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

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

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

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.

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

deepxiv fa read talent ID [ID...] [-l brief|detail|full] [-e EXTRA] [-p k=v]... [-f json|md|html]

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

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.

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.