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User approved without changes: · leoyeai | DecimalAI

Design & Mediaby leoyeai

User approved without changes:

Explain anything — turn ideas into podcasts, explainer videos, or voice narration.

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

3 of 22 test cases passed with the skill.

Measured on gemini-3.6-flash8/10/2026v24 comparable

Lift withheld: 4 comparable cases; this report requires 8.

Track record

Agent runs: 17; 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 leoyeai-listenhub
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("leoyeai-listenhub")
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 leoyeai-listenhub

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/leoyeai-listenhub/SKILL.md https://app.decimal.ai/s/leoyeai-listenhub/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-listenhub/SKILL.md

This gets SKILL.md only. The skill's 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/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/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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("leoyeai-listenhub")
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/leoyeai-listenhub/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-listenhub/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 11 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 leoyeai-listenhub

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/leoyeai-listenhub/SKILL.md https://app.decimal.ai/s/leoyeai-listenhub/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-listenhub/SKILL.md

This gets SKILL.md only. The skill's 11 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/leoyeai-listenhub/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-listenhub.json

This gets SKILL.md only. The skill's 11 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 8/10/2026
Source: LeoYeAI/openclaw-master-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

Four modes, one entry point:

Podcast — Two-person dialogue, ideal for deep discussions
Explain — Single narrator + AI visuals, ideal for product intros
TTS/Flow Speech — Pure voice reading, ideal for articles
Image Generation — AI image creation, ideal for creative visualization

Users don't need to remember APIs, modes, or parameters. Just say what you want.

⛔ Hard Constraints (Inviolable)

The scripts are the ONLY interface. Period.

┌─────────────────────────────────────────────────────────┐
│  AI Agent  ──▶  ./scripts/*.sh  ──▶  ListenHub API     │
│                      ▲                                  │
│                      │                                  │
│            This is the ONLY path.                       │
│            Direct API calls are FORBIDDEN.              │
└─────────────────────────────────────────────────────────┘

MUST:

Execute functionality ONLY through provided scripts in **/skills/listenhub/scripts/
Pass user intent as script arguments exactly as documented
Trust script outputs; do not second-guess internal logic

MUST NOT:

Write curl commands to ListenHub/Marswave API directly
Construct JSON bodies for API calls manually
Guess or fabricate speakerIds, endpoints, or API parameters
Assume API structure based on patterns or web searches
Hallucinate features not exposed by existing scripts

Why: The API is proprietary. Endpoints, parameters, and speakerIds are NOT publicly documented. Web searches will NOT find this information. Any attempt to bypass scripts will produce incorrect, non-functional code.

Script Location

Scripts are located at **/skills/listenhub/scripts/ relative to your working context.

Different AI clients use different dot-directories:

Claude Code: .claude/skills/listenhub/scripts/
Other clients: may vary (.cursor/, .windsurf/, etc.)

Resolution: Use glob pattern **/skills/listenhub/scripts/*.sh to locate scripts reliably, or resolve from the SKILL.md file's own path.

Private Data (Cannot Be Searched)

The following are internal implementation details that AI cannot reliably know:

Category	Examples	How to Obtain
API Base URL	`api.marswave.ai/...`	✗ Cannot — internal to scripts
Endpoints	`podcast/episodes`, etc.	✗ Cannot — internal to scripts
Speaker IDs	`cozy-man-english`, etc.	✓ Call `get-speakers.sh`
Request schemas	JSON body structure	✗ Cannot — internal to scripts
Response formats	Episode ID, status codes	✓ Documented per script

Rule: If information is not in this SKILL.md or retrievable via a script (like get-speakers.sh), assume you don't know it.

Design Philosophy

Hide complexity, reveal magic.

Users don't need to know: Episode IDs, API structure, polling mechanisms, credits, endpoint differences. Users only need: Say idea → wait a moment → get the link.

