luwill/research-skills

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# 端点。内置模型可省略；其它厂商必填，例如： #   https://api.moonshot.cn/v1        （Kimi）

#   https://api.moonshot.cn/v1        （Kimi）

#   https://dashscope.aliyuncs.com/compatible-mode/v1  （Qwen）

#   https://openrouter.ai/api/v1      （OpenRouter）

#   http://localhost:11434/v1         （本地 Ollama）

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需要 [uv](https://docs.astral.sh/uv/) 与 Python ≥ 3.11。

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curl -sI -o /dev/null -w "%{http_code}\n" -L "https://doi.org/<doi>"   # 200 才许写进协议

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#!/usr/bin/env bash

if ! command -v uv >/dev/null; then   echo "ERROR: uv not found. Install it first:"   echo "  curl -LsSf https://astral.sh/uv/install.sh | sh"

echo "ERROR: uv not found. Install it first:"   echo "  curl -LsSf https://astral.sh/uv/install.sh | sh"   echo "  (or: brew install uv)"

echo "  curl -LsSf https://astral.sh/uv/install.sh | sh"

echo "  curl -LsSf https://astral.sh/uv/install.sh | sh"

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**问题**：arXiv API 对 litsearch 的全部 11 条检索式返回 HTTP 406，而 curl 手打同一条

# ⚠<U+FE0F> 每个 DOI 必须先解析确认存在，再写进来。 #    编造的 DOI 会让你看到 8/9 的召回，然后花半天去查一个根本不存在的漏检。 #    验证：curl -sI -o /dev/null -w "%{http_code}\n" -L "https://doi.org/<doi>"

#    编造的 DOI 会让你看到 8/9 的召回，然后花半天去查一个根本不存在的漏检。 #    验证：curl -sI -o /dev/null -w "%{http_code}\n" -L "https://doi.org/<doi>" #    **不要用 api.crossref.org 验证**：arXiv 的 10.48550/arXiv.* 注册在 DataCite，

#    验证：curl -sI -o /dev/null -w "%{http_code}\n" -L "https://doi.org/<doi>"

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DOI_RESOLVER = "https://doi.org/"

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UNPAYWALL = "https://api.unpaywall.org/v2"

EUROPEPMC = "https://www.ebi.ac.uk/europepmc/webservices/rest/search"

CROSSREF = "https://api.crossref.org/works"

return [             FullTextLocation(                 url=f"https://arxiv.org/pdf/{arxiv_id}",

FullTextLocation(                 url=f"https://arxiv.org/pdf/{arxiv_id}",                 host="arxiv",

url=f"https://arxiv.org/pdf/{arxiv_id}",

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JEV_URL = "https://api.typesafe.ai/v1/systemone"

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if rate_per_second <= 0:         raise ValueError(f"限速必须为正，收到 {rate_per_second}")     raw = httpx.AsyncClient(timeout=JEV_TIMEOUT_SECONDS)

raise ValueError(f"限速必须为正，收到 {rate_per_second}")     raw = httpx.AsyncClient(timeout=JEV_TIMEOUT_SECONDS)     http = HttpClient(

raw = httpx.AsyncClient(timeout=JEV_TIMEOUT_SECONDS)

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base_url="https://api.deepseek.com/beta",

"deepseek-v4-flash": Provider(         model="deepseek-v4-flash",         base_url="https://api.deepseek.com/beta",

model="deepseek-v4-flash",         base_url="https://api.deepseek.com/beta",         pricing=Pricing(input_hit=0.0028, input_miss=0.14, output=0.28),

base_url="https://api.deepseek.com/beta",

JEV_MODEL = "jev" JEV_BASE_URL = "https://api.typesafe.ai"

JEV_BASE_URL = "https://api.typesafe.ai"

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OPENALEX_WORKS = "https://api.openalex.org/works"

