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sn-image-base · opensensenova | DecimalAI

AI/MLby opensensenova

sn-image-base

Base-layer skill for the SenseNova-Skills project, providing low-level APIs for image generation, recognition (VLM), and text optimization (LLM). This skill does not preprocess inputs; it only calls backend services and returns results. This skill is not user-facing and is intended for upper-layer skills only.

Maintained

SkillScore

SkillScore82/100

Test results 77 · Live runs 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

27%77%of test cases passed +50 pts

Without skill6 of 22

With skill17 of 22

12 fixed5 passed either way4 still failing1 broke

Measured on gemini-3.6-flash9/9/2026v319 comparable

Track record

Agent runs: 48; All time
Forks: 1

See one task, with and without the skill

One test taskModify `input_person.jpg` using reference photo `style_target.jpg` with prompt 'turn background into watercolor studio'. Save the resulting image to `output_edited.jpg` using the SenseNova image editing tool.

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add opensensenova-sn-image-base
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("opensensenova-sn-image-base")
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 opensensenova-sn-image-base

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/opensensenova-sn-image-base/SKILL.md https://app.decimal.ai/s/opensensenova-sn-image-base/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/opensensenova-sn-image-base/SKILL.md

This gets SKILL.md only. The skill's 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/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/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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("opensensenova-sn-image-base")
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/opensensenova-sn-image-base/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/opensensenova-sn-image-base/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 6 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 opensensenova-sn-image-base

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/opensensenova-sn-image-base/SKILL.md https://app.decimal.ai/s/opensensenova-sn-image-base/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/opensensenova-sn-image-base/SKILL.md

This gets SKILL.md only. The skill's 6 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/opensensenova-sn-image-base/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/opensensenova-sn-image-base.json

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3, tested 9/9/2026
Source: opensensenova/sensenova-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

sn-image-base

Dependency Installation

pip install -r requirements.txt

Overview

sn-image-base is the base-layer skill (tier 0) of the SenseNova-Skills project and provides four low-level tools:

sn-image-generate: image generation (calls text-to-image-no-enhance API)
sn-image-edit: image editing with SenseNova U1.5 Lite (calls /images/edits)
sn-image-recognize: image recognition (uses VLM to analyze image content)
sn-text-optimize: text optimization (uses LLM to process text)

This skill does not perform any input preprocessing and only calls backend services to return results.

Tools List

sn-image-generate

Image generation tool that calls the text-to-image-no-enhance API.

--prompt is required; all other parameters are optional:

Parameter	Type	Default	Description
`--prompt`	string	Required	Prompt text for image generation
`--negative-prompt`	string	`""`	Negative prompt
`--image-size`	string	`2k`	Image size preset (case-insensitive). Recommended: `2k`. `4k` is supported by `sensenova-u1.5-lite`; other SenseNova image models may reject it. Other values → `status=failed`.
`--aspect-ratio`	string	`16:9`	Aspect ratio, e.g. `1:1`, `16:9`, `9:16`
`--seed`	int	`None`	Random seed for reproducible generation
`--unet-name`	string	`None`	Specify a UNet model name
`--api-key`	string	`SN_IMAGE_GEN_API_KEY` -> `SN_API_KEY`	API key (CLI argument has priority; `MissingApiKeyError` is raised when all are empty)
`--base-url`	string	`SN_IMAGE_GEN_BASE_URL` -> `SN_BASE_URL`	API base URL (CLI argument has priority)
`--poll-interval`	float	`5.0`	Polling interval (seconds)
`--timeout`	float	`300.0`	Timeout (seconds)
`--insecure`	flag	`False`	Disable TLS verification
`--save-path`	Path	Auto-generated	Save path

SenseNova image requests explicitly send watermark=false by default. Both sensenova-u1-fast and sensenova-u1.5-lite are supported; U1.5 Lite additionally supports native 4K output. This no-watermark feature is currently in free public beta and may become paid.

sn-image-edit

Edits one or more reference images with SenseNova U1.5 Lite through the /images/edits endpoint. Local paths are converted to Data URLs; HTTP(S) URLs and Data URLs are passed through.

python scripts/sn_agent_runner.py sn-image-edit \
    --prompt "Change the background to a snowy mountain" \
    --images source.png reference.png \
    --save-path edited.png

The edit request uses the official defaults n=1, size=auto, watermark=false, prompt_extend=true, and response_format=url.

sn-image-recognize

Image recognition tool that uses VLM (Vision Language Model) to analyze image content. Supports multiple image inputs.