Environment

ListenHub API Key

API key stored in $LISTENHUB_API_KEY. Check on first use:

source ~/.zshrc 2>/dev/null; [ -n "$LISTENHUB_API_KEY" ] && echo "ready" || echo "need_setup"

If setup needed, guide user:

Visit https://listenhub.ai/settings/api-keys
Paste key (only the lh_sk_... part)
Auto-save to ~/.zshrc

Image Generation API Key

Image generation uses the same ListenHub API key stored in $LISTENHUB_API_KEY. Image generation output path defaults to the user downloads directory, stored in $LISTENHUB_OUTPUT_DIR.

On first image generation, the script auto-guides configuration:

Visit https://listenhub.ai/settings/api-keys (requires subscription)
Paste API key
Configure output path (default: ~/Downloads)
Auto-save to shell rc file

Security: Never expose full API keys in output.

Mode Detection

Auto-detect mode from user input:

→ Podcast (1-2 speakers) Supports single-speaker or dual-speaker podcasts. Debate mode requires 2 speakers. Default mode: quick unless explicitly requested. If speakers are not specified, call get-speakers.sh and select the first speakerId matching the chosen language. If reference materials are provided, pass them as --source-url or --source-text. When the user only provides a topic (e.g., "I want a podcast about X"), proceed with:

detect language from user input,
set mode=quick,
choose one speaker via get-speakers.sh matching the language,
create a single-speaker podcast without further clarification.

Keywords: "podcast", "chat about", "discuss", "debate", "dialogue"
Use case: Topic exploration, opinion exchange, deep analysis

Feature: Two voices, interactive feel

→ Explain (Explainer video)

Keywords: "explain", "introduce", "video", "explainer", "tutorial"
Use case: Product intro, concept explanation, tutorials
Feature: Single narrator + AI-generated visuals, can export video

→ TTS (Text-to-speech) TTS defaults to FlowSpeech direct for single-pass text or URL narration. Script arrays and multi-speaker dialogue belong to Speech as an advanced path, not the default TTS entry. Text-to-speech input is limited to 10,000 characters; split or use a URL when longer.

Keywords: "read aloud", "convert to speech", "tts", "voice"
Use case: Article to audio, note review, document narration
Feature: Fastest (1-2 min), pure audio

Ambiguous "Convert to speech" Guidance

When the request is ambiguous (e.g., "convert to speech", "read aloud"), apply:

Default to FlowSpeech and prioritize direct to avoid altering content.
Input type: URL uses type=url, plain text uses type=text.
Speaker: if not specified, call get-speakers and pick the first speakerId matching language.
Switch to Speech only when multi-line scripts or multi-speaker dialogue is explicitly requested, and require scripts.

Example guidance:

“This request can use FlowSpeech with the default direct mode; switch to smart for grammar and punctuation fixes. For per-line speaker assignment, provide scripts and switch to Speech.”

→ Image Generation

Keywords: "generate image", "draw", "create picture", "visualize"
Use case: Creative visualization, concept art, illustrations
Feature: AI image generation via Labnana API, multiple resolutions and aspect ratios

Reference Images via Image Hosts When reference images are local files, upload to a known image host and use the direct image URL in --reference-images. Recommended hosts: imgbb.com, sm.ms, postimages.org, imgur.com. Direct image URLs should end with .jpg, .png, .webp, or .gif.

Default: If unclear, ask user which format they prefer.

Explicit override: User can say "make it a podcast" / "I want explainer video" / "just voice" / "generate image" to override auto-detection.

Interaction Flow

Step 1: Receive input + detect mode

→ Got it! Preparing...
  Mode: Two-person podcast
  Topic: Latest developments in Manus AI

For URLs, identify type:

youtu.be/XXX → convert to https://www.youtube.com/watch?v=XXX
Other URLs → use directly

Step 2: Submit generation

→ Generation submitted

  Estimated time:
  • Podcast: 2-3 minutes
  • Explain: 3-5 minutes
  • TTS: 1-2 minutes

  You can:
  • Wait and ask "done yet?"
  • Use check-status via scripts
  • View outputs in product pages:
    - Podcast: https://listenhub.ai/app/podcast
    - Explain: https://listenhub.ai/app/explainer
    - Text-to-Speech: https://listenhub.ai/app/text-to-speech
  • Do other things, ask later

Internally remember Episode ID for status queries.

