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Reference Check · o0000-code | DecimalAI

Researchby o0000-code

Reference Check

学术参考文献的端到端检查、验证与修复。解析原始文献列表，去重，按APA第七版(APA 7th edition)格式化，通过OpenAlex和Semantic Scholar数据库验证每条文献的准确性，自动修复DOI、年份、标题等问题，并生成人工审核清单和Word文档。

Never made a case worse

SkillScore

SkillScore73/100

Test results 73

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

32%73%of test cases passed +41 pts

Without skill7 of 22

With skill16 of 22

9 fixed7 passed either way6 still failing0 broke

Measured on gemini-3.6-flash8/3/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task在学术参考文献的人工审核清单（Checklist）中，审查项被划分为哪五类结构化分类？请列出这五类的具体名称。

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 o0000-code-academic-ref-check
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("o0000-code-academic-ref-check")
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 o0000-code-academic-ref-check

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/o0000-code-academic-ref-check/SKILL.md https://app.decimal.ai/s/o0000-code-academic-ref-check/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/o0000-code-academic-ref-check/SKILL.md

This gets SKILL.md only. The skill's 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/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/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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("o0000-code-academic-ref-check")
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/o0000-code-academic-ref-check/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/o0000-code-academic-ref-check/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 8 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 o0000-code-academic-ref-check

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/o0000-code-academic-ref-check/SKILL.md https://app.decimal.ai/s/o0000-code-academic-ref-check/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/o0000-code-academic-ref-check/SKILL.md

This gets SKILL.md only. The skill's 8 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/o0000-code-academic-ref-check/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/o0000-code-academic-ref-check.json

This gets SKILL.md only. The skill's 8 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/3/2026
Source: o0000-code/meta-analysis-skill ↗
License: NOASSERTION

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

Reference Check

学术参考文献的完整检查、验证、修复、格式化流程。从原始文献列表到格式规范、数据库验证通过、附带人工审核清单的终版文献输出。

核心规则

以下五条规则是整个流程的基石。违反任何一条都会导致下游产出错误，且错误难以被后续阶段发现。

1. 使用作者+年份指纹ID，不用序号

所有阶段引用文献时使用内容指纹（作者姓_年份_标题关键词），不使用位置序号（如中[3]、英[218]）。这是因为去重、修复、重排序都会改变文献位置，位置序号在第一次修改后就会指向错误的文献，而指纹ID与文献内容绑定，无论如何重排都保持有效。

指纹ID生成规则：

英文：{第一作者姓}_{年份}_{标题前3个实词}（跳过a/an/the/of/in/on/for/and/with/to）
中文：{第一作者姓}_{年份}_{标题前5个字}
示例：Harding_2025_Musical_neurodynamics、安心_2017_高海拔驻留时间对

2. 有数据库依据才能修复

学术引用必须精确——不能基于猜测修改文献内容。自动修复仅限于数据库置信度高且修复方向无歧义的场景（如DOI补充、明显年份错误）。涉及作者增删、标题实质性差异等情况，只报告不修改，留给用户判断。具体判断标准见 references/fix_decision_matrix.md。

3. 终版文件是唯一权威来源

人工审核清单（或任何面向用户的文档）中引用的"当前内容"必须从终版文献文件中逐字复制。中间阶段的验证报告只用于确定"哪些条目需要关注"，不用于提供条目的具体内容。这条规则存在的原因是：验证阶段记录的文献快照会在后续修复中过时，如果清单从旧快照中复制内容，会出现清单描述与终版文件完全不一致的严重错误。

4. 规则性检查和判断性评审分离

Sentence case、&号、DOI格式等规则性问题由全量扫描式SubAgent逐条检查，确保100%覆盖。期刊真实性、引用准确性等需要判断力的问题由领域专家评审。两者并行运行但职责不重叠。混合使用会导致规则性问题被专家式抽检遗漏（首次执行中58条sentence case问题被4位专家全部漏掉，就是这个教训）。

