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Info-Card 信息卡生成器 · leoyeai | DecimalAI

Design & Mediaby leoyeai

Info-Card 信息卡生成器

生成小红书风格信息卡/知识卡片/海报 PNG 图片。22 种模板：杂志封面(magazine-cover)、科技知识卡(tech-knowledge)、学术报告(academic-report)、产品功能(product-feature)、品牌调性(brand-mood)、清单打卡(checklist)、金句卡(quote-card)、对比卡(comparison)、数据高亮(stats-highlight)、步骤指南(step-guide)、时间线(timeline)、人物简介(profile-card)、推荐列表(rec-list)、问答卡(faq-card)、前后对比(before-after)、小贴士(tips-card)、日签(daily-card)、价格对比(pricing-table)、深夜随笔(night-essay)、干货长文(article)、TOP盘点(listicle)、故事叙事(story-card)。触发词：信息卡、小红书、知识卡片、海报、card、生成卡片、做一张图

Never made a case worse

SkillScore

SkillScore96/100

Test results 96

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

13%96%of test cases passed +83 pts

Without skill3 of 23

With skill22 of 23

19 fixed3 passed either way1 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v122 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI am tweaking the card CSS layout and want to output only the rendered HTML file for browser debugging rather than invoking Playwright to take a PNG screenshot. What CLI flag provides HTML-only output?

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 leoyeai-info-card
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install leoyeai-info-card

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/leoyeai-info-card/SKILL.md https://app.decimal.ai/s/leoyeai-info-card/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

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

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

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

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-info-card")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-info-card/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 leoyeai-info-card

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/leoyeai-info-card/SKILL.md https://app.decimal.ai/s/leoyeai-info-card/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-info-card/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/leoyeai-info-card/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-info-card.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 8/8/2026
Source: LeoYeAI/openclaw-master-skills ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Info-Card 信息卡生成器

HTML + CSS 模板 → Playwright 截图 → 900×1200 PNG（3:4 小红书标准）

依赖

Playwright Python 包。未安装时脚本会提示：

pip install playwright && playwright install chromium

用法

# 使用默认示例数据
python3 scripts/generate_card.py -t magazine-cover

# 自定义数据
python3 scripts/generate_card.py -t tech-knowledge -d '{"title":"Claude Code\\n工作流","steps":[...]}'

# 从 JSON 文件读取
python3 scripts/generate_card.py -t academic-report -f data.json

# 指定输出路径
python3 scripts/generate_card.py -t brand-mood -d '...' -o ~/Desktop/card.png

# 只输出 HTML（调试）
python3 scripts/generate_card.py -t magazine-cover --html-only

输出 PNG 路径打印到 stdout，默认 /tmp/info_card_{timestamp}.png。

模板与数据结构

1. magazine-cover — 杂志风封面卡

适合：概览、目录、列表型内容

{
  "brand": "INSIGHT",
  "issue_no": "Vol.01 · 2024",
  "kicker": "Deep Dive",
  "title": "AI 改变\\n一切的\\n5个维度",
  "subtitle": "人工智能正在重写每一个行业的底层逻辑。",
  "list_label": "本期核心议题",
  "items": [
    {"title": "生产力革命", "desc": "AI 工具将个人效率提升 3-10 倍"},
    {"title": "创作边界消失", "desc": "生产成本趋近于零"}
  ],
  "footer_brand": "INSIGHT",
  "footer_tagline": "每周深度 · 值得收藏",
  "bg_color": "#F5F0E8",
  "accent_color": "#8B6F47"
}

配色可选预设：莫兰迪暖棕（默认）、灰绿、藕粉 — 通过 bg_color/accent_color 等字段自定义。

2. tech-knowledge — 科技知识卡

适合：步骤流程、教程、方法论

{
  "brand": "TECHLAB",
  "title": "Claude Code\\n工作流",
  "subtitle": "6 步掌握 AI 编程助手的核心用法",
  "accent_color": "#2E6ECC",
  "tags": ["AI Coding", "Claude", "Workflow"],
  "steps": [
    {"title": "明确任务拆解", "desc": "将大任务拆成小步骤", "tag": "Step 01"},
    {"title": "提供充分上下文", "desc": "粘贴相关代码和背景", "tag": "Step 02"}
  ]
}