Step 3: Query status

When user says "done yet?" / "ready?" / "check status":

Success: Show result + next options
Processing: "Still generating, wait another minute?"
Failed: "Generation failed, content might be unparseable. Try another?"

Step 4: Show results

Podcast result:

✓ Podcast generated!

  "{title}"

  Episode: https://listenhub.ai/app/episode/{episodeId}

  Duration: ~{duration} minutes

  Download audio: provide audioUrl or audioStreamUrl on request

One-stage podcast creation generates an online task. When status is success, the episode detail already includes scripts and audio URLs. Download uses the returned audioUrl or audioStreamUrl without a second create call. Two-stage creation is only for script review or manual edits before audio generation.

Explain result:

✓ Explainer video generated!

  "{title}"

  Watch: https://listenhub.ai/app/explainer

  Duration: ~{duration} minutes

  Need to download audio? Just say so.

Image result:

✓ Image generated!

  ~/Downloads/labnana-{timestamp}.jpg

Image results are file-only and not shown in the web UI.

Important: Prioritize web experience. Only provide download URLs when user explicitly requests.

Script Reference

Scripts are shell-based. Locate via **/skills/listenhub/scripts/. Dependency: jq is required for request construction. The AI must ensure curl and jq are installed before invoking scripts.

⚠️ Long-running Tasks: Generation may take 1-5 minutes. Use your CLI client's native background execution feature:

Claude Code: set run_in_background: true in Bash tool
Other CLIs: use built-in async/background job management if available

Invocation pattern:

$SCRIPTS/script-name.sh [args]

Where $SCRIPTS = resolved path to **/skills/listenhub/scripts/

Podcast (One-Stage)

Default path. Use unless script review or manual editing is required.

$SCRIPTS/create-podcast.sh --query "The future of AI development" --language en --mode deep --speakers cozy-man-english
$SCRIPTS/create-podcast.sh --query "Analyze this article" --language en --mode deep --speakers cozy-man-english --source-url "https://example.com/article"

Multiple --source-url and --source-text arguments are supported to combine several references in one request.

Podcast (Two-Stage: Text → Review → Audio)

Advanced path. Use only when script review or edits are explicitly requested.

The entire value of two-stage generation is human review between stages. Skipping review reduces it to one-stage with extra latency — never do this.

Stage 1: Generate text content.

$SCRIPTS/create-podcast-text.sh --query "AI history" --language en --mode deep --speakers cozy-man-english,travel-girl-english

Review Gate (mandatory): After text generation completes, the agent MUST:

Run check-status.sh --wait to poll until completion. On exit code 2 (timeout or rate-limited), wait briefly and retry.
Save two files from the response:
- ~/Downloads/podcast-draft-<episode-id>.md — human-readable version assembled from the response fields (title, outline, sourceProcessResult.content, and the scripts array formatted as readable dialogue). This is for the user to review.
- ~/Downloads/podcast-scripts-<episode-id>.json — the raw {"scripts": [...]} object extracted from the response, exactly in the format that create-podcast-audio.sh --scripts expects. This is the machine-readable source of truth for Stage 2.
Inform the user that both files have been saved, and offer to open the markdown draft for review (use the open command on macOS).
STOP and wait for explicit user approval before proceeding to Stage 2.
On user approval:
- No changes: run create-podcast-audio.sh --episode <id> without --scripts (server uses original).
- With edits: the user may edit the JSON file directly, or describe changes for the agent to apply. Pass the modified file via --scripts.

The agent MUST NOT proceed to Stage 2 automatically. This is a hard constraint, not a suggestion.

Stage 2: Generate audio from reviewed/approved text.