5. 清单是流水线的终端产物

人工审核清单必须在所有自动修复和专家评审完成后、作为最终阶段独立生成。过早生成会包含已被后续修复的问题，导致清单与终版文件不一致。

SubAgent 角色与操作指南

每个SubAgent加载本Skill后，根据自己被分配的角色，阅读对应的操作指南和reference文件。

解析器（Parser）

职责： 将原始文献文本解析为结构化数据，为每条文献分配指纹ID。

操作要点：

识别多行断裂的文献条目（标题和期刊名被分割成多行的情况），合并为完整条目
识别非标准引用格式（Nature风格、编号格式[2]、中文[J]标注等）
为每条文献生成指纹ID（规则见核心规则第1条）
提取结构化字段：作者、年份、标题、来源、卷期页码、DOI、文献类型、语言
标记已识别的格式问题（DOI格式错误、全角标点混用、注释残留等）

去重规则：

DOI完全相同 → 保留更完整的版本
标题相似度>90%且年份相同 → 保留更完整版本
预印本vs正式发表版 → 保留正式发表版

格式化器（Formatter）

职责： 将结构化文献数据按APA第七版格式输出。

Before formatting, read references/apa7_rules.md for the complete APA 7th edition formatting rules.

操作要点：

作者格式：姓, 名首字母.，1-20位全列，21+使用省略号规则
标题：sentence case（仅首字母和专有名词大写）
期刊名：title case + 斜体（Markdown中用*期刊名*标记）
DOI统一为https://doi.org/前缀格式
排序：中文文献按拼音在前，英文文献按字母在后
中文文献也使用&连接最后两位作者

验证器（Verifier）

职责： 通过学术数据库验证每条文献的准确性。

Before verifying, read references/verification_guide.md for the complete database query workflow and result classification rules.

核验主路径 = scripts/verify_http.py（OpenAlex + CrossRef 公开 HTTP API，无需 key）。 若运行环境未提供 semantic-scholar / openalex MCP server（常见默认情况），原"强制加载 SS/OpenAlex MCP"路径会空转。本 Skill 接受这一降级，核验改走 HTTP 脚本：

python3 scripts/verify_http.py --in refs.json --out-dir <dir>     # 结构化 JSON 输入
python3 scripts/verify_http.py --in-text refs.md --out-dir <dir>  # 原始文本/Markdown 引用列表（自解析）

产物：L11_ref_verify_report.md（人读，绿/黄/红/unverified 四态）+ L11_ref_verify_report.json（机读）。
退出码（可直接接 SP7 preflight 门）：有红→1 / 否则有 unverified→3 / 否则有黄→2 / 全绿→0。
铁律（不可破）：网络失败 / 查无匹配绝不伪装通过——不假绿。 一切"不确定"落黄或红，绝不落绿；网络失败标 unverified（渲染为黄，须重跑，不可当通过）。匹配只信自算 title 相似度，不信 API 的"有结果"/score/total-results。中文文献国际库未覆盖→黄（非幻觉，非红）。

MCP 通道（可选 · 仅当环境中存在 SS/OpenAlex MCP 时）： 若所在环境确实装有 semantic-scholar / openalex MCP server，可将其作为补充核验通道与 HTTP 脚本交叉印证（用 ToolSearch 加载 mcp__semantic-scholar__* / mcp__openalex__* 后调用，工具用法见 references/verification_guide.md）。这是条件分支，不是前置必需步骤——若环境不具备这些 MCP，直接走 HTTP 主路径即可，无需尝试加载。

验证策略： 主路径下由 verify_http.py 内部完成「DOI 优先 → 标题搜索 → 自算相似度判定」并按绿/黄/红/unverified 分类（见下方 ABCD 类对照）；MCP 在场时可对黄/红条目再交叉印证。两库（HTTP 或 MCP）都查不到才标记为 D 类。

结果分类：

类别	含义	后续处理
A	验证通过	无需处理
B	可自动修复（置信度高）	交给修复器
C	需人工判断	列入审核清单
D	未查到	根据文献类型决定是否列入清单

中文文献特殊处理： OpenAlex和Semantic Scholar对中文文献覆盖有限。中文文献查不到是正常的，不应被视为信息可能有误的信号，除非文献本身存在格式异常。

修复器（Fixer）

职责： 汇总验证结果，对B类条目执行自动修复。

Before fixing, read references/fix_decision_matrix.md for the auto-fix vs report-only decision rules.

