deepxiv/deepxiv_sdk/deepxiv-fa-talent

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Find and read AI researcher profiles with the deepxiv CLI: by name (Chinese, pinyin, English), organisation, role, area, location or h-index; education, career, top papers, coauthors. Use when asked who works on something, to build a list of researchers or students, or about a specific researcher.

Install

npx vskill@latest install deepxiv/deepxiv_sdk/deepxiv-fa-talent

SKILL.md

DeepXiv · Talent (talent)

AI researcher directory: 189k people, 160k LLM-researched profiles (education, career, representative papers, open source), Google Scholar metrics rendered live. One document per person; id is person_id.

Corpus: 191,238 documents, updated 2026-10-19.

Setup

bash
pip install -U deepxiv-sdk # deepxiv registers a token on first use

With a registered key (https://data.rag.ac.cn/register), search and read are fair use: 60/min, 5 at once, 5,000/day; on the auto-registered token they spend the deepxiv daily limit at the credits listed below. ask draws on the daily ask allowance (resets 00:00 UTC; a site-wide daily total can run out early at peak times), or 3 lifetime trial asks on the auto-registered token. On 429, back off for Retry-After seconds. Data goes to stdout; cost, quota and sources go to stderr. Exit 0 ok, 1 API error, 2 usage error. The general CLI guide is the deepxiv-cli skill.

Start here

bash
deepxiv fa search talent "Yann LeCun" -k 3 # a name: English, Chinese, pinyin or "Last, First"
deepxiv fa search talent "具身智能" -F org=清华大学 -F role=教授 -F "h_index>=30" -k 10 # Chinese institutions take Chinese values
deepxiv fa search talent "world model" -F career_stage=student -F location=北京,上海 -k 20 # location values are Chinese city names: 北京, 上海
deepxiv fa read talent 15023 3426 2813 -l brief # several cards in one call
deepxiv fa read talent 15023 -e network # coauthors
deepxiv fa resolve "Geoffrey Hinton" "Yann LeCun" # names → person_id, batch
deepxiv fa ask "who works on self-supervised learning at Mila" -d talent

Free samples: ids 7870; queries Federica Sarro, Jürgen Schmidhuber, 软件工程 实证研究.

Search

bash
deepxiv fa search talent "query" [-F FILTER]... [-k N] [--offset N] [--mode M] [--head] [--json]

Modes: hybrid, dense, bm25, name. Up to 100 hits. Cost 2 (with --head: 3). An empty query is rejected; use "*" to list by filters only. Filters combine with AND; an undeclared field exits 2.

filtertype-F syntaxmeaning
orgkeywordk=v, k=a,bCurrent organisation as stored: Chinese institutions under their Chinese name, e.g. 清华大学 (Tsinghua University); several allowed (past or current: advanced field orgs)
rolekeywordk=v, k=a,b, k~textTitle: the normalised titles match exactly; anything else (or {"text": …}) is a full-text match on the raw title Values: 教授, 副教授, 助理教授, 研究员, 博士后, 博士生, 硕士生, 工程师.
areakeywordk=v, k=a,bResearch area, e.g. embodied AI, RAG; several allowed
locationkeywordk=v, k=a,bLocation as stored: Chinese city names in Chinese, e.g. 北京 (Beijing), 上海 (Shanghai), 香港 (Hong Kong); several allowed. English city names do not match
min_h_indexintegerk=vh-index at least
active_sinceintegerk=vMost recent paper in or after this year (drops inactive people)

Advanced filters (see deepxiv fa spec talent): names, name_zh, name_en, aff_raw, orgs, orgs_current, role_norm, career_stage, areas, h_index, citations_all, citations_5y, i10_index, last_paper_year, profile_at, has_profile, has_scholar.

