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Editorial Presentation (HTML + PPTX 双模) · ethanyoq | DecimalAI

Design & Mediaby ethanyoq

Editorial Presentation (HTML + PPTX 双模)

可变体的 warm editorial 演示设计系统，用于 presentation、case sharing、项目汇报、战略叙事和产品发布。支持横向全屏 HTML deck 与程序化 PPTX，通过 style profile、语义 slide manifest、构图新颖度规划和反重复验证，在保持编辑设计 DNA 的同时避免固定配色、固定卡片和固定 11 页模板。用户要求 Pfizer/Anthropic/warm editorial/编辑设计风格、类似血液科那套，或需要高规格且非模板化的 HTML/PPT 演示时使用。

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

50%82%of test cases passed +32 pts

Without skill11 of 22

With skill18 of 22

8 fixed10 passed either way3 still failing1 broke

Measured on gemini-3.6-flash8/7/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task制作一份 1920x1080 分辨率大屏投影演讲用的网页幻灯片。为了防止正文和标题由于字体过小而无法看清，常规页面的主标题字号与正文容器 `.wrap` 宽度应该控制在什么区间？

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 ethanyoq-yq-editorial-presentation-html
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("ethanyoq-yq-editorial-presentation-html")
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 ethanyoq-yq-editorial-presentation-html

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/ethanyoq-yq-editorial-presentation-html/SKILL.md https://app.decimal.ai/s/ethanyoq-yq-editorial-presentation-html/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/ethanyoq-yq-editorial-presentation-html/SKILL.md

This gets SKILL.md only. The skill's 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/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/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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("ethanyoq-yq-editorial-presentation-html")
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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/ethanyoq-yq-editorial-presentation-html/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 17 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 ethanyoq-yq-editorial-presentation-html

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/ethanyoq-yq-editorial-presentation-html/SKILL.md https://app.decimal.ai/s/ethanyoq-yq-editorial-presentation-html/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/ethanyoq-yq-editorial-presentation-html/SKILL.md

This gets SKILL.md only. The skill's 17 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/ethanyoq-yq-editorial-presentation-html/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/ethanyoq-yq-editorial-presentation-html.json

This gets SKILL.md only. The skill's 17 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/7/2026
Source: ethanyoq/skill-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

Editorial Presentation (HTML + PPTX 双模)

这个 skill 是什么

一套可规划、可验证、可变体的视觉设计语言 skill，源自 Pfizer 血液科 IFI 案例分享 v5 desktop 版，并把单一成品样式拆成稳定 DNA 与可选 style profile。

核心价值：先按受众、目的、正式度、密度和语义关系规划，再渲染 HTML/PPTX。复用的是编辑设计原则、角色化 token、构图语法与质量门，不是把不同主题替换进同一套米色卡片模板。

双输出模式:

HTML 模式（默认）：用于浏览器演示的横向全屏 deck。F11 下每页固定 100vw × 100vh，支持左右翻页、底部圆点、触屏/滚轮、ESC overview，并完整支持 profile 与构图语法。
PPTX 模式：用于必须交 .pptx 文件的场景（高管会、客户演示、传统企业）。通过 python-pptx 程序化生成，与 HTML 共用 manifest；只选择明确支持 PPTX 的 profile。

两种模式共享的设计 DNA：

温暖但克制的编辑质感、证据可读性和固定 16:9 舞台。
display / body / data 三类字体角色；具体字族由 profile 决定，原始 Pfizer profile 仍使用 Fraunces / Inter / JetBrains Mono。
颜色通过 canvas / surface / ink / primary / secondary / signal / positive / negative 等角色分配，不锁死一组 RGB。
先判断数据关系、基数、密度、不确定性和叙事角色，再分别选择表达家族与构图。
同一 profile 内保持一致，但相邻页面不得机械复用相同构图、几何和背景节奏。
HTML 与 PPTX 共用同一份 slide manifest；格式降级必须记录，不能全部退化为卡片网格。

何时使用(必须主动触发,不要等用户说风格名)