操作要点：

仅修复B类中置信度为"高"的条目。中/低置信度一律转入C类报告
每次修复必须同时在修复操作记录中记录：指纹ID、修复类型、修复前值、修复后值、修复依据
修复完成后校验文献总数守恒（修复不应增删文献）
更新修复状态汇总表

典型可自动修复的场景： DOI补充/格式修正、明显年份错误（如2026→2025）、sentence case转换、期刊名PubMed注释去除、标点符号修正、排序修正。

典型仅报告的场景： 作者列表大幅变动、标题实质性差异、卷期页码大幅不一致、数据库未收录的文献。

规则扫描器（Rule Scanner）

职责： 对终版文献执行全量逐条规则检查，确保100%覆盖。

Before scanning, read references/apa7_rules.md for the complete checklist of rules to verify.

与专家评审的区别： 规则扫描器检查的是可程序化判断的格式规则（有明确的对/错标准），专家评审处理的是需要学术判断力的问题。

必须逐条扫描的规则（每条文献都检查）：

Sentence case（英文标题仅首字母和专有名词大写）
作者间的&号（中英文文献均需检查）
DOI格式（https://doi.org/前缀，无多余空格/重复前缀）
期刊名斜体标记（*期刊名*）
标点符号规范（半角、句末句号、逗号位置）
年份格式（括号内，逗号后）
排序正确性（中文拼音、英文字母）
21+作者省略号规则

领域专家（Domain Expert）

职责： 执行需要学术判断力的非规则性审查。

审查维度：

期刊真实性：期刊是否存在、是否为掠夺性期刊
引用准确性：文献是否与论文主题领域一致
内容合理性：年份、卷期、页码是否在合理范围内
特殊格式判断：会议论文、预印本等有多种合理格式的情况

不负责的事项： sentence case、&号等规则性检查（由规则扫描器负责）。

清单生成器（Checklist Generator）

职责： 生成面向用户的人工审核清单。这是整个流程中对准确性要求最高的环节。

Before generating, read references/checklist_spec.md for the complete checklist format specification, category definitions, and validation rules.

强制执行的生成流程：

读取终版文献文件，建立指纹ID→终版内容的完整映射表
读取验证总报告，提取C类和D类条目
读取修复状态汇总表，排除已修复的条目
读取专家评审报告，提取未修复的问题
合并得到"待审核项目池"
对每个待审核项目，通过指纹ID从步骤1的映射表中提取终版内容（不从验证报告复制）
按五类分类组织清单
执行一致性校验（使用scripts/validate_checklist.py）

五类分类：

必须处理（Must Fix）
数据库未覆盖（Not Found）
自动修正需复核（Auto-fixed, Please Verify）
信息补充建议（Suggested Additions）
仅供知晓（FYI）

Word转换器（Word Converter）

职责： 将终版Markdown文献文件转换为符合博士论文排版标准的Word文档。

使用 scripts/convert_refs.py 执行转换：

python scripts/convert_refs.py <input.md> [output.docx]

格式规范：

标题：黑体三号(16pt)居中加粗
条目：宋体+TNR五号(10.5pt)，固定行距20pt，悬挂缩进2字符
期刊名：斜体（通过*期刊名*标记自动识别）
页面：A4，页边距上3/下2.5/左3/右2.5cm

