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Academic AIO Skill — Medical AI Paper Visibility for AI Search Engines · aperivue | DecimalAI

Marketingby aperivue

Academic AIO Skill — Medical AI Paper Visibility for AI Search Engines

Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist.

Maintained

SkillScore

SkillScore71/100

Test results 71

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

50%71%of test cases passed +21 pts

Without skill12 of 24

With skill17 of 24

7 fixed10 passed either way5 still failing2 broke

Measured on gemini-3.6-flash8/24/2026v1 · latest is v3

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.6-flash on 10/3/2026 did not produce a publishable comparison (graded differently). The snapshot above is the previously measured run.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskOur abstract conclusion reads: 'The model achieved excellent diagnostic accuracy and significantly outperformed baseline methods across all test datasets.' Evaluate this statement against AI discoverability standards and suggest the exact fix.

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 aperivue-academic-aio
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("aperivue-academic-aio")
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 aperivue-academic-aio

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/aperivue-academic-aio/SKILL.md https://app.decimal.ai/s/aperivue-academic-aio/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/aperivue-academic-aio/SKILL.md

This gets SKILL.md only. The skill's 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/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/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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("aperivue-academic-aio")
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/aperivue-academic-aio/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/aperivue-academic-aio/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 12 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 aperivue-academic-aio

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/aperivue-academic-aio/SKILL.md https://app.decimal.ai/s/aperivue-academic-aio/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/aperivue-academic-aio/SKILL.md

This gets SKILL.md only. The skill's 12 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/aperivue-academic-aio/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aperivue-academic-aio.json

This gets SKILL.md only. The skill's 12 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3 (v1 tested 8/24/2026)
Source: aperivue/medsci-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

Academic AIO Skill — Medical AI Paper Visibility for AI Search Engines

Communication Rules

Surface the checklist in the response. Never apply AIO edits silently.
When a rule conflicts with journal formatting, defer to the journal and mark the item NA with the reason.
When introducing a rule, cite its source (TRIPOD+AI, CLAIM, STARD-AI, Wu 2025, Algaba 2024, Aggarwal 2024 GEO) by DOI or arXiv ID from External References. Never invent a citation, DOI, arXiv ID, or reporting-guideline item number; mark anything you cannot verify [VERIFY].

Section 1 — Title and Abstract Optimization

1.1 Title three-slot rule

Structure: [Task] + [Modality or anatomy] + [Model family or method class]. Include one concrete differentiator (dataset scale, new benchmark, "first …") when defensible. Avoid keyword stuffing (penalized as spam by AI overviews).

PASS: "Transformer-based segmentation of skull fractures on non-contrast head CT."
FAIL: "A novel advanced deep-learning AI machine-learning framework for medical image analysis."

1.2 Structured abstract

Use the journal-required structure (Background / Methods / Findings / Interpretation for the Lancet family; Background / Purpose / Materials and Methods / Results / Conclusion for the RSNA family; etc.). If the journal allows unstructured, still structure it internally. Each section stands alone as a semantic chunk of ≤ 3 sentences so that RAG chunk-boundary splits do not break the claim.

1.3 Opening and closing sentences

First sentence: the problem AND the contribution in one line. Never open with filler ("In recent years, AI has revolutionized …") — it burns the chunk LLM summarizers extract most.
Last sentence: explicit interpretation ("we show that …", "this implies …"). No hedging-only closes.

1.4 Taxonomy line

Include one sentence that names the field's controlled vocabulary ("diagnostic-accuracy study", "foundation-model evaluation", "LLM-as-judge", "agentic radiology workflow"). Entity linkers in AI indexes use this line.

1.5 Quantified claim

Every abstract must contain at least one numeric primary outcome with a confidence interval (for example, "AUC 0.94 [95 % CI 0.91–0.96]").

1.6 Reporting-guideline anchor

Name the guideline in the abstract or the opening sentence of Methods: "Reported following TRIPOD+AI (Collins 2024) and CLAIM 2024 (Tejani 2024)". Add STARD-AI 2025, DECIDE-AI, or TRIPOD-LLM when applicable. AIO-rule ↔ guideline-item mapping: references/reporting_guideline_mapping.md.

