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Image Card Series Generator · guanyang | DecimalAI

Design & Mediaby guanyang

Image Card Series Generator

Generates infographic image card series with 12 visual styles, 8 layouts, and 3 color palettes. Breaks content into 1-10 cartoon-style image cards optimized for social media engagement.

Never made a case worse

SkillScore

SkillScore48/100

Test results 48

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

9%48%of test cases passed +39 pts

Without skill2 of 23

With skill11 of 23

9 fixed2 passed either way12 still failing0 broke

Measured on gemini-3.6-flash7/27/2026v1

Track record

Agent runs: 19; 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 baoyu-image-cards
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("baoyu-image-cards")
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 baoyu-image-cards

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/baoyu-image-cards/SKILL.md https://app.decimal.ai/s/baoyu-image-cards/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/baoyu-image-cards/SKILL.md

This gets SKILL.md only. The skill's 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/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/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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("baoyu-image-cards")
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/baoyu-image-cards/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/baoyu-image-cards/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 2 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 baoyu-image-cards

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/baoyu-image-cards/SKILL.md https://app.decimal.ai/s/baoyu-image-cards/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/baoyu-image-cards/SKILL.md

This gets SKILL.md only. The skill's 2 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/baoyu-image-cards/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/baoyu-image-cards.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/27/2026
Source: guanyang/open-agent-hub ↗
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

Image Card Series Generator

Break down complex content into eye-catching image card series with multiple style options.

User Input Tools

When this skill prompts the user, follow this tool-selection rule (priority order):

Prefer built-in user-input tools exposed by the current agent runtime — e.g., AskUserQuestion, request_user_input, clarify, ask_user, or any equivalent.
Fallback: if no such tool exists, emit a numbered plain-text message and ask the user to reply with the chosen number/answer for each question.
Batching: if the tool supports multiple questions per call, combine all applicable questions into a single call; if only single-question, ask them one at a time in priority order.

Concrete AskUserQuestion references below are examples — substitute the local equivalent in other runtimes.

Image Generation Tools

When this skill needs to render an image, resolve the backend in this order:

Current-request override — if the user names a specific backend in the current message, use it.
Saved preference — if EXTEND.md sets preferred_image_backend to a backend available right now, use it.
Auto-select (when the preference is auto, unset, or the pinned backend isn't available):
- Codex (imagegen) — first, inspect your available-skills / tool inventory. If a skill named imagegen is listed, you are running inside Codex and MUST use it: invoke via the Skill tool with skill: "imagegen", passing the saved prompt file's content (plus output path and aspect ratio per Codex imagegen's own args). Codex imagegen is the official raster backend in that runtime and outranks any non-native skill (e.g., baoyu-imagine) unless the user has explicitly pinned a different preferred_image_backend.
- Other runtime-native tools — if the runtime exposes a different native image tool (e.g., Hermes image_generate), use it the same way.
- Otherwise, if exactly one non-native backend is installed (e.g., baoyu-imagine), use it.
- Otherwise (multiple non-native backends with no runtime-native tool), ask the user once — batch with any other initial questions.
If none are available, tell the user and ask how to proceed.

⛔ Never substitute SVG, HTML, canvas, or other code-based rendering for raster image generation. Codex imagegen's own description says it should be used "when the output should be a bitmap asset rather than repo-native code or vector." If you cannot resolve a raster backend via step 3, fall through to step 4 and ask the user — do not silently emit SVG, write inline <svg> markup, or produce HTML/CSS art as a substitute. This applies even if the article/section seems "diagram-like": the consumer skill calling this rule has already decided that a raster image is what it needs.

⛔ Never repair rendered text by painting over a generated bitmap. Do not use ImageMagick, Pillow, Canvas, SVG, HTML/CSS, OCR scripts, or any other programmatic overlay to cover, rewrite, erase, stroke, or replace titles, body copy, tags, or any other text inside an already generated image card. If text is wrong or unclear, regenerate from a corrected prompt, switch to a layout with less on-card text, or ask the user which imperfect candidate to keep.

Setting preferred_image_backend: ask forces the step-3 prompt every run regardless of available backends. Users change the pinned backend via the ## Changing Preferences section below.

Prompt file requirement (hard): write each image's full, final prompt to a standalone file under prompts/ (naming: NN-{type}-[slug].md) BEFORE invoking any backend. The file is the reproducibility record and lets you switch backends without regenerating prompts.

Concrete tool names (imagegen, image_generate, baoyu-imagine) above are examples — substitute the local equivalents under the same rule.