任一信号出现时使用：

明确要求 "warm editorial / Anthropic / Pfizer / 编辑设计 / 类似血液科那套"
"做一个 presentation / slides / PPT / 幻灯片 / 演示文稿 / 汇报" 且上下文要求高规格视觉设计
"做 case sharing / 案例分享 / 项目展示 / 战略汇报"
"给老板/director/VP 看 / 给客户演示"
"类似血液科那套 / Pfizer 风格 / Anthropic 风格 / warm editorial / 编辑设计风格"
"把 X 内容做成可演示的页面 / 做成 deck"
用户给一份内容(产品发布、市场调研、技术分享、案例复盘),需要做成展示

普通 PPT 编辑、只改一行文字或用户已指定另一套视觉系统时不要抢占。触发后也先选择 profile 与构图语法，不默认套原始 Pfizer 配色。

第 0 步:判断输出格式

问自己 / 看用户表述:

用户说...	用
"做 HTML 页面 / 网页 / 浏览器演示 / F11 全屏 / 加跳转链接 / 嵌入视频"	HTML 模式
"做 PPT / .pptx / PowerPoint / 幻灯片文件 / 给传统企业用"	PPTX 模式
"做 presentation"(没说格式)	默认 HTML 模式(更高保真),并主动问"要不要也出一份 PPT 版?"
"做一份给客户演示用的"(看上下文)	客户是 IT/科技 → HTML;客户是医药/金融/政府 → PPTX
"做幻灯片样式"但没说文件类型	HTML 模式(横向全屏 deck)

如果不确定,默认 HTML(因为它复刻度最高,后续可再衍生 PPT)。

HTML 模式工作流

Step 1 · 读 references

按需读:

references/visual-grammar.md 必读 — profile 选择、密度模式、slide manifest、构图语法与反重复质量门。
references/style-index.json 必读 — 只做 shortlist；选定后只把一个 profile 的角色 token 带入生成上下文。
references/design-tokens.md 必读 — invariant token 与 profile 角色映射。只有用户锁定原始 Pfizer look 时才原样使用 legacy token block。
references/typography.md 必读 — display / body / data 角色与 projection scale；具体字族来自选定 profile。
references/chart-selection.md 必读 — 图表选择规则。先判断数据关系,再选 stat / timeline / matrix / funnel / bar 等组件。禁止把所有数字都画成条形图。
references/components.md — 视觉组件的 HTML+CSS 切片,包含每类组件的"何时用 / 不何时用"。
references/layouts.md — 横向全屏 deck 的 slide 节奏、grid 布局、navigation。
references/rewrite-existing-html.md — 改写已有 HTML deck 的专用流程。保留内容 section,只替换演示 shell / CSS / JS。
references/qa.md — 生成后检查。重点查主内容宽度、垂直沉底、页脚遮挡、overview 和滚动条。

Step 2 · 用 behavior-only shell 起手

assets/deck-shell.html 是默认行为骨架：横向全屏 deck shell、底部圆点、左右翻页、ESC overview、触屏与滚轮。它不提供成品内容页，避免示例内容和版式预先支配生成。不要从旧的纵向滚动 starter 开始。

动手前先读 shell 的行为 CSS 和 <script>。shell 是 #deck、overview、dots、wheel/touch 的结构来源，不是视觉方向或 slide anatomy 的来源。构图来自 manifest 和选定 profile。assets/starter-template.html 仅作为 legacy Pfizer component gallery，除非用户锁定原始 profile，否则不要加载。

Step 2.2 · 先生成 slide manifest

先整理 brief JSON 和 slide intents，再运行：

python assets/plan_deck.py brief.json -o deck-manifest.json

检查 validation.passed。生成 HTML 与 PPTX 时复用同一份 manifest。用户锁定 profile 时传 locked_profile；未锁定且视觉空间较大时，先展示三个真正不同的方向供选择。相同 brief + profile + seed 必须生成相同 manifest。

Step 2.4 · 用同一 manifest 渲染

新 deck 优先使用共享 renderer，而不是让 HTML 和 PPTX 各自猜一遍布局：

python assets/render_manifest.py deck-manifest.json --output-dir output/deck

它会生成 deck.html、deck.pptx 和 render-report.json，把同一 manifest SHA256 写入两种输出，并记录每页 composition 与 major-region signature。只需 HTML 或 PPTX 时使用 --html-only / --pptx-only。legacy add_*_slide() 只用于兼容旧调用或需要自定义低层绘制的页面。

Step 2.5 · 如果用户给的是已有 HTML

打开 references/rewrite-existing-html.md。这种任务的目标不是重写内容,而是把旧 deck 按本 skill 的 shell 重新渲染:

保留原始 <section class="slide"...>...</section> 的内容块,除非用户明确要求改文案。
替换纵向 scroll-snap / scrollIntoView / IntersectionObserver 为横向 fixed deck。
把旧整页深色主题降级为局部语义色,整体仍用 v5 warm editorial。
按 references/qa.md 跑检测,不通过就先修 skill / 模板规则,再重新生成。

Step 3 · 按需嵌组件

references/components.md 是组件粘贴库和使用说明:

数据展示:stat-card / stat-grid / market-bars / evidence-pyramid / literature-funnel / proof-bars
时间与流程:timeline / clock-path / decision-window / phase-pill / wf-phase / ttet-step / arch-overview
匹配关系:matrix / gene-drug-map / product-path-card / before-after
导航跳转:jump-row / subpage-card / aux-card
CTA / Callout:cta-grid / data-pain / iron-rule-band / anti-pattern-callout
品牌:product-card / dual-products

Step 4 · 图表选择(先做,不要跳过)

打开 references/chart-selection.md，先把每页的关系、基数、系列数、密度、不确定性和叙事角色列出来。表达家族与页面构图是两个决策，不要把 comparison 永远映射为同一张 bar slide。

数据关系	组件
一个数字很重要	stat-card / big-number
多个独立 KPI	stat-grid / stat-strip
时间先后 / 诊疗窗口	timeline / decision-window
输入到输出 / 操作步骤	pipeline / phase-pill
病原体 / 基因型 / 机制 / 产品匹配	matrix / gene-drug-map
文献筛选数量递减	funnel
证据强弱分层	pyramid / evidence-ladder
同一指标跨对象比较	proof-bars / ranked bars
患者旅程阶段覆盖	market-bars

硬规则：

条形图只用于同一指标的横向比较、完成率、覆盖率、通过率。
多个互不相干 KPI 不得画成 proof-bars。
时间路径、机制匹配、证据链、决策流程不得画成普通 bar chart。
一个 deck 默认至少使用 3 类不同表达家族；10-12 页 deck 在内容允许时至少 5 个构图 ID、4 种主几何。
相邻非续页不得使用相同 composition_id；不得连续 3 页使用相同主几何或 tone。
重复同一表达家族时，必须改变构图或注释结构。

Step 5 · 颜色语义化分配

从选定 profile 读取角色 token：canvas、surface、contrast_surface、ink、muted、primary、secondary、signal、positive、negative。状态色语义在整份 deck 内固定；分类色不能误用 positive / warning / negative。按 profile cadence 安排背景节奏，不逐页随机换色，也不连续三页使用同一 tone。

Step 6 · 章节节奏（按论证结构规划，不默认 11 页）

opener → context/tension → evidence → explanation → decision → close

这是论证语法，不是固定页数。投资人、技术评审、学术报告、产品发布和运营复盘应产生不同节奏。每页在 manifest 中声明 narrative_role，需要续页时声明 continuation_of。

每个 slide 固定 width:100vw;height:100vh,内容区居中但必须吃满演讲画布。常规页面优先 .wrap,.frame { max-width:min(1690px,88vw) };内容密集或投影演讲页面可用 .wrap.wide,.frame.wide { max-width:min(1760px,93vw) },避免主体缩在屏幕中间太小。常规页标题应接近 90-106px,lead 应接近 28-36px;长标题或 1440×900 下可降到 78px,但 1920 下 regular title median 不能只有 78-85px。dense-slide 不等于 compact:dense 页可以更宽、更紧,但非 compact dense 页不能把标题封顶到 84px。只有 evidence/table/appendix .compact 页可以降级。页脚留在 slide flex 流里,但 .wrap 不要用 margin:auto auto 0 / margin:auto,否则会和 footer 的 auto margin 叠加,导致偶发内容沉到底部。不要使用页面纵向滚动来展示下一页。

HTML 验证清单

PPTX 模式工作流

Step 1 · 读 references

references/visual-grammar.md 必读 — PPTX 与 HTML 共用 manifest；不允许 PPTX 全部降级为 card grid。
references/style-index.json 必读 — 只选择 formats 包含 pptx 的 profile。
references/pptx-mode.md 必读 — PPT 输出模式的完整说明:design tokens 如何映射到 PPT 主题、组件如何用 shape 实现、哪些视觉特性降级、字体如何嵌入。
references/chart-selection.md 必读 — PPTX 也必须先判断数据关系,不要默认 proof-bars。
references/design-tokens.md — 共享的色板/字号 scale(直接复用)
references/typography.md — 共享的字体规则(直接复用)