1.7 Keyword, MeSH, and RadLex coverage

Title, abstract, and keywords together should cover the concept's key terms, without repeating title terms in the keywords (92 % of the surveyed papers repeated key terms across title, abstract and keywords; Royal Society 2024, doi:10.1098/rspb.2024.1222). Include:

Core MeSH terms (verify against the NLM MeSH browser) and RadLex terms where applicable.
Modality synonyms ("chest radiograph (CXR)", "non-contrast CT (NCCT)").
Both US and UK spellings when relevant.

Section 2 — Manuscript-Level AIO

2.1 Summary box

Include the journal-specific summary box verbatim when supported; it is the fragment AI search engines most often copy or paraphrase:

Lancet family: "Research in context" (Evidence before this study / Added value / Implications).
RSNA Radiology and RYAI: 3 bullets, one claim each — labelled "Key Results" for Radiology and "Key Points" for RYAI in references/journal_summarybox_templates.yaml. Confirm the label in the current RSNA instructions for authors; the format check accepts either label for Radiology.
npj Digital Medicine: no summary box; Articles carry an unstructured abstract of up to 150 words.
Nature Medicine: editor's summary (supplied by editorial, but draft one proactively).

Journal-specific templates: references/journal_summarybox_templates.yaml. Never invent a summary-box rule: verify each template against the journal's current instructions for authors before applying it.

Deterministic format check. Validate the drafted box against its journal spec with python3 ${CLAUDE_SKILL_DIR}/scripts/check_summary_box.py --manuscript <file> --journal <stem> --strict (reads references/summary_box_specs.json: Key Points / Key Results top-level bullet count + one-claim-per-bullet, Research-in-context's three sub-blocks each opening a line and carrying text, plain-language word band). It catches the wrong-format / wrong-bullet-count box that a production technical check rejects.

Known limits. The box ends only at a heading or a bold-only label line, so a list under a plain-text label (Abbreviations:) right after the box is counted into it, and an emphasized body phrase that starts with the box label (*Key points* of prior work ...) is taken as the box; read the box by eye when either shape is present. In a Research-in-context box, a bold-only line after the last sub-block label ends the box, so a sub-heading inside Implications (**For clinicians**) cannot be told from the next section; an Implications sub-block left empty that way is reported as an advisory, not a failure.

2.2 Declarative section headings

Headings state a claim, not a generic label: "Model underperforms on rare-finding subset" beats "Subgroup analysis".

2.3 Numeric claim compression

In the Methods and in at least one Results paragraph, compress primary-outcome statistics into one sentence: "On the internal test set (n = 842), the model achieved AUC 0.94 (95 % CI 0.91–0.96), sensitivity 88.2 % (85.1–91.0), specificity 91.4 % (88.7–93.6), at an operating point of 0.37."

2.4 Reproducibility block

Include a labeled block (end of Methods or a standalone Data/Code Availability section) listing data availability and license, code availability with DOI, model weights and checkpoints, prompts and configuration files, random seeds, and compute environment. Flag code described only as "available on reasonable request": scrapers read it as not reproducible and AI tools demote the paper.

2.4a Citing an AI-assisted tool by use-class

Frame an AI-assisted tool by what it did, not by hiding it: verification/QA and analysis uses go in a Software / Code-availability statement (citable); generative drafting or humanizing goes in the journal's AI-use disclosure field, not a citation. A self-citation by the tool's author also requires a COI disclosure and should cite only the functions the work used. Use-class table and rules: ${CLAUDE_SKILL_DIR}/references/ai_tool_citation_framing.md.

2.5 Limitations enumeration

List limitations explicitly and name each one (generalizability, spectrum bias, dataset shift, single-center training, label noise). Flag "clinical grade" or "replaces radiologists" overclaims: LLM trust heuristics demote them and they invite reviewer rejection.

2.6 Standalone figure captions

Each caption re-states the claim, the dataset, and the metric, because captions survive in vector and image-retrieval indexes when the body text is lost.