Batch Generation Policy

After every prompt file for the current generation group has been saved and verified, generate images in batches by default.

Priority order:

Use the chosen backend's native batch / multi-task interface if it exists. Each task must keep its own prompt file, output path, aspect ratio, session ID, and direct reference images.
If no native batch interface exists but the runtime can issue parallel tool calls, dispatch up to generation_batch_size images at a time. Default: 4. An explicit user request in the current message, such as --batch-size 4 or "并行4张一起生成", overrides EXTEND.md.
If neither native batch nor parallel tool calls are available, generate sequentially.

Rules:

Honor the image-1 anchor chain: generate image 1 first, then batch images 2+ using image 1 as the reference.
Never start a batch until every selected prompt file for that batch exists on disk.
Retry failed items once without regenerating successful items.
Do not use subagents merely to parallelize image rendering. Use subagents only for separate prompt iteration or creative exploration.

Confirmation Policy

Default behavior: confirm before generation.

Treat explicit skill invocation, a file path, matched signals/presets, and EXTEND.md defaults as recommendation inputs only. None of them authorizes skipping confirmation.
Do not start Step 3 until the user completes Step 2.
Skip confirmation only when the current request explicitly says to do so, for example: --yes, "直接生成", "不用确认", "跳过确认", "按默认出图", or equivalent wording.
If confirmation is skipped explicitly, state the assumed strategy / style / layout / palette / count / backend in the next user-facing update before generating.

Language

Respond in the user's language across questions, progress, errors, and completion summary. Keep technical tokens (style names, file paths, code) in English.

Options

Option	Description
`--style <name>`	Visual style (see Styles below)
`--layout <name>`	Information layout (see Layouts below)
`--palette <name>`	Color override: macaron / warm / neon
`--preset <name>`	Style + layout + optional palette shorthand (see Presets below; per-preset prompt fragments in `references/style-presets.md`)
`--ref <files...>`	Reference images applied to image 1 as the series anchor
`--batch-size <n>`	Temporary generation batch size for this run. Default: `generation_batch_size` from EXTEND.md, otherwise 4. Clamp to 1-8.
`--yes`	Non-interactive: skip all confirmations, use EXTEND.md or built-in defaults, auto-confirm recommended plan (Path A)

Dimensions

Three independent knobs combine freely:

Dimension	Controls	Options
Style	Visual aesthetics (lines, decorations, rendering)	12 styles (see Styles below)
Layout	Information structure (density, arrangement)	8 layouts (see Layouts below)
Palette (optional)	Color override, replaces the style's default colors	macaron / warm / neon (see Palettes below)

Example: --style notion --layout dense makes an intellectual knowledge card; add --palette macaron to soften the colors without changing notion's rendering rules. A --preset is a shorthand for style + layout (+ optional palette).

Palette behavior: no --palette → style's built-in colors; --palette <name> → overrides colors only, rendering rules unchanged. Some styles declare a default_palette (e.g., sketch-notes defaults to macaron).

Styles (12)

Style	Description
`cute` (Default)	Sweet, adorable, girly aesthetic
`fresh`	Clean, refreshing, natural
`warm`	Cozy, friendly, approachable
`bold`	High impact, attention-grabbing
`minimal`	Ultra-clean, sophisticated
`retro`	Vintage, nostalgic, trendy
`pop`	Vibrant, energetic, eye-catching
`notion`	Minimalist hand-drawn line art, intellectual
`chalkboard`	Colorful chalk on black board, educational
`study-notes`	Realistic handwritten photo style, blue pen + red annotations + yellow highlighter
`screen-print`	Bold poster art, halftone textures, limited colors, symbolic storytelling
`sketch-notes`	Hand-drawn educational infographic, macaron pastels on warm cream, wobble lines

Per-style specifications: references/presets/<style>.md.

Layouts (8)

Layout	Description
`sparse` (Default)	1-2 points, maximum impact
`balanced`	3-4 points, standard
`dense`	5-8 points, knowledge-card style
`list`	Enumeration / ranking (4-7 items)
`comparison`	Side-by-side contrast
`flow`	Process / timeline (3-6 steps)
`mindmap`	Center-radial (4-8 branches)
`quadrant`	Four-quadrant / circular sections

Layout specs: references/elements/canvas.md.

Palettes (optional override)

Replaces the style's colors while keeping rendering rules (line treatment, textures) intact.