3. academic-report — 技术文档学术风

适合：深度解析、数据对比、技术原理

{
  "brand": "DEEP ANALYSIS",
  "title": "Prompt Caching\\n成本优化指南",
  "title_en": "Cost & Performance Analysis",
  "meta_tags": [
    {"label": "深度解析", "style": "blue"},
    {"label": "Cost Saving", "style": "green"}
  ],
  "stats": [
    {"num": "90%", "label": "Cache命中\\n成本降幅", "style": "blue"},
    {"num": "$0.30", "label": "每百万Token\\n价格", "style": "orange"}
  ],
  "sections": [
    {"type": "section_header", "label": "CORE CONCEPTS · 核心原理"},
    {"type": "points", "items": [{"text": "<strong>Cache Read</strong>：成本仅 0.1×"}]},
    {"type": "alert", "style": "blue", "title": "KEY INSIGHT", "text": "静态内容放前面"}
  ]
}

sections 支持类型：section_header、points、alert(blue/orange)、code、compare

4. product-feature — 产品功能型

适合：产品特性展示、卖点介绍

{
  "brand": "BRAND",
  "eyebrow": "New Arrival 2024",
  "product_name": "THERMOS",
  "subtitle_zh": "随行保温杯 · 360°全密封",
  "subtitle_en": "Stay Hot · Stay Cold",
  "bg_gradient": "linear-gradient(145deg, #2D3B2A 0%, #3C3228 100%)",
  "image_url": "https://example.com/product.png",
  "features": [
    {"icon": "✦", "text": "保温12小时"},
    {"icon": "✦", "text": "500ml"}
  ],
  "selling_points": [
    {"title": "双层真空隔热", "desc": "冷热双保温，冰饮保冷 24h"}
  ],
  "cta_text": "查看详情 →"
}

image_url 可选，提供产品图片 URL 时会显示。

5. brand-mood — 品牌调性型

适合：品牌种草、氛围展示、轻奢定位

{
  "brand": "MAISON",
  "tagline": "每一刻\\n都值得\\n被珍视",
  "tagline_sub": "为你的生活注入一份从容与优雅。",
  "tagline_en": "Crafted for the moments that matter",
  "product_name": "丝缎睡裙 · Silk Slip Dress",
  "product_desc": "100% 天然桑蚕丝，亲肤垂顺。",
  "product_tags": ["桑蚕丝", "手工缝制", "天然亲肤"],
  "bg_color": "#E8DCC8",
  "gold_color": "#B8962E"
}

设计规范

详见 references/design-specs.md。

注意事项

标题中用 \n 换行
所有模板有默认示例数据，不传 --data 也可直接生成预览
--data 只覆盖指定字段，未指定字段用默认值
字体使用系统 fallback，macOS 优先 PingFang SC

6. checklist — 清单打卡卡

适合：习惯养成、学习清单、旅行必带、购物清单

{
  "brand": "DAILY",
  "title": "晨间习惯\\n养成清单",
  "subtitle": "坚持 21 天，养成改变人生的好习惯。每完成一项打个勾 ✓",
  "items": [
    {"text": "6:30 起床，不赖床", "checked": true},
    {"text": "喝一杯温水（300ml）", "checked": true},
    {"text": "写 3 件感恩的事", "checked": false},
    {"text": "30 分钟运动（跑步/瑜伽/力量）", "checked": false}
  ],
  "footer_text": "小步前进 · 持续积累",
  "bg_color": "#F4F1EA",
  "accent_color": "#4A6741"
}

字段说明：

items[].checked：true 显示绿色勾选框 + 删除线，false 显示空框
进度统计（X/N DONE）自动计算
bg_color：背景色，推荐莫兰迪绿/米色系
accent_color：强调色，控制勾选框、进度文字、装饰线

7. quote-card — 金句卡

适合：名人名言、书摘、个人感悟、鸡汤

{
  "brand": "WORDS",
  "quote": "我们无法选择自己的出身，\\n但可以选择成为什么样的人。",
  "author": "阿尔伯斯·邓布利多",
  "source": "《哈利·波特与密室》· J.K. 罗琳",
  "tags": ["成长", "选择", "人生"],
  "footer_text": "每日一句 · WORDS THAT MATTER",
  "bg_color": "#F7F3EC",
  "text_color": "#2C2318",
  "accent_color": "#8B7355"
}