Step 2 · 用 generate_pptx.py 生成

assets/render_manifest.py 是新 deck 的默认入口；assets/generate_pptx.py 提供兼容 API 和低层 python-pptx builder：

EditorialDeck 类:封装好的 deck builder,自动应用色板/字体/版面
11 个 legacy slide 模板函数:add_why_slide(), add_hero_slide(), add_evidence_slide(), add_architecture_slide(), add_workflow_slide(), add_coverage_slide(), add_relationship_slide(), add_proof_slide(), add_limitations_slide(), add_bonus_slide(), add_cta_slide()
组件函数:draw_stat_grid(), draw_decision_timeline(), draw_gene_drug_matrix(), draw_evidence_list(), draw_double_bar_chart(), draw_evidence_pyramid(), draw_phase_pill_row(),...
用法:

from generate_pptx import EditorialDeck

deck = EditorialDeck(
    industry="medical",
    style_profile=manifest["deck_profile"],
    manifest=manifest,
)
deck.add_hero_slide(
    eyebrow="HEMATOLOGY · IFI MARKET INTELLIGENCE · 2026",
    title_main="一句话起跑",
    title_accent="AI 编排 7 大血液疾病抗真菌药市场调研",
    sub="用户输入一句话疾病名 → 系统跑通 5 phase 流水线 → 4 件交付物",
    stats=[("38亿", "血液 IFI 总市场"), ("7", "Disease Modules"), ("22", "Engineered Charts"), ("43", "PMID Refs")]
)
deck.add_evidence_slide(
    eyebrow="EVIDENCE · TRACEABILITY",
    title="每一个数字都能回溯",
    funnel=[("PubMed 触达", "数千"), ("初筛", "~500"), ("纳入", "43")],
    pyramid=[("最高级", 18, "green"), ("重要级", 17, "blue"), ("参考级", 7, "gold")]
)
deck.save("output/blood_oncology_v5.pptx")

Step 3 · 字体处理

PPT 不能像浏览器那样动态加载 web 字体。两条路:

A. 字体嵌入(推荐 · Windows + 桌面 PowerPoint):

deck = EditorialDeck(embed_fonts=True)

要求 Fraunces、Inter、JetBrains Mono 已安装，且字体自身允许文档嵌入。生成器先用 python-pptx 写文件，再调用 assets/embed_pptx_fonts.ps1 让桌面 Microsoft PowerPoint 以 EmbedFonts=true 重新保存，并逐一比对请求字体、ppt/fonts/ 与 embeddedFontLst。仅注册了兼容 COM 的 WPS Office 不满足此要求。如果 PowerPoint 不可用、字体未安装或字体授权禁止嵌入，任务必须失败并列出请求字体或缺失字体，不能静默声称已嵌入。

在非 Windows、没有桌面 PowerPoint或不需要嵌入时，使用 embed_fonts=False 和降级字体。

B. 字体降级(共享/在线场景):

Fraunces → "Cambria"(Windows 自带) / "Times New Roman" 或客户机器装的衬线
Inter → "Calibri"(Windows 自带) / "Segoe UI"
JetBrains Mono → "Consolas"(Windows 自带)

降级会损失一点辨识度,但配色/版面/组件仍然 ~85% 还原。

Step 4 · 视觉特性降级地图

HTML 特性	PPT 实现	保真度
profile 角色色	`EditorialDeck(style_profile=...)` 映射到 PPT shape/theme 色	100%
profile cadence	根据 manifest 切换页面 tone；不支持的暗色 profile 在规划阶段拒绝	90%
Fraunces / Inter / Mono	嵌入字体或降级到 Cambria / Calibri / Consolas	95% / 70%
软阴影卡片	shape 加 outer shadow + 圆角 12pt	100%
渐变文字	text fill gradient 三色 stop	100%
噪声纹理	slide background image fill 用 noise.png	80%
径向渐变光晕	shape 径向渐变填充 alpha	90%
玻璃态 backdrop-filter	半透明白色填充 + 软阴影	70%
Section navigation	PPT 没有滚动,改用 slide-jump hyperlinks	N/A
reveal 滚动动效	用 PPT 进入动画(渐入)等价	不同机制
`clamp()` 响应式	PPT 固定布局,16:9 1920×1080 设计	N/A

