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medical-evidence-grading · 医学证据等级排序与编排层(L2) · ethanyoq | DecimalAI

Scienceby ethanyoq

medical-evidence-grading · 医学证据等级排序与编排层(L2)

Medical evidence grading orchestrator. 当用户需要评估医学文献的证据强度、比较多篇论文的质量、查找某疾病的高等级证据(指南或 RCT)、或验证临床声明的证据支持度时,调用本 skill。整合 6 个原子 skill 的检索结果,按证据金字塔自动分级为 A/B/C/D。优先使用 BioMCP fast-path(~3-5s),无 BioMCP 时回退 slow-path(~15-25s)。

Never made a case worseMaintained

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%95%of test cases passed +59 pts

Without skill8 of 22

With skill21 of 22

13 fixed8 passed either way1 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v1 · latest is v321 comparable

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task在并发检索 PubMed、EuropePMC 及 ClinicalTrials 时，同一篇研究同时返回了 PMID: 32001002、PMCID: PMC7010001 和 DOI: 10.1056/NEJMoa1912345。请说明合成内部唯一键（evidence_id）的优先级规则。

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-medical-evidence-grading
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-medical-evidence-grading")
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-medical-evidence-grading

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-medical-evidence-grading/SKILL.md https://app.decimal.ai/s/ethanyoq-medical-evidence-grading/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/ethanyoq-medical-evidence-grading/SKILL.md

This gets SKILL.md only. The skill's 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/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-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/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-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading")
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-medical-evidence-grading/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/ethanyoq-medical-evidence-grading/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 4 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-medical-evidence-grading

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-medical-evidence-grading/SKILL.md https://app.decimal.ai/s/ethanyoq-medical-evidence-grading/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/ethanyoq-medical-evidence-grading/SKILL.md

This gets SKILL.md only. The skill's 4 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-medical-evidence-grading/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/ethanyoq-medical-evidence-grading.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3 (v1 tested 8/8/2026)
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

medical-evidence-grading · 医学证据等级排序与编排层(L2)

一句话定义

对底层 6 个原子 skill 召回的医学文献做"证据金字塔排序",输出带 GRADE A/B/C/D 评级的文献集与汇总报告;同时充当智能调度器,根据问题类型(疗效/诊断/病因/预后/不良反应/预测/指南/SR)自动选择最佳原子 skill 与 PubMed Clinical Queries filter。

Iron Law(不可违反)

GRADE 评级前必须调用 pubmed-eutils.efetch_pubmed() 拿真实 publication_type。不可仅凭 title/abstract 关键词推断。

理由:title 含"randomized"未必是 RCT(可能是综述讨论 RCT);只有 NLM MeSH 索引员人工标注的 publication_type 才是权威依据。仅当 efetch 返回为空(发表 < 6 个月新文献)才允许启发式推断,且必须给 grade 加 _inferred 后缀。

完整规则见 references/grade-rules.md。

何时调用(Auto-trigger)

调用本 skill (✅):

"评估这个临床声明的证据强度" → ✅ cross_validate_evidence(claim=...)
"找血液 IFI 最新指南级证据" → ✅ evidence_search(query=..., target_grade='guideline_only')
"对比这些 PMID 的 GRADE 等级" → ✅ grade_pmid_list(pmid_list=[...])
"我要给附录 C 重新分级" → ✅ grade_pmid_list + evidence_summary
"venetoclax AML 高质量 RCT" → ✅ evidence_search(target_grade='rct_or_above')

不调用本 skill (❌,直接调原子 skill):

"检索 PubMed" → ❌ 直接调 pubmed-eutils
"找 NCT 试验" → ❌ 直接调 clinical-trials-v2
"拿全文段落" → ❌ 直接调 bioc-fulltext-fetch
"全库批量分析" → ❌ 直接调 aact-bulk-trials

与其他 skill 协作矩阵(关键 · 编排核心)