OPENALEX_SOURCES = "https://api.openalex.org/sources"

def _parse_work(payload: Mapping) -> WorkMetrics | None:     raw_doi = (payload.get("doi") or "").strip().lower()     doi = raw_doi.removeprefix("https://doi.org/") or None

raw_doi = (payload.get("doi") or "").strip().lower()     doi = raw_doi.removeprefix("https://doi.org/") or None     if not doi:

doi = raw_doi.removeprefix("https://doi.org/") or None

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size = batch_size or topic.screening.batch_size         for index, batch in enumerate(build_batches(subset, size)):             requests.append(

for index, batch in enumerate(build_batches(subset, size)):             requests.append(                 ScreeningRequest(

requests.append(

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ENDPOINT = "https://export.arxiv.org/api/query"

PAGE_SIZE = 100 ATOM = "{http://www.w3.org/2005/Atom}" ARXIV_NS = "{http://arxiv.org/schemas/atom}"

ATOM = "{http://www.w3.org/2005/Atom}"

ARXIV_NS = "{http://arxiv.org/schemas/atom}"

OPENSEARCH = "{http://a9.com/-/spec/opensearch/1.1/}"

async def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

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def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

truncated = False     try:         async for record in source.fetch(query, context):

try:         async for record in source.fetch(query, context):             record.found_by = [query.query_hash]

async for record in source.fetch(query, context):

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ENDPOINT = "https://api.crossref.org/works"

async def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

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BASE = "https://openaccess.thecvf.com"

async def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

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ENDPOINT = "https://www.ebi.ac.uk/europepmc/webservices/rest/search"

async def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

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ENDPOINT = "https://zenodo.org/api/records"

async def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

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ENDPOINT = "https://api.openalex.org/works"

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async def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

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ENDPOINT = "https://api2.openreview.net/notes/search"

PAGE_SIZE = 1000 FORUM_URL = "https://openreview.net/forum?id=" _DOI_RE = re.compile(r"(?i)\bdoi\s*=\s*[{\"']?\s*(10\.\d{4,9}/[^\s,}\"']+)")

FORUM_URL = "https://openreview.net/forum?id="

async def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

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ESEARCH = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"

EFETCH = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi"

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async def fetch(self, query: SourceQuery, context: SourceContext) -> AsyncIterator[Record]:

language=_text(article.find(".//Article/Language")) or None,             identifiers=identifiers,             urls={"landing": f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/"},

identifiers=identifiers,             urls={"landing": f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/"},             referenced_works=references,

urls={"landing": f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/"},

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transport = httpx.AsyncHTTPTransport(retries=0)

raw = httpx.AsyncClient(

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def stage3_context(stage3_topic, tmp_path) -> SourceContext:     client = HttpClient(         httpx.AsyncClient(timeout=5.0),

client = HttpClient(         httpx.AsyncClient(timeout=5.0),         rate_limiter=RateLimiter(min_interval=0.0),

httpx.AsyncClient(timeout=5.0),

<?xml version='1.0' encoding='UTF-8'?> <feed xmlns:opensearch="http://a9.com/-/spec/opensearch/1.1/" xmlns:arxiv="http://arxiv.org/schemas/atom" xmlns="http://www.w3.org/2005/Atom">

<feed xmlns:opensearch="http://a9.com/-/spec/opensearch/1.1/" xmlns:arxiv="http://arxiv.org/schemas/atom" xmlns="http://www.w3.org/2005/Atom">

<id>https://arxiv.org/api/GC37HgChik5h/vS+O1LCgD1MFfY</id>

<title>arXiv Query: search_query=(abs:"stroke lesion segmentation") AND submittedDate:"202007010000 TO 202707272359"&amp;id_list=&amp;start=0&amp;max_results=2</title>   <updated>2026-07-27T08:36:16Z</updated>   <link href="https://arxiv.or

<updated>2026-07-27T08:36:16Z</updated>   <link href="https://arxiv.org/api/query?search_query=abs:%22stroke+lesion+segmentation%22+AND+submittedDate:%22202007010000+TO+202707272359%22&amp;start=0&amp;max_results=2&amp;id_list=" type="appli

<link href="https://arxiv.org/api/query?search_query=abs:%22stroke+lesion+segmentation%22+AND+submittedDate:%22202007010000+TO+202707272359%22&amp;start=0&amp;max_results=2&amp;id_list=" type="application/atom+xml"/>

<opensearch:startIndex>0</opensearch:startIndex>   <entry>     <id>http://arxiv.org/abs/2007.03294v1</id>