依赖： pip install python-docx

APA 第七版格式速查表

以下是最常用的10条规则，完整规则见 references/apa7_rules.md。

#	规则	正确示例
1	作者格式：姓, 名首字母.	`Zhang, L. M.`
2	多作者用`,`分隔，最后两位用`&`	`Li, A., Wang, B., & Chen, C.`
3	21+作者：前19位...最后1位	`Author, A., Author, B., ... Author, U.`
4	年份在作者后括号内	`Smith, J. (2023).`
5	文章标题sentence case	`Effects of music on cognitive development in children`
6	期刊名title case+斜体	`Journal of Experimental Psychology`
7	卷号斜体，期号不斜体括号内	`12(3), 45-67`
8	DOI用https://doi.org/前缀	`https://doi.org/10.1037/rev0000106`
9	书名斜体+sentence case	`Cognitive psychology: A student's handbook`
10	条目末尾无句号（如以DOI结尾）	DOI链接后不加句号

中文文献补充规则：

中文文献也使用&连接最后两位作者（不用"和"）
博士论文格式：作者. (年份). *标题* [博士学位论文, 院校名称]. 数据库名称.
中文期刊名不斜体

脚本使用指南

convert_refs.py — Markdown转Word

# 基本用法
python scripts/convert_refs.py input.md output.docx

# 默认输出（与输入同名.docx）
python scripts/convert_refs.py input.md

脚本自动处理：元数据跳过、分类标题识别、期刊名斜体渲染、方括号注释清理。

validate_checklist.py — 清单校验

python scripts/validate_checklist.py checklist.md final_refs.md

执行五项校验：

内容一致性：清单引用的文献内容与终版文件逐字匹配
分类准确性：D类标注与验证总报告一致
完整性：所有C类和未修复问题都已覆盖
编号连续性：每类内序号连续，总数与统计表一致
统计交叉验证：总文献数 = 无需审核数 + 需审核数

Performance Notes

验证阶段是耗时最长的环节。每批25-30条文献分配一个SubAgent，最多12个并行，每批使用双数据库（Semantic Scholar + OpenAlex）交叉验证
质量优先于速度：不跳过任何验证步骤，不因为数据库响应慢就放弃第二个数据库的查询
评审循环控制在1-2轮：通过规则扫描器+专家评审并行的首轮全覆盖设计，争取首轮发现90%+问题，减少迭代轮数
所有SubAgent必须使用Opus 4.6模型

变更记录

S7（2026-06-23，Lane C）：Verifier 角色整体重写为 HTTP 主路径（scripts/verify_http.py）/ MCP 在场时可选补充——运行环境无 SS/OpenAlex MCP，接受降级走 OpenAlex/CrossRef 公开 HTTP API。清除原强制加载 MCP 块中"环境中 MCP 一定可加载"的过度断言与"必须先加载 MCP 才能核验"的前置强制，并移除 S3 留下的本节待办标注。verification_guide.md §1 同步收口。

When it activates

Automatically, when a task matches. Use this skill whenever you need to: 参考文献检查, 参考文献格式调整, APA格式化, 文献去重, DOI验证, 参考文献修复, 参考文献整理, reference check, APA formatting, bibliography verification, DOI validation, citation formatting, or reference list cleanup.

About this skill

Automatic

Writing & Editing pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/1/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

o0000-code / meta-analysis-skill

skills/academic-ref-check/SKILL.md

View on GitHub ↗

License PolyForm-Noncommercial-1.0.0 AND CC-BY-NC-SA-4.0Imported 8/1/2026Version v1Bundled 8 files · scripts

Illustrative Want to see what o0000-code/academic-ref-check 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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Reference Check

核心规则

1. 使用作者+年份指纹ID，不用序号

2. 有数据库依据才能修复

3. 终版文件是唯一权威来源

4. 规则性检查和判断性评审分离

5. 清单是流水线的终端产物

SubAgent 角色与操作指南

解析器（Parser）

格式化器（Formatter）

验证器（Verifier）

修复器（Fixer）

规则扫描器（Rule Scanner）

领域专家（Domain Expert）

清单生成器（Checklist Generator）

Word转换器（Word Converter）

APA 第七版格式速查表

脚本使用指南

convert_refs.py — Markdown转Word

validate_checklist.py — 清单校验

Performance Notes

变更记录

When it activates

SkillSafety

Source

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