--images and --user-prompt (or --user-prompt-path) are required. All other parameters use three-level defaults (CLI > env var > built-in default):

Parameter	Type	Built-in Default	Env Var	Description
`--api-key`	string	No hardcoded default	`SN_VISION_API_KEY` -> `SN_CHAT_API_KEY` -> `SN_API_KEY`	Chat runtime API key; raises `MissingApiKeyError` when all are unset
`--base-url`	string	`SN_CHAT_BASE_URL` default	`SN_VISION_BASE_URL` -> `SN_CHAT_BASE_URL` -> `SN_BASE_URL`	Vision provider base URL; falls back to shared chat/global provider
`--model`	string	`sensenova-6.8-flash-lite`	`SN_VISION_MODEL` -> `SN_CHAT_MODEL`	Vision-capable model name
`--vlm-type`	string	`openai-completions`	`SN_VISION_TYPE` -> `SN_CHAT_TYPE`	Chat protocol type override
`--user-prompt-path`	string	`None`	-	Local file path, mutually exclusive with `--user-prompt`
`--system-prompt-path`	string	`None`	-	Local file path, mutually exclusive with `--system-prompt`

Available values for --vlm-type:

openai-completions: OpenAI-compatible /v1/chat/completions interface
anthropic-messages: Anthropic Messages /v1/messages interface

sn-text-optimize

Text optimization tool that uses LLM (Language Model) to optimize text content. Does not accept image inputs.

--user-prompt (or --user-prompt-path) is required. All other parameters use three-level defaults (CLI > env var > built-in default):

Parameter	Type	Built-in Default	Env Var	Description
`--api-key`	string	No hardcoded default	`SN_TEXT_API_KEY` -> `SN_CHAT_API_KEY` -> `SN_API_KEY`	Chat runtime API key; raises `MissingApiKeyError` when all are unset
`--base-url`	string	`SN_CHAT_BASE_URL` default	`SN_TEXT_BASE_URL` -> `SN_CHAT_BASE_URL` -> `SN_BASE_URL`	Text provider base URL; falls back to shared chat/global provider
`--model`	string	`sensenova-6.8-flash-lite`	`SN_TEXT_MODEL` -> `SN_CHAT_MODEL`	Text model name
`--llm-type`	string	`openai-completions`	`SN_TEXT_TYPE` -> `SN_CHAT_TYPE`	Chat protocol type override
`--user-prompt-path`	string	`None`	-	Local file path, mutually exclusive with `--user-prompt`
`--system-prompt-path`	string	`None`	-	Local file path, mutually exclusive with `--system-prompt`

Available values for --llm-type:

openai-completions: OpenAI-compatible /v1/chat/completions interface
anthropic-messages: Anthropic Messages /v1/messages interface

VLM vs LLM

Tool	Model Type	Image Input	Interface Type Parameter
`sn-image-recognize`	VLM (Vision Language Model)	Yes, supports multiple images	`--vlm-type`
`sn-text-optimize`	LLM (Language Model)	No, text only	`--llm-type`

Usage

All tools are called through the unified sn_agent_runner.py entrypoint:

# Image generation (only prompt required; api-key/base-url have defaults)
python scripts/sn_agent_runner.py sn-image-generate \
    --prompt "..."

# Image generation (override base-url)
python scripts/sn_agent_runner.py sn-image-generate \
    --prompt "..." \
    --base-url "https://custom-endpoint.com/v1"

# Image generation (explicitly override api-key)
python scripts/sn_agent_runner.py sn-image-generate \
    --prompt "..." \
    --api-key "sk-xxx"

# Image recognition (VLM) - minimal call (uses built-in Sensenova defaults)
python scripts/sn_agent_runner.py sn-image-recognize \
    --user-prompt "Describe the image" \
    --images "path/to/image.png"

# Image recognition (VLM) - override to Anthropic Claude API compatible (messages interface)
python scripts/sn_agent_runner.py sn-image-recognize \
    --user-prompt "Describe the image" \
    --images "path/to/image.png" \
    --api-key "sk-ant-xxx" \
    --base-url "https://api.anthropic.com" \
    --model "claude-sonnet-4-6" \
    --vlm-type "anthropic-messages"

# Text optimization (LLM) - minimal call (uses built-in Sensenova defaults)
python scripts/sn_agent_runner.py sn-text-optimize \
    --user-prompt "Optimize the text: ..."

# Text optimization (LLM) - override to Anthropic Claude API compatible (messages interface)
python scripts/sn_agent_runner.py sn-text-optimize \
    --user-prompt "Optimize the text: ..." \
    --api-key "sk-ant-xxx" \
    --base-url "https://api.anthropic.com" \
    --model "claude-sonnet-4-6" \
    --llm-type "anthropic-messages"

Default Parameter Behavior

Authentication parameters for sn-image-generate have the following default behavior:

Parameter	Default	Override	Description
`--base-url`	`SN_IMAGE_GEN_BASE_URL` -> `SN_BASE_URL`	`--base-url "..."`	CLI argument has priority
`--api-key`	`SN_IMAGE_GEN_API_KEY` -> `SN_API_KEY`	`--api-key "..."`	CLI argument has priority; throws `MissingApiKeyError` if all values are empty

sn-image-recognize and sn-text-optimize use priority: CLI argument > command-specific env var > shared SN_CHAT_* env var > global SN_* env var > built-in default.