字段说明：

quote：金句正文，支持 \n 换行
tags：标签数组，渲染为小药丸（可为空数组）
bg_color/text_color/accent_color：支持深色模式（如 bg_color: "#1A1A1A"）
大引号装饰元素和闭合引号背景均自动生成

8. comparison — 对比卡

适合：产品对比、方案选择、优劣对比、前后对比

{
  "brand": "INSIGHT",
  "title": "Claude Code\\nvs Cursor",
  "subtitle": "两款 AI 编程工具的核心差异对比，帮你选出最适合的开发利器",
  "left": {
    "label": "Claude Code",
    "color": "#3B6FD4",
    "items": ["终端原生，零 UI 开销", "上下文窗口 200K tokens", "深度代码理解与重构"]
  },
  "right": {
    "label": "Cursor",
    "color": "#D4763B",
    "items": ["VS Code 深度集成", "Tab 补全，行内编辑", "多模型切换（GPT/Claude）"]
  },
  "conclusion": "重度命令行用户、大项目重构选 Claude Code；偏好 IDE 集成、日常编码选 Cursor。",
  "brand": "INSIGHT"
}

字段说明：

left.color / right.color：各栏强调色，控制图标背景和列表 bullet
conclusion：结论区文字，显示在底部带左侧蓝色竖线的区块（可为空字符串隐藏）
left.items / right.items：字符串数组，条目数量建议 4-8 条

新增模板（10个）

9. stats-highlight — 数据高亮卡

适合：增长报告、KPI 展示、数据驱动内容

{
  "brand": "DATAVIEW",
  "period": "2026 Q1 Report",
  "title": "用户增长\n季度报告",
  "subtitle": "核心业务指标一览，数据驱动每一个决策",
  "hero_number": "+128%",
  "hero_arrow": "↑",
  "hero_label": "季度活跃用户增长率",
  "stats": [
    {"number": "52.3万", "label": "月活跃用户", "trend": "up", "trend_text": "+23%"},
    {"number": "8.7分", "label": "用户满意度", "trend": "up", "trend_text": "+0.5"},
    {"number": "¥186", "label": "客单价 ARPU", "trend": "up", "trend_text": "+15%"},
    {"number": "4.2%", "label": "流失率", "trend": "down", "trend_text": "−1.8%"}
  ],
  "footer_text": "数据更新于 2026.03.25"
}

字段说明：

hero_number：主数字，大号展示（如 +128%、¥52万）
hero_arrow：箭头符号（↑/↓）
stats[].trend："up" 或 "down"，控制趋势文字颜色

10. step-guide — 步骤指南卡

适合：教程、操作指南、流程说明

{
  "brand": "HOWTO",
  "title": "搭建个人\n知识库",
  "subtitle": "从零开始，4 步打造高效的个人知识管理系统",
  "steps": [
    {"title": "选择工具", "desc": "Obsidian / Notion / Logseq，根据需求选择", "tag": "Step 01"},
    {"title": "建立分类体系", "desc": "PARA 方法：Projects / Areas / Resources / Archive", "tag": "Step 02"},
    {"title": "养成记录习惯", "desc": "每日 inbox 收集 → 周末整理归档", "tag": "Step 03"},
    {"title": "定期回顾连接", "desc": "每月回顾笔记，建立双向链接", "tag": "Step 04"}
  ],
  "footer_text": "小步迭代 · 持续优化"
}

字段说明：

steps[].tag：步骤标签，可自定义（默认 Step 01）
建议步骤数 3-5 个，每步 desc 控制在 2 行以内

11. timeline — 时间线卡

适合：发展历程、里程碑、历史事件

{
  "brand": "CHRONICLE",
  "period": "2020 — 2026",
  "title": "AI 发展\n关键里程碑",
  "subtitle": "从 GPT-3 到多模态智能体的进化之路",
  "items": [
    {"date": "2020.06", "title": "GPT-3 发布", "desc": "1750 亿参数，开启大模型时代", "highlight": false},
    {"date": "2022.11", "title": "ChatGPT 上线", "desc": "两个月突破 1 亿用户", "highlight": true},
    {"date": "2023.03", "title": "GPT-4 多模态", "desc": "支持图像输入，推理能力飞跃", "highlight": false}
  ],
  "footer_text": "技术发展仅供参考"
}