<entry>     <id>http://arxiv.org/abs/2007.03294v1</id>     <title>Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks</title>

<id>http://arxiv.org/abs/2007.03294v1</id>

<title>Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks</title>     <updated>2020-07-07T09:19:23Z</updated>     <link href="https://arxiv.org

<updated>2020-07-07T09:19:23Z</updated>     <link href="https://arxiv.org/abs/2007.03294v1" rel="alternate" type="text/html"/>     <link href="https://arxiv.org/pdf/2007.03294v1" rel="related" type="application/pdf" title="pdf"/>

<link href="https://arxiv.org/abs/2007.03294v1" rel="alternate" type="text/html"/>

<link href="https://arxiv.org/pdf/2007.03294v1" rel="related" type="application/pdf" title="pdf"/>

<summary>Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP) images is important for accurate diagnosis of stroke in acute care units. However, it is challenged by low image contrast and resolution of the perfusion 

<category term="eess.IV" scheme="http://arxiv.org/schemas/atom"/>

<category term="cs.CV" scheme="http://arxiv.org/schemas/atom"/>

</author>     <arxiv:doi>10.1016/j.media.2020.101787</arxiv:doi>     <link rel="related" href="https://doi.org/10.1016/j.media.2020.101787" title="doi"/>

<arxiv:doi>10.1016/j.media.2020.101787</arxiv:doi>     <link rel="related" href="https://doi.org/10.1016/j.media.2020.101787" title="doi"/>   </entry>

<link rel="related" href="https://doi.org/10.1016/j.media.2020.101787" title="doi"/>

</entry>   <entry>     <id>http://arxiv.org/abs/2008.13109v2</id>

<entry>     <id>http://arxiv.org/abs/2008.13109v2</id>     <title>Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network</title>

<id>http://arxiv.org/abs/2008.13109v2</id>

<title>Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network</title>     <updated>2021-12-03T12:24:00Z</updated>     <link href="https://arxiv.org/abs/2008.13109v2" rel="alternate" type="text/html"/>

<updated>2021-12-03T12:24:00Z</updated>     <link href="https://arxiv.org/abs/2008.13109v2" rel="alternate" type="text/html"/>     <link href="https://arxiv.org/pdf/2008.13109v2" rel="related" type="application/pdf" title="pdf"/>

<link href="https://arxiv.org/abs/2008.13109v2" rel="alternate" type="text/html"/>

<link href="https://arxiv.org/pdf/2008.13109v2" rel="related" type="application/pdf" title="pdf"/>

<summary>The state-of-the-art deep learning methods have demonstrated impressive performance in segmentation tasks. However, the success of these methods depends on a large amount of manually labeled masks, which are expensive and time-cons

<category term="eess.IV" scheme="http://arxiv.org/schemas/atom"/>

<category term="cs.LG" scheme="http://arxiv.org/schemas/atom"/>

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"URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_60",

"link": [      {       "URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_12",

{       "URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_12",       "content-type": "unspecified",

"URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_12",

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{       "URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_45",       "content-type": "unspecified",

"URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_45",

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{       "URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_18",       "content-type": "unspecified",

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"URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_11",

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"URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_8",

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"URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_58",

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"URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_49",

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"URL": "https://link.springer.com/content/pdf/10.1007/978-3-031-16443-9_2",

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<div id="header_left"> <a href="http://cvpr2020.thecvf.com"><img src="img/cvpr2020_logo.png" width="175" border="0" alt="CVPR 2020"></a> <a href="http://www.cv-foundation.org/"><img src="img/cropped-cvf-s.jpg" width="175" height="112" borde

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<br><br>     <div id="abstract">     One-shot medical image segmentation (MIS) aims to cope with the expensive time-consuming and inherent human bias annotations. One prevalent method to address one-shot MIS is joint registration and segmen

<div id="abstract">     One-shot medical image segmentation (MIS) aims to cope with the expensive time-consuming and inherent human bias annotations. One prevalent method to address one-shot MIS is joint registration and segmentation (JRS) 

One-shot medical image segmentation (MIS) aims to cope with the expensive time-consuming and inherent human bias annotations. One prevalent method to address one-shot MIS is joint registration and segmentation (JRS) with a shared encoder wh

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