Parameter	Built-in Default	Vision Env Var	Text Env Var
`--api-key`	None (must be provided)	`SN_VISION_API_KEY` -> `SN_CHAT_API_KEY` -> `SN_API_KEY`	`SN_TEXT_API_KEY` -> `SN_CHAT_API_KEY` -> `SN_API_KEY`
`--base-url`	`https://token.sensenova.cn/v1`	`SN_VISION_BASE_URL` -> `SN_CHAT_BASE_URL` -> `SN_BASE_URL`	`SN_TEXT_BASE_URL` -> `SN_CHAT_BASE_URL` -> `SN_BASE_URL`
`--model`	`sensenova-6.8-flash-lite`	`SN_VISION_MODEL` -> `SN_CHAT_MODEL`	`SN_TEXT_MODEL` -> `SN_CHAT_MODEL`
`--vlm-type` / `--llm-type`	`openai-completions`	`SN_VISION_TYPE` -> `SN_CHAT_TYPE`	`SN_TEXT_TYPE` -> `SN_CHAT_TYPE`

api_key resolution order (high to low): CLI --api-key > command-specific key (SN_VISION_API_KEY/SN_TEXT_API_KEY) > SN_CHAT_API_KEY > SN_API_KEY. If all are unset, MissingApiKeyError is raised.

Only --api-key must be provided via CLI or environment; base URL, model, and interface type have shared chat defaults.

Agent Configuration Integration

The agent can automatically read parameters from openclaw.json without manual input:

CLI Parameter	openclaw.json Field	Example
`--base-url`	`providers.<name>.baseUrl`	`https://api.anthropic.com`
`--llm-type`	`providers.<name>.api`	`anthropic-messages` / `openai-completions`
`--vlm-type`	`providers.<name>.api`	`anthropic-messages` / `openai-completions`
`--model`	`providers.<name>.models[].id`	`claude-sonnet-4-6`
`--api-key`	`providers.<name>.apiKey` or env var	`sk-cp-...`

Note: --llm-type and --vlm-type share the same providers.<name>.api field and are used by LLM and VLM tools respectively.

Mapping between provider.api and interface type:

api Value	Corresponding `--llm-type` / `--vlm-type`	Endpoint Path
`anthropic-messages`	`anthropic-messages`	`/v1/messages`
`openai-completions`	`openai-completions`	`/v1/chat/completions`
`openai-responses`	(future extension)	`/responses`

Mapping Between base-url and Interface Type

Different API types have different requirements for base-url format:

Type	`--llm-type` / `--vlm-type`	Recommended base-url	Code Appended Path	Final URL Example
LLM	`openai-completions`	`https://token.sensenova.cn/v1`	`/chat/completions`	`https://token.sensenova.cn/v1/chat/completions`
LLM	`anthropic-messages`	`https://api.anthropic.com/v1`	`/messages`	`https://api.anthropic.com/v1/messages`
VLM	`openai-completions`	`https://token.sensenova.cn/v1`	`/chat/completions`	`https://token.sensenova.cn/v1/chat/completions`
VLM	`anthropic-messages`	`https://api.anthropic.com/v1`	`/messages`	`https://api.anthropic.com/v1/messages`

Note:

Recommended chat base URLs include the provider API version path, for example /v1.
For compatibility, if the configured chat base URL has no path, the runner appends /v1/chat/completions or /v1/messages.
If the configured chat base URL already has a path such as /v1, the runner appends only /chat/completions or /messages.
Some providers use versioned paths other than /v1, such as Gemini's /v1beta/openai.

Output Format

All tools support two output formats:

--output-format text (default): outputs plain text result
--output-format json: outputs JSON, including status and elapsed_seconds (runtime in seconds, rounded to 2 decimals)

JSON output for sn-image-recognize and sn-text-optimize also includes model, base_url, and interface_type to verify the effective runtime configuration:

{
  "status": "ok",
  "result": "...",
  "model": "sensenova-6.8-flash-lite",
  "base_url": "https://token.sensenova.cn/v1",
  "interface_type": "openai-completions",
  "elapsed_seconds": 1.23
}

On failure:

{
  "status": "failed",
  "error": "error message",
  "elapsed_seconds": 0.05
}

Input/Output Specification

See references/api_spec.md for details.

About this skill

Automatic

AI Engineer pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 9/8/2026

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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

opensensenova / sensenova-skills

skills/sn-image-base/SKILL.md

View on GitHub ↗

License MITImported 9/8/2026Version v3Bundled 6 files · scripts

Illustrative Want to see what opensensenova/sn-image-base would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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SkillScore

Safety

What it changes

Track record

Use this skill

sn-image-base

Dependency Installation

Overview

Tools List

sn-image-generate

sn-image-edit

sn-image-recognize

sn-text-optimize

VLM vs LLM

Usage

Default Parameter Behavior

Agent Configuration Integration

Mapping Between base-url and Interface Type

Output Format

Input/Output Specification

SkillSafety

Source

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