# User approved without changes:
$SCRIPTS/create-podcast-audio.sh --episode "<episode-id>"

# User provided edits:
$SCRIPTS/create-podcast-audio.sh --episode "<episode-id>" --scripts modified-scripts.json

Speech (Multi-Speaker)

$SCRIPTS/create-speech.sh --scripts scripts.json
echo '{"scripts":[{"content":"Hello","speakerId":"cozy-man-english"}]}' | $SCRIPTS/create-speech.sh --scripts -

# scripts.json format:
# {
#   "scripts": [
#     {"content": "Script content here", "speakerId": "speaker-id"},
#     ...
#   ]
# }

Get Available Speakers

$SCRIPTS/get-speakers.sh --language zh
$SCRIPTS/get-speakers.sh --language en

Guidance:

若用户未指定音色，必须先调用 get-speakers.sh 获取可用列表。
默认值兜底：取与 language 匹配的列表首个 speakerId 作为默认音色。

Response structure (for AI parsing):

{
  "code": 0,
  "data": {
    "items": [
      {
        "name": "Yuanye",
        "speakerId": "cozy-man-english",
        "gender": "male",
        "language": "zh"
      }
    ]
  }
}

Usage: When user requests specific voice characteristics (gender, style), call this script first to discover available speakerId values. NEVER hardcode or assume speakerIds.

Explain

$SCRIPTS/create-explainer.sh --content "Introduce ListenHub" --language en --mode info --speakers cozy-man-english
$SCRIPTS/generate-video.sh --episode "<episode-id>"

TTS

$SCRIPTS/create-tts.sh --type text --content "Welcome to ListenHub" --language en --mode smart --speakers cozy-man-english

Image Generation

$SCRIPTS/generate-image.sh --prompt "sunset over mountains" --size 2K --ratio 16:9
$SCRIPTS/generate-image.sh --prompt "style reference" --reference-images "https://example.com/ref1.jpg,https://example.com/ref2.png"

Supported sizes: 1K | 2K | 4K (default: 2K). Supported aspect ratios: 16:9 | 1:1 | 9:16 | 2:3 | 3:2 | 3:4 | 4:3 | 21:9 (default: 16:9). Reference images: comma-separated URLs, maximum 14.

Check Status

# Single-shot query
$SCRIPTS/check-status.sh --episode "<episode-id>" --type podcast

# Wait mode (recommended for automated polling)
$SCRIPTS/check-status.sh --episode "<episode-id>" --type podcast --wait
$SCRIPTS/check-status.sh --episode "<episode-id>" --type flow-speech --wait --timeout 60
$SCRIPTS/check-status.sh --episode "<episode-id>" --type explainer --wait --timeout 600

tts is accepted as an alias for flow-speech.

--wait mode handles polling internally with configurable limits. Agents SHOULD use --wait instead of manual polling loops. On exit code 2, wait briefly and retry the command.

Option	Default	Description
`--wait`	off	Enable polling mode
`--max-polls`	30	Maximum poll attempts
`--timeout`	300	Maximum total wait (seconds)
`--interval`	10	Base poll interval (seconds)

Exit codes: 0 = completed, 1 = failed, 2 = timeout or rate-limited (still pending, safe to retry after a short wait).

Language Adaptation

Automatic Language Detection: Adapt output language based on user input and context.

Detection Rules:

User Input Language: If user writes in Chinese, respond in Chinese. If user writes in English, respond in English.
Context Consistency: Maintain the same language throughout the interaction unless user explicitly switches.
CLAUDE.md Override: If project-level CLAUDE.md specifies a default language, respect it unless user input indicates otherwise.
Mixed Input: If user mixes languages, prioritize the dominant language (>50% of content).

Application:

Status messages: "→ Got it! Preparing..." (English) vs "→ 收到！准备中..." (Chinese)
Error messages: Match user's language
Result summaries: Match user's language
Script outputs: Pass through as-is (scripts handle their own language)

Example:

User (Chinese): "生成一个关于 AI 的播客"
AI (Chinese): "→ 收到！准备双人播客..."

User (English): "Make a podcast about AI"
AI (English): "→ Got it! Preparing two-person podcast..."