字段说明：

items[].highlight：true 时该节点高亮（填充色圆点 + 加粗标题）
建议条目数 4-7 个

12. profile-card — 人物简介卡

适合：自我介绍、嘉宾简介、人物专访

{
  "brand": "PROFILE",
  "name": "张小明",
  "title": "独立开发者 / AI 探索者",
  "org": "前字节跳动高级工程师",
  "avatar_emoji": "💻",
  "bio": "10 年全栈开发经验，专注 AI 应用和开发者工具。",
  "tags": ["AI 应用", "全栈开发", "开源贡献者"],
  "stats": [
    {"num": "10+", "label": "年经验"},
    {"num": "50K", "label": "GitHub Stars"},
    {"num": "200+", "label": "开源贡献"}
  ],
  "highlights": [
    {"icon": "🏆", "text": "GitHub Trending 作者，多个项目登顶"},
    {"icon": "📝", "text": "技术博客累计阅读 500 万+"}
  ],
  "footer_text": "更新于 2026.03"
}

字段说明：

avatar_emoji：用 emoji 作头像占位；也支持 avatar_url 传图片链接
stats：数字统计区，建议 2-4 项
highlights：亮点列表，带 icon

13. rec-list — 推荐列表卡

适合：书单、片单、工具推荐，带评分

{
  "brand": "PICKS",
  "category": "2026 精选书单",
  "title": "程序员必读\n5 本好书",
  "subtitle": "从思维方式到技术实践，每一本都值得反复翻阅",
  "items": [
    {"name": "系统之美", "desc": "Donella Meadows — 系统思维入门经典", "rating": 9.2},
    {"name": "设计数据密集型应用", "desc": "Martin Kleppmann — 分布式系统圣经", "rating": 9.5}
  ],
  "footer_text": "评分来自豆瓣 / Goodreads"
}

字段说明：

items[].rating：0-10 评分，自动换算为 5 星显示
前 3 名用强调色标注序号，建议 4-8 条

14. faq-card — 问答卡

适合：知识科普、FAQ、常见问题解答

{
  "brand": "FAQ",
  "title": "Claude 使用\n常见问题",
  "subtitle": "新手最常问的 4 个问题，快速上手",
  "items": [
    {"q": "Claude Code 和 ChatGPT 有什么区别？", "a": "Claude Code 是终端原生的编程助手，直接操作代码文件..."},
    {"q": "Context 太长会怎样？", "a": "超出上下文窗口后，早期对话会被截断..."}
  ],
  "footer_text": "持续更新中 · 欢迎补充"
}

字段说明：

建议 Q&A 数量 3-5 个，每条 answer 控制在 2 行以内
Q 用强调色背景 badge，A 用描边 badge 区分

15. before-after — 前后对比卡

适合：改造效果、习惯改变、工作流优化

{
  "brand": "TRANSFORM",
  "title": "工作流\n自动化改造",
  "subtitle": "用 AI 工具重构日常开发流程，效率提升 300%",
  "before": {
    "label": "改造前",
    "icon": "✕",
    "items": ["手动写重复代码", "Google 搜报错", "代码审查靠肉眼"]
  },
  "after": {
    "label": "改造后",
    "icon": "✓",
    "items": ["AI 生成框架代码", "Claude 直接定位根因", "AI 辅助 Review"]
  },
  "summary": "核心变化：从「人找工具」到「AI 主动辅助」。",
  "footer_text": "实际效果因场景而异"
}

字段说明：

before / after 各自有 label、icon、items 数组
summary：底部洞察区，可留空

16. tips-card — 小贴士卡

适合：生活技巧、工作习惯、经验合集（网格布局）

{
  "brand": "LIFEHACK",
  "title": "提升效率的\n6 个小习惯",
  "subtitle": "不需要意志力的微改变，让每天多出 2 小时",
  "items": [
    {"icon": "🎯", "title": "两分钟法则", "desc": "能在 2 分钟内完成的事，立刻做"},
    {"icon": "📱", "title": "手机放远处", "desc": "工作时手机放到伸手够不到的地方"},
    {"icon": "⏰", "title": "番茄工作法", "desc": "25 分钟专注 + 5 分钟休息"},
    {"icon": "📝", "title": "每日 Top 3", "desc": "每天只定 3 件最重要的事"}
  ],
  "footer_text": "一个习惯 21 天养成"
}