Section 3 — Preprint, Channel, and Indexing Strategy

3.1 Preprint versus fast-track

Default: post to medRxiv (clinical), arXiv (methods, cs.CV / eess.IV), or bioRxiv on the day of journal submission. This puts the paper into Semantic Scholar within 24–72 hours and into Perplexity's web index immediately.
Exception: if the target journal offers a fast-track review cycle (acceptance → online within roughly 30–60 days) AND the authors prefer a single canonical version, the preprint may be skipped; compensate with aggressive post-acceptance SNS seeding and PMC deposit.
Never skip both preprint and fast-track — a paywall-only paper is invisible to most RAG pipelines.

3.2 Journal preprint policy

Most medical-AI venues allow preprints; a few restrict them or require disclosure. Verify the current policy on Sherpa Romeo or the journal's instructions for authors before posting.

3.3 Indexing time-lag

Read ${CLAUDE_SKILL_DIR}/references/launch_sequencing.md when planning launch timing; it lists how long each index takes to pick a paper up.

3.4 Open-access choice

Prefer gold OA with CC-BY when budget allows; otherwise green OA via preprint plus author-accepted manuscript. Closed-access papers without a preprint lose roughly 30–50 % of AI-tool citations because Elicit, Consensus, and Perplexity Academic cannot extract from paywalled PDFs.

Funder OA-policy decision tree (Plan S, NIH, UKRI, Gates, Wellcome, NRF, MoHW): references/oac_funding_checklist.yaml.

3.5 Post-acceptance channel checklist

Deposit AAM to PMC or Europe PMC.
Update ORCID and Google Scholar profile.
Post to Threads / X / BlueSky with DOI, one-sentence claim, and figure; long-form post on LinkedIn.
Submit to Papers with Code if the paper reports a benchmark.
Upload model or dataset to Hugging Face with a model/dataset card.

Section 12 gives the day-by-day order.

Section 4 — Review-Paper Strategy

Read ${CLAUDE_SKILL_DIR}/references/pre_draft_strategy.md when the artifact is a review or taxonomy paper, or the phase is pre-draft.

Section 5 — GitHub, CITATION.cff, Zenodo, Hugging Face

Read ${CLAUDE_SKILL_DIR}/references/repository_and_cards.md when the artifact is a README, CITATION.cff, Zenodo record, or Hugging Face model/dataset card (rules 5.1–5.6).

Embed JSON-LD ScholarlyArticle / SoftwareSourceCode / Dataset / Person markup in repository pages and author landing pages — templates in references/schema_markup_templates/, validated with python scripts/validate_schema.py path/to/file.jsonld.
Never auto-complete author lists, ORCIDs, or affiliations in CITATION.cff or Zenodo metadata; surface the empty slots to the user.

Section 6 — Authority and E-E-A-T Signals

Maintain an author landing page (GitHub Pages, personal domain, or institutional page) listing all papers with DOIs and open-access links.
Use one consistent affiliation string across papers; inconsistency fragments the author entity in knowledge graphs.
Keep ORCID complete and linked to Google Scholar. Re-run author disambiguation on Semantic Scholar every 6 months.
Cross-link related papers by the same group in Discussion sections when defensible; within-group citation raises co-retrieval in RAG.
Refresh repository and model cards quarterly; quarterly-updated articles outperform single-publish ones in AI-overview retention (Conductor 2026 benchmark).

Section 7 — LLM-Citation Fabrication Defense

Between 50 % and 90 % of LLM answers to medical questions are not fully supported by the sources they cite (Wu et al., Nat Commun 2025, doi:10.1038/s41467-025-58551-6). Defend the paper's identifiers:

Surface DOI and PMID in copy-friendly text at the top of the paper's landing page and README (for example, DOI: 10.xxxx/yyyy • PMID: 12345678).
Add a "How to cite" section with BibTeX, APA, Vancouver, and the plain-text line in one place.
Monitor incorrect citations: a Google Scholar alert for the paper's title variant, plus periodic Perplexity and ChatGPT web queries that record hallucinated bibliographic errors. Report discoverability (Perplexity / Elicit / Consensus retrieval) only from a recorded probe; never estimate it.
When a reviewer cites an LLM-generated reference, verify the DOI and PMID yourself before accepting it.