协作方向	Skill	调用的具体函数	用途
上游调用方	`ifi-market-sizing-skill`	(调本 skill)	市场规模研究的"数据回补"
上游调用方	`disease-market-sizing-orchestration`	(调本 skill)	调研启动阶段的高等级证据搜集
下游被调 1	`pubmed-eutils`	`.esearch_pubmed()` / `.efetch_pubmed()` / `.pubmed_clinical_queries()`	拿 PMID + publication_type
下游被调 2	`europepmc-search`	`.search_articles()` / `.find_similar_articles()`	EuropePMC + open-access 链接
下游被调 3	`clinical-trials-v2`	`.search_studies()`	NCT + phase + enrollment
下游被调 4	`aact-bulk-trials`	`.bulk_disease_landscape()`	全库历史试验(罕见病/纵向)
下游被调 5	`bioc-fulltext-fetch`	`.to_rag_chunks()`	抽样本量(只对疑似 RCT/Cohort)
下游被调 6	`pubtator-entity-search`	`.search_by_relation()`	给文献打疾病/药物 entity tag
下游接收方	`evidence-appendix-sync`	(接受本 skill 输出)	报告附录 C 同步

              用户 / 上游 skill (ifi-market-sizing-skill, disease-market-sizing-orchestration)
                              │
                              ▼
                  [medical-evidence-grading]
                       ↙             ↘
            BioMCP fast-path      自建 slow-path
                  │                       │
            (单次 fan-out)        并发调 6 原子 skill
                  ↓                       ↓
                统一去重 (PMID > PMCID > DOI)
                              ↓
                publication_type 解析 (efetch · Iron Law)
                              ↓
                GRADE 评级 (A/B/C/D/excluded · 见 grade-rules.md)
                              ↓
                  evidence-appendix-sync (报告附录 C)

BioMCP fast-path vs 自建 slow-path 双模式

Fast-path: BioMCP MCP server (优先)

genomoncology/biomcp 一次性 fan-out PubMed + EuropePMC + ClinicalTrials + PubTator。

Slow-path: 自建 fan-out (兜底)

当 BioMCP 不可用,并发调 pubmed-eutils.esearch_pubmed() + europepmc-search.search_articles() + clinical-trials-v2.search_studies(),然后 pubmed-eutils.efetch_pubmed() 拿 publication_type,可选调 bioc-fulltext-fetch.to_rag_chunks() 抽样本量。

完整检测/配置/兜底逻辑见 references/biomcp-integration.md。

性能对比:

模式	100 条文献延迟	API 调用数	缓存命中后
Fast-path (BioMCP)	~3-5s	1	<500ms
Slow-path (自建)	~15-25s	6-10	<500ms

5 核心函数签名(跨平台稳定)

# ============== 1. 主入口:综合证据搜索 ==============
def evidence_search(
    query: str,
    target_grade: str = "all",          # 'guideline_only' | 'rct_or_above' | 'all'
    max_results: int = 200,
    include_preprints: bool = False,
    include_trials: bool = True,
    date_range: str = "10years",        # 'all' | '5years' | '10years' | '2020:2025'
    rare_disease_mode: bool = False,    # 罕见病阈值放宽,见 grade-rules.md
    use_biomcp_if_available: bool = True,
) -> dict:
    """
    返回 {total, by_grade, fast_path_used, cache_hit, elapsed_seconds, results: [{evidence_id, pmid, pmcid, doi, title, authors, journal, year, publication_types, sample_size, is_preprint, grade, grade_rationale, abstract, source}]}
    """

# ============== 2. 给现有 PMID 列表打分 ==============
def grade_pmid_list(
    pmid_list: list[str],
    fetch_fulltext_for_sample_size: bool = False,
) -> list[dict]:
    """
    输入 ['38234567', ...] → 输出每条带 grade / grade_rationale 的字典。
    内部:批量 pubmed-eutils.efetch_pubmed() → 解析 publication_type → 应用 grade-rules.md。
    fetch_fulltext_for_sample_size=True 时,对疑似 RCT/Cohort 调 bioc-fulltext-fetch.to_rag_chunks() 抽 n。
    """

# ============== 3. 推荐检索策略 ==============
def recommend_search_strategy(
    question_type: str,       # 'therapy' | 'diagnosis' | 'etiology' | 'prognosis' | 'harm' | 'prediction' | 'guideline' | 'sr'
    user_query: str = "",
) -> dict:
    """
    返回 {clinical_queries_filter, pubmed_query_template, europepmc_query_template,
          expected_grade_distribution, recommended_skill, tips[]}.
    完整 8 类映射见 references/clinical-queries-mapping.md。
    """