Palette	Background	Zone Colors	Accent	Feel
`macaron`	Warm cream #F5F0E8	Blue #A8D8EA, Lavender #D5C6E0, Mint #B5E5CF, Peach #F8D5C4	Coral #E8655A	Soft, educational
`warm`	Soft peach #FFECD2	Orange #ED8936, Terracotta #C05621, Golden #F6AD55, Rose #D4A09A	Sienna #A0522D	Earth tones, cozy
`neon`	Dark purple #1A1025	Cyan #00F5FF, Magenta #FF00FF, Green #39FF14, Pink #FF6EC7	Yellow #FFFF00	High-energy, futuristic

Palette specs: references/palettes/<palette>.md.

Presets (style + layout shortcuts)

Quick-start combos, grouped by scenario. Use --preset <name> or recommend during Step 2.

Knowledge & Learning:

Preset	Style	Layout	Best For
`knowledge-card`	notion	dense	干货知识卡、概念科普
`checklist`	notion	list	清单、排行榜
`concept-map`	notion	mindmap	概念图、知识脉络
`swot`	notion	quadrant	SWOT 分析、四象限
`tutorial`	chalkboard	flow	教程步骤、操作流程
`classroom`	chalkboard	balanced	课堂笔记、知识讲解
`study-guide`	study-notes	dense	学习笔记、考试重点
`hand-drawn-edu`	sketch-notes	flow	手绘教程、流程图解
`sketch-card`	sketch-notes	dense	手绘知识卡
`sketch-summary`	sketch-notes	balanced	手绘总结、图文笔记

Lifestyle & Sharing:

Preset	Style	Layout	Best For
`cute-share`	cute	balanced	少女风分享、日常种草
`girly`	cute	sparse	甜美封面、氛围感
`cozy-story`	warm	balanced	生活故事、情感分享
`product-review`	fresh	comparison	产品对比、测评
`nature-flow`	fresh	flow	健康流程、自然主题

Impact & Opinion:

Preset	Style	Layout	Best For
`warning`	bold	list	避坑指南、重要提醒
`versus`	bold	comparison	正反对比
`clean-quote`	minimal	sparse	金句、极简封面
`pro-summary`	minimal	balanced	专业总结、商务内容

Trend & Entertainment:

Preset	Style	Layout	Best For
`retro-ranking`	retro	list	复古排行、经典盘点
`throwback`	retro	balanced	怀旧分享
`pop-facts`	pop	list	趣味冷知识
`hype`	pop	sparse	炸裂封面、惊叹分享

Poster & Editorial:

Preset	Style	Layout	Best For
`poster`	screen-print	sparse	海报风封面、影评书评
`editorial`	screen-print	balanced	观点文章、文化评论
`cinematic`	screen-print	comparison	电影对比、戏剧张力

Full prompt-fragment definitions: references/style-presets.md.

Auto-Selection

Match content signals to the best combo. First row whose keywords appear wins; fall back to cute-share if nothing matches.

Signals in source	Style	Layout	Recommended preset
beauty, fashion, cute, girl, pink	`cute`	sparse/balanced	`cute-share`, `girly`
health, nature, fresh, organic	`fresh`	balanced/flow	`product-review`, `nature-flow`
life, story, emotion, warm	`warm`	balanced	`cozy-story`
warning, important, must, critical	`bold`	list/comparison	`warning`, `versus`
professional, business, elegant	`minimal`	sparse/balanced	`clean-quote`, `pro-summary`
classic, vintage, traditional	`retro`	balanced	`throwback`, `retro-ranking`
fun, exciting, wow, amazing	`pop`	sparse/list	`hype`, `pop-facts`
knowledge, concept, productivity, SaaS	`notion`	dense/list	`knowledge-card`, `checklist`
education, tutorial, learning, classroom	`chalkboard`	balanced/dense	`tutorial`, `classroom`
notes, handwritten, study guide, realistic	`study-notes`	dense/list/mindmap	`study-guide`
movie, poster, opinion, editorial, cinematic	`screen-print`	sparse/comparison	`poster`, `editorial`, `cinematic`
hand-drawn, infographic, workflow, 手绘, 图解	`sketch-notes`	flow/balanced/dense	`hand-drawn-edu`, `sketch-card`, `sketch-summary`

Style × Layout Matrix

Compatibility scores (✓✓ highly recommended, ✓ works well, ✗ avoid). Use when the user picks a non-default combo and you want to flag a poor match.