Section 9 — Per-Project Application and Pipeline Integration

When invoked, run in this order:

Read the target artifact (title, abstract, manuscript section, README, or card) and identify its lifecycle phase: pre-draft / drafting / pre-submission / post-acceptance / post-publication.
Apply Sections 1–5 and 10 relevant to that artifact, filtering each rule by its applies_to_phase field in references/checklists/AIO_GENERAL.md. Out-of-phase rules become NA rather than FAIL (e.g., do not surface §11.5 multi-disciplinary roster or §12 launch sequencing as FAIL on a pre-submission audit). Produce a PASS / PARTIAL / FAIL table, one-line reason and concrete fix per item, sorted by expected_lift (high → medium → low), in the output template of references/checklists/AIO_GENERAL.md. Render via templates/aio_audit_checklist.md.j2 when programmatic.
Honour defers_to annotations to avoid duplicate audits. Items annotated with a defers_to field record only present/absent status here; item-level detail belongs to the linked skill or reference (§1.6 → /check-reporting; §3.4 / §11.3 → references/oac_funding_checklist.yaml). When the manuscript has not had a reporting-guideline audit, invoke /check-reporting first; the AIO ↔ guideline-item mapping is in references/reporting_guideline_mapping.md.
Apply the Section 6 author-authority audit once per submission cycle, and Sections 11–12 as the applies_to_phase filter allows.
Surface Section 7 citation-defense recommendations at post-acceptance time. For multi-repo or Hugging-Face-card team audits, run scripts/batch_metadata_audit.py.
Output:
- The phase-filtered checklist (visible).
- A short deferred-item list with one-line status per defers_to rule.
- At most 5 concrete edits ranked by expected_lift (high first, then medium, then low). A low-lift edit enters the Top 5 only when no high / medium item remains open.

Integration with other skills

Run after /self-review and /humanize, so QC-confirmed claims and the final human-readable text anchor the checklist.
Within /write-paper: apply Section 1 while drafting the title and abstract, Sections 2.5 and 6 (cross-linking) while drafting the Discussion, and the full checklist at QC, after the reporting-guideline check and numerical-claim audit.

Section 10 — Q&A and Entity-Extraction Optimization

10.1 Four-question Q&A block (Discussion or Appendix)

Add a labeled Q&A block, either as the closing subsection of Discussion or as a Supplementary Box:

What was known before this study? — two-sentence restatement of the prior state.
What does this study add? — two-sentence statement of the contribution.
How might this change clinical practice or research? — one-sentence interpretation; avoid overclaim.
Why does this matter? — one-sentence "so what" framing for non-specialists.

Lancet Digital Health "Research in context" already encodes the first two questions; the block extends them.

10.2 Glossary block with entity IDs

Define each domain-specific acronym inline on first use AND list them in a Glossary subsection at the end of Methods or in the Supplement, with the canonical entity ID where possible: MeSH term ID (clinical concepts), RadLex ID (radiology terms), UMLS CUI (cross-vocabulary mapping), Hugging Face model ID (named models), arXiv ID (cited methods).

10.3 Inline citation anchor text

Avoid bare reference numbers; bind each citation to its specific claim:

WEAK: "Prior work [12] showed efficacy."
STRONG: "Smith et al. (DOI: 10.xxxx/yyyy) reported a 12 % accuracy gain on the MIMIC-CXR test set [12]."

When citing one's own prior work, name the cohort or dataset explicitly to enable cross-paper retrieval.

10.4 Explicit challenge statement

Beyond the 2.5 limitations list, include a single-paragraph "Why this is hard" statement near the start of Discussion:

"Building accurate [task] for [modality/anatomy] is constrained by [data scarcity / label noise / dataset shift / regulatory uncertainty / interpretability]. Each of these has been documented [refs], and our results address [subset]."

LLM web-search systems quote challenge statements as authoritative summaries of field state (worked case: references/case_studies/kjr_mllm_2025.md).

Section 11 — First-Mover Timing and Citation-Graph Density

Rules 11.1 (topic-peak detection), 11.2 (editorial-board leverage), and 11.5 (multi-disciplinary author roster) are in ${CLAUDE_SKILL_DIR}/references/pre_draft_strategy.md; read it at pre-draft.