Read

bash
deepxiv fa read talent ID [ID...] [-l brief|detail|full] [-e EXTRA] [-p k=v]... [-f json|md|html]
levelcreditsreturns
brief1Card: name, affiliation, title, areas, h-index, citations, has_profile (what search hits carry as brief)
detail (default)3Everything most callers need in one call (the default, level=detail): card + bio, current organisations, education, career, top 5 papers, Scholar metrics, top 5 coauthors, advisors, and the full profile's section map (read one with level=full&section=<name>)
full10The full profile markdown (level=full; the contact section is omitted unless contact=1)
-e extracreditsreturns
scholar2Structured Google Scholar: metrics (all / 5y), citations by year, top papers, as_of, stale, source. A stale snapshot is rebuilt from cache for free when possible, otherwise queued for background refresh
scholar_live20Same as scholar but synchronously fetches the latest numbers from Google Scholar (≤10s). Subject to a per-account daily quota (talent:scholar_live) and a global crawl budget; if the refresh does not actually happen you get a 502 and the credits and quota back — use extra=scholar for the stored snapshot
papers2Representative papers: Scholar top papers + profile top_papers + OpenAlex works, deduplicated by title, sorted by citations
network2Relations: coauthors (with in-corpus person_id), advisors, students
sources1The profile's reference list: the [n] numbers used in the report, url, title, confidence, whether the page archived at research time is still readable (snapshot), and which facts it backs (backs: education / affiliation)
source2The archived page behind one reference (the snapshot taken at research time; no live fetch). n is the [n] in the report; raw=1 returns the original HTML
structured3Structured profile for section-by-section rendering: education / work with dates (verbatim strings plus parsed start_year / end_year / current), open_source, top_papers, links, bio_md, activities_md, tags, career_stage; every item carries source_ids numbered as in view=sources. No Scholar metrics (live ones are in view=scholar); email only with contact=1; people without a researched profile get 404 (not charged)

At -l full, -p section=NAME reads one section and -p characters=N the first N characters.

Facets

deepxiv fa facets talent FIELD lists the legal values and counts, free. Use it instead of guessing a spelling. Fields: orgs, orgs_current, areas, role_norm, career_stage, location, last_paper_year.

deepxiv fa resolve NAME [NAME...] checks a batch of names against the corpus and returns ids.

Ask

bash
deepxiv fa ask "question" -d talent [-e low|medium|high] [-p k=v]... [--no-stream] [--json]

Find or understand people: an agent searches the corpus and reads profiles, answering with [talent:<id> <name>] citations; no web access, says so when nobody matches

Always pass -d talent: automatic domain picking is still early.

-p paramtypemeaning
top_kintegercandidates per corpus search

What this corpus will and will not tell you

  • People with has_profile=false carry only name/affiliation-level data; level=full (and its sections) returns 404.
  • Profile content has a daily quota counted in distinct people read per day (talent:profiles; per-plan numbers are in the self-description's quotas): re-reading the same person is free, card / brief and search are not capped. Over the limit returns 429 quota_exceeded with used_today and resets_at. Contact us for a talent:bulk key for volume or offline use (it lifts the scholar_live quota too).
  • Common usage is in quickstart: search to look people up or filter them, read (default level=detail) to read one in a single call; other facets via extra=, a human-readable page via format=html; filters marked advanced are there when you need them.
  • Facet fields: org / role / area / location / active_since, plus orgs / career_stage (recomputed nightly).
  • POST /v1/talent/resolve {names:[…]} checks a batch of names (paper authors, a team page) against the corpus and returns person_ids.
  • Name lookup: when the query looks like a person's name (2–4 Chinese characters / 2–4 Latin words), people with that name come first (hit.match="name"); Chinese names are converted to pinyin and Hongjin / Hong Jin / Hong-Jin all match. The response carries name_match: exact (full name matches — a Latin name, or a Chinese name of 3+ characters matched directly) returns only those people; partial (e.g. matched only through pinyin) keeps the normal results after them. mode=name returns only people with that name (ordered by has_profile, then h-index).
  • Every [n] in a profile points at an entry in extra=sources; extra=source&n=3 returns the page archived when the profile was researched (text plus the fetch date). Only snapshot=text has readable text — none / blocked (the site refused the crawler) / html (raw page only, add raw=1 for the original HTML) return 404, which is not charged.
  • Scholar numbers inside profile.publications are crawl-time values; trust h_index / citations_* in level=brief / detail.
  • The closing 'online footprint & contact' section comes from public homepages.

Limits

  • Quota talent:profiles: How many distinct people's profile content you may read per day (profile / page / full / raw / head / preview / section / source). Re-reading the same person is free; card / brief and search are not capped. Contact us for a talent:bulk key if you need volume.
  • Quota talent:scholar_live: How many distinct people you may refresh live from Google Scholar per day (scholar_live). This is your share of the global crawl budget; counted per person, cache hits included, so it stays predictable.
  • Grant talent:bulk: Lifts talent's daily quotas (profile content and live Scholar refreshes). For callers with a bulk or licence agreement. Issued on request, not self-serve.

Citing

Cite records as [talent:7870 Federica Sarro]. Say plainly when the corpus has nothing.


Generated by scripts/gen_fa_skills.py from deepxiv fa spec talent --json on 2026-10-19. Field names and prices can change; deepxiv fa spec talent is the live source.

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