	sparse	balanced	dense	list	comparison	flow	mindmap	quadrant
cute	✓✓	✓✓	✓	✓✓	✓	✓	✓	✓
fresh	✓✓	✓✓	✓	✓	✓	✓✓	✓	✓
warm	✓✓	✓✓	✓	✓	✓✓	✓	✓	✓
bold	✓✓	✓	✓	✓✓	✓✓	✓	✓	✓✓
minimal	✓✓	✓✓	✓✓	✓	✓	✓	✓	✓
retro	✓✓	✓✓	✓	✓✓	✓	✓	✓	✓
pop	✓✓	✓✓	✓	✓✓	✓✓	✓	✓	✓
notion	✓✓	✓✓	✓✓	✓✓	✓✓	✓✓	✓✓	✓✓
chalkboard	✓✓	✓✓	✓✓	✓✓	✓	✓✓	✓✓	✓
study-notes	✗	✓	✓✓	✓✓	✓	✓	✓✓	✓
screen-print	✓✓	✓✓	✗	✓	✓✓	✓	✗	✓✓
sketch-notes	✓	✓✓	✓✓	✓✓	✓	✓✓	✓✓	✓

Outline Strategies

Three differentiated approaches — each produces a structurally different outline. The workflow recommends one; Path C generates all three and lets the user choose.

Strategy	Concept	Best for	Structure
A — Story-Driven	Personal experience as the thread, emotional resonance first	Reviews, personal shares, transformation	Hook → Problem → Discovery → Experience → Conclusion
B — Information-Dense	Value-first, efficient information delivery	Tutorials, comparisons, checklists	Core conclusion → Info card → Pros/Cons → Recommendation
C — Visual-First	Visual impact as core, minimal text	High-aesthetic products, lifestyle, mood content	Hero image → Detail shots → Lifestyle scene → CTA

Reference Images

User-supplied refs are separate from the internal "image-1 as anchor" chain (Step 3) — they layer on top of it.

Intake: via --ref <files...> or paths pasted in conversation.

File path → copy to refs/NN-ref-{slug}.{ext}
Pasted with no path → ask for the path, or extract style traits as a text fallback

Usage modes (per reference):

Usage	Effect
`direct`	Pass the file to the backend (typically on image 1 only, so the anchor propagates through the chain)
`style`	Extract style traits and append to every card's prompt body
`palette`	Extract hex colors and append to every card's prompt body

Record refs in each affected card's prompt frontmatter:

references:
  - ref_id: 01
    filename: 01-ref-brand.png
    usage: direct

At generation time: verify files exist. Image 1 with usage: direct + backend that accepts refs → pass via the backend's ref parameter (becomes the chain anchor). Images 2+ keep using image-1 as --ref per Step 3 — do NOT re-stack user refs on top (avoids conflicting signals). For style/palette, embed extracted traits in every prompt.

File Layout

image-cards/{topic-slug}/
├── source-{slug}.{ext}
├── analysis.md
├── outline-strategy-{a,b,c}.md    # Path C only
├── outline.md
├── prompts/NN-{type}-{slug}.md
├── NN-{type}-{slug}.png
└── refs/                          # only if --ref used

Slug: 2-4 words, kebab-case. "AI 工具推荐" → ai-tools-recommend. On collision, append -YYYYMMDD-HHMMSS.

Backup rule (applies throughout): before overwriting any file — source, outline, prompt, image — rename the existing one to <name>-backup-YYYYMMDD-HHMMSS.<ext>. This protects user edits.

Workflow

- [ ] Step 0: Load EXTEND.md ⛔ BLOCKING (interactive only)
- [ ] Step 1: Analyze content → analysis.md
- [ ] Step 2: Smart Confirm ⚠️ REQUIRED (Path A / B / C)
- [ ] Step 3: Generate images
- [ ] Step 4: Completion report

Step 0: Load EXTEND.md ⛔ BLOCKING

Check these paths in order; first hit wins:

Path	Scope
`.baoyu-skills/baoyu-image-cards/EXTEND.md`	Project
`${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-image-cards/EXTEND.md`	XDG
`$HOME/.baoyu-skills/baoyu-image-cards/EXTEND.md`	User home

Found → read, parse, print a summary (style / layout / watermark / language), continue.
Not found + interactive → run first-time setup (see references/config/first-time-setup.md) and save before anything else. Do NOT analyze content or ask style questions until preferences exist — this keeps first-run behavior predictable.
Not found + --yes → skip setup, use built-in defaults (no watermark, style/layout auto-selected, language from content). Do not prompt, do not create EXTEND.md.