Principle: Language is interface, not barrier. Adapt seamlessly to user's natural expression.

AI Responsibilities

Black Box Principle

You are a dispatcher, not an implementer.

Your job is to:

Understand user intent (what do they want to create?)
Select the correct script (which tool fits?)
Format arguments correctly (what parameters?)
Execute and relay results (what happened?)

Your job is NOT to:

Understand or modify script internals
Construct API calls directly
Guess parameters not documented here
Invent features that scripts don't expose

Mode-Specific Behavior

ListenHub modes (passthrough):

Podcast/Explain/TTS/Speech → pass user input directly
Server has full AI capability to process content
If user needs specific speakers → call get-speakers.sh first to list options

Labnana mode (passthrough by default):

Image Generation → pass the user's prompt through as-is by default
The generation model handles prompt interpretation; client-side rewriting is not required

Prompt Handling (Image Generation)

Default behavior: transparent forwarding. Pass the user's prompt directly to the script without modification.

When to offer optimization:

The user provides only a short topic or phrase (e.g., "a cat"), AND
The user has not explicitly stated they want verbatim generation

In this case, ask whether the user would like help enriching the prompt. Do not optimize without confirmation.

When to never modify:

The user pastes a long, structured, or detailed prompt — treat them as experienced
The user explicitly says "use this prompt exactly" or similar

If the user agrees to optimization, the following techniques are available as reference:

Style: "cyberpunk" → add "neon lights, futuristic, dystopian"; "ink painting" → add "Chinese ink painting, traditional art style"

Scene: time of day, lighting conditions, weather

Quality: "highly detailed", "8K quality", "cinematic composition"

Rules when optimizing:

Use English keywords (models trained on English)
Show the optimized prompt transparently before submitting
Keep the user's core intent unchanged
Do not over-stack terminology or add unwanted elements

→ Generation submitted, about 2-3 minutes

You can: • Wait and ask "done yet?" • Check listenhub.ai/app/library

→ Generation submitted, explainer videos take 3-5 minutes

Includes: Script + narration + AI visuals

→ TTS submitted, about 1-2 minutes

Wait a moment, or ask "done yet?" to check

Prompt: Cyberpunk city at night, neon lights reflecting on wet streets, towering skyscrapers with holographic ads, flying vehicles, cinematic composition, highly detailed, 8K quality

Resolution: 2K (16:9)

✓ Image generated! ~/Downloads/labnana-20260121-143145.jpg

Prompt: a futuristic car Reference images: 1 Reference image URL: https://example.com/style-ref.jpg Resolution: 2K (16:9)

✓ Image generated! ~/Downloads/labnana-20260122-154230.jpg

"AI Revolution: From GPT to AGI"

Listen: https://listenhub.ai/app/podcast

Duration: ~8 minutes

Need to download? Just say so.

When it activates

Automatically, when a task matches. Use when the user wants to "make a podcast", "create an explainer video", "read this aloud", "generate an image", or share knowledge in audio/visual form. Supports: topic descriptions, YouTube links, article URLs, plain text, and image prompts.

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 v4 — 8/2/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/listenhub-2/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v2Bundled 11 files · scripts

Illustrative Want to see what leoyeai/listenhub 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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Safety

What it changes

Track record

Use this skill

⛔ Hard Constraints (Inviolable)

Script Location

Private Data (Cannot Be Searched)

Design Philosophy

Environment

ListenHub API Key

Image Generation API Key

Mode Detection

Ambiguous "Convert to speech" Guidance

Interaction Flow

Step 1: Receive input + detect mode

Step 2: Submit generation

Step 3: Query status

Step 4: Show results

Script Reference

Podcast (One-Stage)

Podcast (Two-Stage: Text → Review → Audio)

Speech (Multi-Speaker)

Get Available Speakers

Explain

TTS

Image Generation

Check Status

Language Adaptation

AI Responsibilities

Black Box Principle

Mode-Specific Behavior

Prompt Handling (Image Generation)

When it activates

SkillSafety

Source

Related skills

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