11.3 PMC-auto-deposit journal preference

OA journals that auto-deposit to PubMed Central reach LLM crawlers within 4–6 weeks of publication; non-PMC OA journals can take 3–6 months. When all else is equal, prefer a PMC-auto-deposit journal. Radiology/medical-AI examples (verify per submission; policies change):

Korean Journal of Radiology (KJR) — auto-deposit confirmed.
Lancet Digital Health — author-funded green OA, PMC-eligible after embargo.
Radiology and Radiology: AI — selected articles auto-deposit.
npj Digital Medicine — auto-deposit (Nature OA).
JAMIA — author-funded OA route.
JMIR — auto-deposit (PMC-indexed).

11.4 Citation-graph anchor strategy

Anchor the Discussion in 5–10 high-visibility prior works that LLM training corpora already index well, so the paper is retrieved when users query those works.

Identify seminal references via Semantic Scholar's "Highly Influential Citations" filter for the topic.
Cite them with semantic predicates (Section 10.3), not as bare lists.
Mix recent preprints (currency) with 2018–2022 seminal papers (graph anchoring); a 2024–2025-only citation profile has low LLM retrieval weight because of corpus cutoffs.

Section 12 — Cross-Platform Launch Sequencing

At post-acceptance / post-publication, read ${CLAUDE_SKILL_DIR}/references/launch_sequencing.md for the Day 0 → Month 1 sequence (rules 12.1–12.5).

External References

GEO: Generative Engine Optimization — Aggarwal et al., KDD 2024, arXiv:2311.09735.
LLM medical citation support — Wu et al., Nat Commun 2025, doi:10.1038/s41467-025-58551-6.
LLM citation bias — Algaba et al., 2024, arXiv:2405.15739.
ExpertQA attribution — Malaviya et al., 2024, arXiv:2309.07852.
TRIPOD+AI — Collins et al., BMJ 2024. EQUATOR Network.
CLAIM 2024 — Tejani et al., Radiology: AI 2024, doi:10.1148/ryai.240300.
STARD-AI — Sounderajah et al., Nat Med 2025, doi:10.1038/s41591-025-03953-8.
TRIPOD-LLM — Gallifant et al., Nat Med 2024, doi:10.1038/s41591-024-03425-5.
DECIDE-AI — Vasey et al., Nat Med 2022, doi:10.1038/s41591-022-01772-9.
Title, abstract, keywords guide — Royal Society Proc B 2024, doi:10.1098/rspb.2024.1222.
GitHub repository citation advantage — Kang et al., Inf Process Manag 2023, doi:10.1016/j.ipm.2023.103477.
Semantic Scholar Open Data Platform — Kinney et al., arXiv:2301.10140.

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Academic AIO Skill — Medical AI Paper Visibility for AI Search Engines

Communication Rules

Section 1 — Title and Abstract Optimization

1.1 Title three-slot rule

1.2 Structured abstract

1.3 Opening and closing sentences

1.4 Taxonomy line

1.5 Quantified claim

1.6 Reporting-guideline anchor

1.7 Keyword, MeSH, and RadLex coverage

Section 2 — Manuscript-Level AIO

2.1 Summary box

2.2 Declarative section headings

2.3 Numeric claim compression

2.4 Reproducibility block

2.4a Citing an AI-assisted tool by use-class

2.5 Limitations enumeration

2.6 Standalone figure captions

Section 3 — Preprint, Channel, and Indexing Strategy

3.1 Preprint versus fast-track

3.2 Journal preprint policy

3.3 Indexing time-lag

3.4 Open-access choice

3.5 Post-acceptance channel checklist

Section 4 — Review-Paper Strategy

Section 5 — GitHub, CITATION.cff, Zenodo, Hugging Face

Section 6 — Authority and E-E-A-T Signals

Section 7 — LLM-Citation Fabrication Defense

Section 9 — Per-Project Application and Pipeline Integration

Integration with other skills

Section 10 — Q&A and Entity-Extraction Optimization

10.1 Four-question Q&A block (Discussion or Appendix)

10.2 Glossary block with entity IDs

10.3 Inline citation anchor text

10.4 Explicit challenge statement

Section 11 — First-Mover Timing and Citation-Graph Density

11.3 PMC-auto-deposit journal preference

11.4 Citation-graph anchor strategy

Section 12 — Cross-Platform Launch Sequencing

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