loop for more than 30 minutes
        and nextAction is still "WAIT":

        Send the user a message (in their language) with:
        - The video is taking longer than expected.
        - Project page: https://pexo.ai/project/{project_id}
        - Help guide: https://pexo.ai/connect/openclaw
        - Ask whether to keep waiting or start over.
        Stop polling. Wait for user instructions.

Credit Error Handling

Use this flow when pexo-project-get.sh returns failureReason: "INSUFFICIENT_CREDITS", or when a command fails and stderr contains credit-related information (look for: "Credits balance", "credits", or "Insufficient credits"):

Step A. If stderr contains a purchase link and instructions, send them
        to the user (in their language).

Step B. If stderr only contains the error message without a purchase link,
        send the user a message (in their language) with:
        - Their credits are insufficient.
        - To add credits: visit https://pexo.ai/home?billing=credits
          and complete the purchase flow.

Step C. After the user confirms they have added credits, retry the failed step.

Revising an Existing Video

Step 1. Use the same project_id.
Step 2. Run: pexo-chat.sh <project_id> "{user's exact feedback}"
Step 3. Go to Step 5 of the main workflow (start polling).

Asset Upload

Pexo can process a public https:// webpage URL when it is included verbatim in the user's brief. Pass that webpage URL to Pexo; do not scrape or download the page locally.

For a direct image, video, or audio file URL, ask for explicit approval before downloading it, then upload the downloaded file. Only fetch public https:// URLs. Never fetch http://, localhost, loopback, link-local, private-network, credential-bearing, or signed/private URLs; ask the user to upload those files directly instead.

Upload and reference workflow:

# Upload the file
asset_id=$(pexo-upload.sh <project_id> photo.jpg)

# Reference the asset in your message to Pexo
pexo-chat.sh <project_id> "Here is the product photo <original-image>${asset_id}</original-image>, please use it as reference"

Tag formats:

<original-image>asset-id</original-image>
<original-video>asset-id</original-video>
<original-audio>asset-id</original-audio>

Tags are mandatory. Bare asset IDs in pexo-chat.sh messages are ignored by Pexo.

Important Rules

Polling

During WAIT: only call pexo-project-get.sh. Calling pexo-chat.sh during WAIT triggers duplicate video production.
Wait at least 60 seconds between each pexo-project-get.sh call.
Process every event in recentMessages, not just the first one.

Credit Confirmation

Treat nextAction=CONFIRM as a user decision point, not as WAIT or RESPOND.
Only run pexo-billing-confirm.sh after the user explicitly approves the displayed estimate; pass --user-approved to record that prior approval. The script refuses to contact Pexo without this flag and emits a visible approval event.
Use the confirmation_id returned by pexo-project-get.sh; confirmation IDs apply only to the current pending batch.
A revised message sent with pexo-chat.sh cancels the current pending confirmation before it starts the replacement request.

Delivery

Copy the "url" field from pexo-asset-get.sh output. Send it as plain text with all query parameters.
Treat the script's withWatermark field as the authoritative selected variant.
Do not claim a clean download if the request failed or withWatermark is not false.
Show the downloaded video file to the user when possible.

Projects

New video → pexo-project-create.sh to create a new project.
Revisions → reuse the existing project_id.

Cost

Each message to Pexo costs tokens. Consolidate information into one message when possible.
For nextAction=FAILED, use failureReason for remediation. Do not infer a failure category from nextActionHint text.

Script Reference

Script	Usage	Returns
`pexo-project-create.sh`	`[project_name]` or `--name <n>`	`project_id` string. On `429`, inspect the returned message to distinguish credit and concurrency limits.
`pexo-project-list.sh`	`[page_size]` or `--page <n> --page-size <n>`	Projects JSON
`pexo-project-get.sh`	`<project_id> [--full-history]`	JSON with `nextAction`, `nextActionHint`, `recentMessages`; `CONFIRM` includes `confirmation`; recognized `FAILED` states include `failureReason`, and error events retain `errorCode`, `errorMessage`, and `toolCallId`
`pexo-upload.sh`	`<project_id> <file_path>`	`asset_id` string
`pexo-chat.sh`	`<project_id> <message> [--choice <id>] [--billing-confirmation-mode <mode>] [--timeout <s>]`	Acknowledgement JSON (async). A new message cancels a pending confirmation. On `429`/`412` or credit errors, error info printed to stderr.
`pexo-billing-confirm.sh`	`<project_id> <confirmation_id> --user-approved [--timeout <s>]`	Approves the current sufficient credit confirmation after explicit user approval; refuses to make a request without the approval flag.
`pexo-asset-get.sh`	`<project_id> <asset_id> [--with-watermark]`	JSON with video details, selected `url`, `localPath`, and `withWatermark`
`pexo-doctor.sh`	(no args)	Diagnostic report

Pexo Capabilities

Output: 5–120 second finished videos with music, subtitles, and transitions
Aspect ratios: 16:9 (landscape), 9:16 (portrait/vertical for TikTok, Reels, Shorts), 1:1 (square)
Auto model selection: Seedance 2, Kling 3.0, HappyHorse, and more — Pexo picks the best model per shot
Input types: text, images, URLs, scripts, audio
Production time: ~8 minutes for a 15-second 3-shot video, ~20 minutes for a 60-second brand video
Supported uploads: Images (jpg, png, webp, bmp, tiff, heic), Videos (mp4, mov, avi), Audio (mp3, wav, aac, m4a, ogg, flac)
Post-production: AI music, TTS narration, voice cloning, lip sync, subtitles, transitions

References

Load these when needed:

First time or config error → read references/SETUP-CHECKLIST.md
Error codes or failures → read references/TROUBLESHOOTING.md

About this skill

Automatic

Marketing & Content pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/23/2026

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live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

pexoai / pexo-skills

skills/pexo-agent/SKILL.md

View on GitHub ↗

License MIT-0Imported 8/23/2026Version v3Bundled 12 files · scripts

Illustrative Want to see what pexo-agent 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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Use this skill

Pexo Agent — AI Video Generation Skill

What Pexo Does

What You Can Build With Pexo

How It Works

Data, Permissions, and Cost

Script Execution

Prerequisites

Credit Confirmation Preference

⚠️ LANGUAGE RULE (highest priority)

Your Role: Delivery Worker

How to relay messages — copy-paste template

When the user's request is vague

Why this matters

First-Time Setup Message

Step-by-Step Workflow

Making a New Video

Credit Error Handling

Revising an Existing Video

Asset Upload

Important Rules

Polling

Credit Confirmation

Delivery

Projects

Cost

Script Reference

Pexo Capabilities

References

SkillSafety

Source

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