| name | paper-search |
| description | Find a small list of academic papers on demand — "给我 N 篇目标检测领域
顶刊 / 顶会 / SOTA 论文 / 综述" — using the multi-source chain
arxiv API → Semantic Scholar → web_search. Distinct from
`daily-paper-digest` (scheduled daily delivery) and `arxiv` (single-day
category dump): this one is the on-demand "find me N papers about X"
surface that the user usually triggers in chat.
|
| version | 0.1.0 |
| tags | ["research","papers","on-demand"] |
| metadata | {"jiharn":{"category":"research","triggers":["顶刊","顶会","几篇论文","几个论文","篇论文","找.*论文","推荐.*论文","SOTA","state-of-the-art","最新进展","综述","survey","对比.*论文","benchmark"],"capabilities":["web_query","read_url"],"related_skills":["arxiv","daily-paper-digest","web-summary"]}} |
paper-search
Trigger
Use this skill when the user asks for a small ad-hoc list of academic
papers, typically:
- "给我目标检测领域的 5 篇顶刊论文，要最新的"
- "AI agent 方向最近半年的 SOTA 论文有哪些"
- "推荐 3 篇关于 RAG 评估的综述"
- "Find me 5 recent papers on diffusion video generation"
Do NOT use this skill for:
- Scheduled daily / weekly digests →
daily-paper-digest.
- "Today's cs.AI listing on arxiv" (category dump, not topic-targeted)
→
arxiv.
- Reading / summarising a single paper the user already linked →
web-summary + read_url.
Inputs
Resolve from the user message before fetching:
topic — the research area in plain words ("目标检测 / object
detection", "RAG evaluation"). Required; ask one short follow-up if
truly ambiguous.count — how many papers, default 5, hard cap 8.recency — "最新 / latest" → last 12 months; "近年 / recent" → last
3 years; explicit window like "2024 以来" → respect verbatim.kind — empirical / SOTA / survey / benchmark / methods. Default:
whichever the user phrased; if silent, prefer SOTA + 1 survey when
both are obviously useful.
Steps
-
Source priority (cheapest + highest signal first):
- arxiv API —
https://export.arxiv.org/api/query?search_query=ti:<topic>+OR+abs:<topic>&start=0&max_results=20&sortBy=submittedDate&sortOrder=descending
via read_url. Returns Atom XML; parse for title / authors /
summary / arxiv id. Best for ML / CS / EE.
- Semantic Scholar Graph API —
https://api.semanticscholar.org/graph/v1/paper/search?query=<topic>&limit=20&fields=title,authors,year,venue,citationCount,abstract,url
via read_url. Better venue + citation metadata than arxiv. If
it returns 429, wait and retry once with a 60s gap; then move
on without it.
- web_search — only as a last fallback when arxiv + S2 both
yielded fewer than
count candidates. Query template:
"<topic>" survey 2024 site:arxiv.org then site:openreview.net.
-
Filter + rank:
- Drop preprints whose abstract is empty / "Withdrawn".
- Prefer top-tier venues when the user asked for 顶刊 / 顶会 (CV:
CVPR / ICCV / ECCV / TPAMI; ML: NeurIPS / ICML / ICLR; NLP: ACL
/ EMNLP / NAACL; speech: INTERSPEECH; systems: SOSP / OSDI;
security: S&P / USENIX). Surface arxiv-only entries only when
no venued match exists.
- Sort: recency desc → citation desc → venue tier.
- Dedupe by arxiv id / DOI / canonical URL.
-
Render (one bullet per paper, newest first):
- Title (in original language).
- Primary authors (max 3 then
et al.).
- Year + venue if known.
- One-line plain-language takeaway (≤ 80 chars).
- Source URL (arxiv abs preferred over PDF).
-
Tool budget: aim for ≤ 3 tool calls total. List-task
adaptive budget already grants up to 10 — reserve the headroom
for retries, not for browsing every candidate.
Verification
- Every paper has a real source URL.
- Title / authors / year are never invented; if unknown, omit the
field and say so plainly.
- If the result set is thin (<
count solid hits), say "找到 N 篇
够稳的，剩下的来源都不太可信，先给你这 N 条" — do not pad.
- Do NOT ask the user to "确认是否保存到知识库" unless they signalled
long-term storage intent. Default is in-chat output only.
Pitfalls
- Semantic Scholar 429 is common; honour the
Retry-After header
and degrade silently to arxiv-only.
- DDG / Bing captcha will trigger the provider chain in
web_search; if all 3 web providers fail, fall back to whatever
arxiv + S2 produced even if < count.
- "顶刊" sometimes means journal (TPAMI / TMLR) and "顶会" means
conference (CVPR / NeurIPS); when the user uses both,
include at least one of each.
- Don't translate paper titles — keep the originals so the user can
search them as-is.