# ============== 4. 反向证据交叉验证 ==============
def cross_validate_evidence(
    claim: str,               # 例: "Posaconazole 预防 IFI 比 fluconazole 更有效"
    top_k: int = 10,
    min_grade: str = "B",
) -> dict:
    """
    NLP 拆解 claim → PICO → 反向搜索高等级证据。
    返回 {claim, pico, supporting[], contradicting[], verdict, confidence}.
    verdict ∈ {supported, contradicted, mixed, insufficient}.
    """

# ============== 5. 证据汇总报告 ==============
def evidence_summary(
    graded_list: list[dict],
    output_format: str = "markdown",   # 'markdown' | 'html' | 'json'
    output_path: str | None = None,    # 提供则写 evidence/_summary.md
) -> str:
    """
    生成等级分布表 + Top 5 grade-A 摘要表 + 关键发现 + 引用建议 + 缺口提示。
    """

Clinical Queries 8 类问题速览

question_type	优先 grade	主调 skill
therapy	A-B	pubmed-eutils.pubmed_clinical_queries(scope='therapy/narrow')
diagnosis	A-C	pubmed-eutils + europepmc-search
etiology	B-C	pubmed-eutils
prognosis	B-C	pubmed-eutils
harm	A-C	pubmed-eutils + clinical-trials-v2 (AE 表)
prediction	A-B	pubmed-eutils
guideline	A only	pubmed-eutils.pubmed_clinical_queries(scope='guidelines')
sr	A only	pubmed-eutils + europepmc-search

完整查询模板与罕见病调整见 references/clinical-queries-mapping.md。

失败模式速查 (10 条)

症状	可能原因	处理方式
BioMCP 检测到但调用失败	MCP server 配置存在但未启动/版本不兼容	静默回退 slow-path,日志告警,不阻塞
BioMCP 故障 fall-back 至 slow-path 但 NCBI 也限流	双路连续失败	加 jitter 退避,3 次后切 europepmc-search 单源,grade 标 `_partial`
GRADE 自动评级 publication_type 缺失	文献 < 6 个月未 MeSH 索引	启发式推断(见 grade-rules.md),grade 加 `_inferred` 后缀,警告用户人工复核
样本量提取失败(efetch 摘要不含 n)	全文不可访问 / 摘要写法不规范	跳过 n 校验,grade 按 publication_type 默认值,不应用 n 阈值降权
重复 PMID 跨原子 skill 未去重	BioMCP 已去重但回退到 slow-path 未去重	强制 `evidence_id = "PMID:" + pmid` 三段优先级(PMID > PMCID > DOI)再去重
证据矛盾(同一 claim 高等级证据正反皆有)	真实存在的临床争议	`cross_validate_evidence` 返回 `verdict='mixed'`,supporting 与 contradicting 并列展示,不强行下结论
缓存命中但 GRADE 规则更新过	grade-rules.md 升级未 bump 版本	`grade_rule_version` 字段不匹配视为 miss,自动重算并刷新缓存
evidence/ 文件夹组织规范冲突	下游 evidence-appendix-sync 期望 grade-A/B/C/D 子目录	严格按 `grade-A/PMID-XXX_AuthorYear.json` 命名,变动需双方同步升级
同一研究多 PMID(预印本+正式版)	medRxiv → 期刊	优先保留期刊版,预印本标记 `superseded_by`
中文期刊文献缺失	NCBI 不索引部分中文期刊	提示用户用 CNKI / 万方补充,本 skill 不覆盖中文数据库

完整缓存策略见 references/cache-schema.md。

evidence/ 文件夹组织规范

下游 evidence-appendix-sync 期望本 skill 产出:

evidence/
├── grade-A/
│   ├── PMID-32786187_DiNardo-NEJM-2020.json
│   └── PMID-32786187_DiNardo-NEJM-2020_abs.txt
├── grade-B/  grade-C/  grade-D/  excluded/
├── _summary.md          # evidence_summary() 产出
├── _index.csv           # 全部文献索引(便于附录 C 引用)
└── _query_log.jsonl     # 检索日志(可复现)

每条 *.json 含 {evidence_id, grade, grade_rationale, citation_apa, citation_chinese, metadata, abstract, fetched_at, source_chain}。

跨疾病移植清单

本 skill 与具体疾病无关,移植到肺癌/糖尿病等只需:

确认底层 6 原子 skill 已安装(/skills 列表查看)
涉及罕见病时 evidence_search(rare_disease_mode=True) 自动放宽阈值(见 grade-rules.md)
涉及外科器械时,补充 Comparative Effectiveness Research 类型识别
中文医学领域加 CNKI / 万方补充检索(本 skill 不覆盖,建议外挂)

跨平台兼容(无锁)

平台	兼容性	说明
Claude Code	原生	SKILL.md 自动加载,Skill 工具直接调用
Codex (CLI)	兼容	复制到 `$CODEX_HOME/skills/medical-evidence-grading/`,通过 skill-installer
Gemini CLI	兼容	作为 prompt template 使用,函数签名需手工实现
Cursor / Continue	部分	提取 GRADE 规则表(见 grade-rules.md)作为 system prompt

关键不变量(确保跨平台一致):

函数签名稳定(5 个核心函数不重命名)
GRADE 规则用 Markdown 表格表达(任何 LLM 可解析)
缓存路径 ~/.claude/skills/medical-evidence-grading/cache.sqlite (Path.home() 自动适配)
BioMCP 检测三路:~/.claude/mcp.json / ~/.codex/config.toml / BIOMCP_ENDPOINT 环境变量

References (Progressive Disclosure)

主文件保持精简;深度细节按需加载:

references/grade-rules.md — GRADE 评级完整规则表 + 降权升权信号 + 启发式推断 + 跨疾病阈值
references/clinical-queries-mapping.md — Clinical Queries 8 类问题映射 + 多类型并行 + 罕见病调整 + 中国本土补充
references/biomcp-integration.md — BioMCP fast-path 检测三路 + fall-back 触发条件 + slow-path 自建 fan-out + 跨平台配置
references/cache-schema.md — SQLite cache 表结构 + TTL 策略 + 命中检测 + 维护命令

调用示例

# 示例 1: 找血液 IFI 最新指南
result = evidence_search(
    query="invasive fungal infection prophylaxis hematology",
    target_grade="guideline_only",
    max_results=20,
    date_range="5years",
)
# 返回 ECIL / IDSA / NCCN 等指南

# 示例 2: 给已有 PMID 列表打分
graded = grade_pmid_list(
    pmid_list=["32786187", "37123456", "38234567"],
    fetch_fulltext_for_sample_size=True,
)

# 示例 3: 反向验证临床声明
verdict = cross_validate_evidence(
    claim="Posaconazole 预防 IFI 比 fluconazole 更有效",
    top_k=10,
    min_grade="B",
)

版本与依赖

版本: 1.1.0 (P0.2 阶段 · references 拆分版)
GRADE 规则版本: v1.0.0 (改动需 bump,见 cache-schema.md)
依赖原子 skill: pubmed-eutils ≥1.0, europepmc-search ≥1.0, clinical-trials-v2 ≥2.0, aact-bulk-trials ≥1.0, bioc-fulltext-fetch ≥1.0, pubtator-entity-search ≥1.0
可选 MCP: biomcp (genomoncology/biomcp,推荐安装以启用 fast-path)
更新日志:
- v1.1.0 (2026-04-25): references/ 拆分(grade-rules / clinical-queries-mapping / biomcp-integration / cache-schema),主文件 500 → ~370 行,新增 Iron Law / Auto-trigger 5 prompt / 函数级原子 skill 引用
- v1.0.0 (2026-04-25): 初始版本

About this skill

Automatic

Clinical / Healthcare pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/24/2026

View 15 scanner checks

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

ethanyoq / skill-hub

skills/06-data-search/medical-evidence-grading/SKILL.md

View on GitHub ↗

License MITImported 10/24/2026Version v3Bundled 4 files

Illustrative Want to see what ethanyoq/medical-evidence-grading would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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What it changes

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Use this skill

medical-evidence-grading · 医学证据等级排序与编排层(L2)

一句话定义

Iron Law(不可违反)

何时调用(Auto-trigger)

与其他 skill 协作矩阵(关键 · 编排核心)

BioMCP fast-path vs 自建 slow-path 双模式

Fast-path: BioMCP MCP server (优先)

Slow-path: 自建 fan-out (兜底)

5 核心函数签名(跨平台稳定)

Clinical Queries 8 类问题速览

失败模式速查 (10 条)

evidence/ 文件夹组织规范

跨疾病移植清单

跨平台兼容(无锁)

References (Progressive Disclosure)

调用示例

版本与依赖

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