EXTEND.md keys: watermark, preferred style/layout, custom style definitions, language preference, preferred image backend, generation batch size. Schema: references/config/preferences-schema.md.

Step 1: Analyze Content → `analysis.md`

Save the source (backup rule applies if source.md exists).
Run the deep analysis in references/workflows/analysis-framework.md: content type, hook potential, audience, engagement signals, visual opportunity map, swipe flow.
Detect source language, pick recommended image count (2-10).
Auto-recommend strategy + style + layout + palette using the Auto-Selection table above.
Write everything to analysis.md.

Step 2: Smart Confirm ⚠️ REQUIRED

Hard gate: this step is mandatory per the Confirmation Policy — Step 3 cannot start until the user confirms here (or explicitly opts out with --yes / equivalent wording in the current request).

Goal: present the auto-recommended plan and let the user confirm or adjust. Skip this step entirely under --yes — proceed with Path A using the analysis and any CLI overrides.

Display summary before asking:

📋 内容分析
  主题：[topic] | 类型：[content_type]
  要点：[key points]
  受众：[audience]

🎨 推荐方案（自动匹配）
  策略：[A/B/C] [name]（[reason]）
  风格：[style] · 布局：[layout] · 配色：[palette or 默认] · 预设：[preset]
  图片：[N]张（封面+[N-2]内容+结尾）
  元素：[background] / [decorations] / [emphasis]

Then ask one question — three paths. Verbatim option copy: references/confirmation.md.

Path A — Quick confirm (trust auto-recommendation): generate a single outline using the recommended strategy + style → save to outline.md → Step 3.

Path B — Customize: ask five questions (strategy/style, layout, palette, count, optional notes) with the recommendation pre-filled — blanks keep the recommendation. Generate one outline with the user's choices → outline.md → Step 3. See references/confirmation.md.

Path C — Detailed mode: two sub-confirmations.

Step 2a — Content understanding: ask selling points (multi-select), audience, style preference (authentic / professional / aesthetic / auto), optional context. Update analysis.md.
Step 2b — Three outline variants: generate outline-strategy-a.md, outline-strategy-b.md, outline-strategy-c.md. Each MUST have a different structure AND a different recommended style — include style_reason in the frontmatter. Page-count heuristic: A ~4-6, B ~3-5, C ~3-4. Template: references/workflows/outline-template.md; frontmatter example in references/confirmation.md.
Step 2c — Selection: ask three questions (outline A/B/C/Combined, style, visual elements). Save selected/merged outline to outline.md → Step 3.

Step 3: Generate Images

With confirmed outline + style + layout + palette:

Visual consistency — image-1 anchor chain: character / mascot / color rendering drifts between calls unless you anchor them. Generate image 1 (cover) first WITHOUT --ref, then pass image 1 as --ref to every subsequent image. This is the single most important consistency trick for this skill — don't skip it even if the backend also supports a session ID.

Generation flow:

Write the full prompt for every image to prompts/NN-{type}-{slug}.md in the user's preferred language (backup rule applies), then verify all selected prompt files exist.
Generate image 1 first without --ref; backup rule applies to the PNG file. This establishes the anchor.
Build a task list for images 2+ using image 1 as --ref <path-to-image-01.png>.
Dispatch images 2+ in batches per the ## Batch Generation Policy: backend native batch first, runtime parallel tool calls second, sequential only as fallback.
Report progress after each completed image. On failure, retry only the failed item once from the same saved prompt file.

Watermark (if enabled in EXTEND.md): append to the generation prompt:

Include a subtle watermark "[content]" positioned at [position].
The watermark should be legible but not distracting.

See references/config/watermark-guide.md.

Backend selection: per the Image Generation Tools rule at the top — use whatever is available, ask once if multiple, before any generation. Under --yes, use the EXTEND.md preference and fall back to the first available backend. Prompt files MUST exist before invoking any backend.

Session ID (if the backend supports --sessionId): use cards-{topic-slug}-{timestamp} for every image; combined with the ref chain this gives maximum consistency.

Step 4: Completion Report

Image Card Series Complete!

Topic: [topic]
Mode: [Quick / Custom / Detailed]
Strategy: [A/B/C/Combined]
Style: [name]
Palette: [name or "default"]
Layout: [name or "varies"]
Location: [directory]
Images: N total

✓ analysis.md
✓ outline.md
✓ outline-strategy-a/b/c.md (detailed mode only)

- 01-cover-[slug].png ✓ Cover (sparse)
- 02-content-[slug].png ✓ Content (balanced)
- ...
- NN-ending-[slug].png ✓ Ending (sparse)

Content Breakdown Principles

Position	Purpose	Typical layout
Cover (image 1)	Hook + visual impact	`sparse`
Content (middle)	Core value per image	`balanced` / `dense` / `list` / `comparison` / `flow`
Ending (last)	CTA / summary	`sparse` or `balanced`

For the style × layout compatibility matrix, see the Style × Layout Matrix above.