PPTX 验证清单

关键设计原则(两模通用,不要破坏)

不要全部正文用 Fraunces — 衬线长篇阅读疲劳
不要把所有 deck 都锁定同一 palette — 先选 profile，再按角色 token 和 cadence 用色
不要把所有数字都画成条形图 — 图表先表达关系,再表达大小
不要用纯黑文字 — 用 #141413 暖墨
不要让每页/每屏背景一样 — 按选定 profile 的 cadence 形成对比节拍，不随机换色
不要混用按钮风格 — 主 CTA 一种样式,辅 CTA 另一种,跳转第三种,不要乱
不要把演讲 deck 做成网页报告 — 常规页必须采用 projection typography;放不下就拆页或 compact,不要全局缩字
PPT 模式:不要在 PPT 里写 CSS — 所有视觉走 python-pptx 的 shape API,不要嵌网页
不要把组件数量当作视觉多样性 — 相同白卡、相同标题位置和相同网格，即使名字不同也属于重复
不要用随机性掩盖重复 — 使用稳定 seed 和 novelty penalty；语义正确优先于新奇

常见任务模板

任务 A · "做一份 X 主题的 case sharing presentation"

→ HTML 模式 default → 选择 profile 与密度 → 生成 manifest → 拷贝行为 shell → 按构图语法渲染 → 反重复与视口 QA

任务 B · "做一份 X 主题的 PPT 给客户演示"

→ PPTX 模式 → 生成 manifest → 选择 PPTX-compatible profile → 用 EditorialDeck(style_profile=..., manifest=...) → 按语义构图生成 → save .pptx

任务 C · "把这份 HTML presentation 转成 PPT 版"

→ 解析 HTML 11 sections → 每节调用对应 add_*_slide() → 字体降级 / 嵌入选一 → save

任务 D · "我要 HTML 和 PPT 都要,内容一致"

→ 同一份内容数据先抽成 JSON / dict → 生成一次 manifest → HTML 与 PPTX 分别渲染 → 记录格式替代并分别 QA

为什么这个 skill 重要

原始 Pfizer deck 证明了 warm editorial DNA 的可用性；本 skill 把它从一个固定成品扩展为可选择、可复现和可审计的设计系统。复用它意味着：

每次做 presentation 不必从零定义设计规则，但也不会自动得到同一套配色和卡片
HTML / PPT 双格式无缝切换,保持视觉一致
跨项目沉淀视觉资产，同时让不同受众和内容拥有可辨识的 deck identity
客户/老板第一眼会觉得"这是花了心思的",而不是"模板套出来的"

源版本参考:血液科市场调研 v5 desktop deck。

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skills/07-media-content/yq-editorial-presentation-html/SKILL.md

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License MITImported 10/24/2026Version v1Bundled 17 files

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Editorial Presentation (HTML + PPTX 双模)

这个 skill 是什么

何时使用(必须主动触发,不要等用户说风格名)

第 0 步:判断输出格式

HTML 模式工作流

Step 1 · 读 references

Step 2 · 用 behavior-only shell 起手

Step 2.2 · 先生成 slide manifest

Step 2.4 · 用同一 manifest 渲染

Step 2.5 · 如果用户给的是已有 HTML

Step 3 · 按需嵌组件

Step 4 · 图表选择(先做,不要跳过)

Step 5 · 颜色语义化分配

Step 6 · 章节节奏（按论证结构规划，不默认 11 页）

HTML 验证清单

PPTX 模式工作流

Step 1 · 读 references

Step 2 · 用 generate_pptx.py 生成

Step 3 · 字体处理

Step 4 · 视觉特性降级地图

PPTX 验证清单

关键设计原则(两模通用,不要破坏)

常见任务模板

任务 A · "做一份 X 主题的 case sharing presentation"

任务 B · "做一份 X 主题的 PPT 给客户演示"

任务 C · "把这份 HTML presentation 转成 PPT 版"

任务 D · "我要 HTML 和 PPT 都要,内容一致"

为什么这个 skill 重要

SkillSafety

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