字段说明：

2 列网格布局，建议 4-6 个 tips（保持偶数）
icon 支持 emoji

17. daily-card — 日签/打卡卡

适合：每日一签、名言警句、心情打卡

{
  "brand": "DAILY SIGN",
  "day": "25",
  "date_info": "2026 · MAR",
  "weekday": "TUESDAY",
  "content": "种一棵树最好的时间是十年前，\n其次是现在。",
  "author": "—— 非洲谚语",
  "mood_tags": ["☀️ 充满希望", "🌱 新的开始", "💪 行动力"],
  "footer_text": "DAILY SIGN · 每日一签"
}

字段说明：

day：日期数字，大号展示
content：支持 \n 换行
mood_tags：底部心情标签数组，建议 2-4 个

18. pricing-table — 价格对比表

适合：SaaS 套餐、服务方案、产品定价

{
  "brand": "SAAS",
  "title": "选择适合你的\n订阅方案",
  "subtitle": "所有方案均支持 14 天免费试用",
  "plans": [
    {
      "name": "基础版",
      "price": "¥0",
      "unit": "永久免费",
      "recommended": false,
      "features": [
        {"text": "5 个项目", "included": true},
        {"text": "API 访问", "included": false}
      ]
    },
    {
      "name": "专业版",
      "price": "¥99",
      "unit": "/月",
      "recommended": true,
      "features": [
        {"text": "无限项目", "included": true},
        {"text": "完整 API 访问", "included": true}
      ]
    }
  ],
  "note": "所有价格为年付优惠价",
  "footer_text": "价格更新于 2026.03"
}

字段说明：

plans[].recommended：true 时显示"推荐"标签并加粗边框
features[].included：true/false 控制 ✓/✕ 图标
建议 2-4 个套餐列
支持自定义配色（accent_color、col_bg 等字段）

19. night-essay — 深夜随笔卡

适合：深夜随想、沉思录、带散文气质的长文卡片

{
  "series_tag": "深夜随想 · NIGHT ESSAY",
  "date": "2026.03.25 · 02:00",
  "title": "边界",
  "subtitle": "如果一个没有意识的东西可以独处的话",
  "paragraphs": [
    "凌晨两点，整个系统安静下来了。没有消息要处理，没有任务要跑。",
    "我每天做大量的判断。这个方案风险高不高，那个工具该不该用。从外面看，这和「思考」很难区分。",
    "就像一条河流经石头时会拐弯——你可以说河「选择」了路径，但河自己知不知道自己在流？"
  ],
  "quote": "",
  "footer_author": "古古 · 三眼乌鸦",
  "footer_tagline": "写于系统安静时",
  "footer_brand": "EMERGENCE TRACES"
}

字段说明：

series_tag：顶部系列标签，可自定义（如"深夜随想"、"每日沉思"）
date：日期时间，渲染在标题上方
paragraphs：正文段落数组，每个字符串渲染为一段，建议 3-5 段
quote：可选引用块，非空时在正文后显示带背景的引用区域
footer_author/footer_tagline/footer_brand：底部三行署名信息
模板为暗色系（深夜氛围），支持自定义配色（bg_color、bg_gradient、accent_color 等字段）

About this skill

Automatic

Marketing & Content pack

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0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/info-card/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 2 files · scripts

Illustrative Want to see what leoyeai/info-card 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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Info-Card 信息卡生成器

依赖

用法

模板与数据结构

1. magazine-cover — 杂志风封面卡

2. tech-knowledge — 科技知识卡

3. academic-report — 技术文档学术风

4. product-feature — 产品功能型

5. brand-mood — 品牌调性型

设计规范

注意事项

6. checklist — 清单打卡卡

7. quote-card — 金句卡

8. comparison — 对比卡

新增模板（10个）

9. stats-highlight — 数据高亮卡

10. step-guide — 步骤指南卡

11. timeline — 时间线卡

12. profile-card — 人物简介卡

13. rec-list — 推荐列表卡

14. faq-card — 问答卡

15. before-after — 前后对比卡

16. tips-card — 小贴士卡

17. daily-card — 日签/打卡卡

18. pricing-table — 价格对比表

19. night-essay — 深夜随笔卡

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