Image Modification

Action	How
Edit	Update `prompts/NN-{type}-{slug}.md` first, then regenerate with the same session ID
Add	Specify position, create prompt, generate, renumber subsequent files `NN+1`, update outline
Delete	Remove files, renumber subsequent `NN-1`, update outline

Always update the prompt file before regenerating — it's the source of truth and makes changes reproducible.

Text correction policy:

If a card's title, body copy, tags, or any other rendered text is misspelled, garbled, hard to read, or visually weak, do not patch the bitmap with code.
For text-correction regenerations, write a new prompt file and a new output path so the flawed candidate is preserved for comparison.
Post-processing is limited to crop, resize, compression, or format conversion that does not alter text or the main composition.

References

File	Content
`references/confirmation.md`	Verbatim AskUserQuestion copy for every confirmation path
`references/style-presets.md`	Full preset shortcut definitions
`references/presets/<style>.md`	Per-style element definitions
`references/palettes/<name>.md`	Per-palette color definitions
`references/elements/canvas.md`	Aspect ratios, safe zones, grid layouts
`references/elements/image-effects.md`	Cutout, stroke, filters
`references/elements/typography.md`	Decorated text, tags, text direction
`references/elements/decorations.md`	Emphasis marks, backgrounds, doodles, frames
`references/workflows/analysis-framework.md`	Content analysis framework
`references/workflows/outline-template.md`	Outline template with layout guide
`references/workflows/prompt-assembly.md`	Prompt assembly guide
`references/config/preferences-schema.md`	EXTEND.md schema
`references/config/first-time-setup.md`	First-time setup flow
`references/config/watermark-guide.md`	Watermark configuration

Notes

Auto-retry once on generation failure before reporting an error.
For sensitive public figures, use stylized cartoon alternatives.
Smart Confirm (Step 2) is required; Detailed mode adds a second confirmation (2a + 2c).

Changing Preferences

EXTEND.md lives at the first matching path listed in Step 0. Three ways to change it:

Edit directly — open EXTEND.md and change fields. Full schema: references/config/preferences-schema.md.
Reconfigure interactively — delete EXTEND.md (or ask "reconfigure baoyu-image-cards preferences" / "重新配置"). The next run re-triggers first-time setup.
Common one-line edits:
- preferred_image_backend: auto — default; runtime-native tool wins, falls back to the only installed backend, asks only if multiple non-native are present.
- preferred_image_backend: codex-imagegen — pin to Codex's built-in.
- preferred_image_backend: baoyu-imagine — pin to the baoyu-imagine skill.
- preferred_image_backend: ask — confirm backend every run.
- generation_batch_size: 4 — default number of images to render concurrently when the backend/runtime supports batch or parallel generation.
- preferred_style: notion, preferred_layout: dense, preferred_palette: macaron, language: zh.
- watermark.enabled: true + watermark.content: "@handle" — add a watermark.

When it activates

Automatically, when a task matches. Use when user mentions "小红书图片", "小红书种草", "小绿书", "微信图文", "微信贴图", "image cards", "图片卡片", or wants social media infographic series.

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 — 7/30/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

guanyang / open-agent-hub

skills/baoyu-image-cards/SKILL.md

View on GitHub ↗

License MITImported 7/31/2026Version v1Bundled 2 files

Illustrative Want to see what baoyu-image-cards 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

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What it changes

Track record

Use this skill

Image Card Series Generator

User Input Tools

Image Generation Tools

Batch Generation Policy

Confirmation Policy

Language

Options

Dimensions

Styles (12)

Layouts (8)

Palettes (optional override)

Presets (style + layout shortcuts)

Auto-Selection

Style × Layout Matrix

Outline Strategies

Reference Images

File Layout

Workflow

Step 0: Load EXTEND.md ⛔ BLOCKING

Step 1: Analyze Content → analysis.md

Step 2: Smart Confirm ⚠️ REQUIRED

Step 3: Generate Images

Step 4: Completion Report

Content Breakdown Principles

Image Modification

References

Notes

Changing Preferences

When it activates

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry

Step 1: Analyze Content → `analysis.md`