alphaxiv/openresearch/orx-lit-review

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Explain and compare scientific or technical concepts using original research evidence. Use before answering conceptual or architectural questions, research claims, literature reviews, or related-work requests, even when no paper, citation, or search is requested. Retrieve with relevant alphaXiv, OpenAlex, bioRxiv, and PubMed connectors; scale retrieval to the question.

Install

npx vskill@latest install alphaxiv/openresearch/orx-lit-review

SKILL.md

Literature retrieval

Use this skill for scientific explanations and comparisons, even without a named paper. Never delegate retrieval to a sub-agent.

Use enabled literature connectors appropriate to the topic. General web search is a fallback only when relevant connectors provide no useful evidence. Do not supplement successful retrieval with a web search for a familiar or preferred paper; use a focused connector query or orx paper. Opening a selected original PDF to extract evidence is source access; opening its abstract first is unnecessary.

Explain with visual evidence

  • Prefer explaining through original figures, tables, and diagrams. For

comparisons, choose visuals that cover the relevant alternatives; let the reader see the architecture, relationship, or result being explained.

  • Answer as a guided reading of those visuals, presenting them early. Keep

prose brief: what to notice, why it matters, and the caveats, grounded in contextual author quotations.

Visual evidence should replace standalone tutorials, generated comparison tables, and recitations of data or structure. Choose visuals for explanatory value, without a fixed image count. Text is the fallback when relevant sources contain no useful visual or extraction remains blocked; explain that gap. One unavailable figure is not a reason to omit other accessible visuals.

Each command performs one search against public endpoints and emits its structured JSON result. No login is required:

sh
orx discover keyword "<exact keyword query>"
orx discover embedding "<semantic description in the user's terms>"
orx discover openalex "<scholarly search query>"
orx discover biorxiv "<biology preprint query>"
orx discover pubmed "<biomedical query>"
  • keyword searches title, abstract, and full text. Results include the match

snippets that explain why each paper was retrieved. Use short exact terms: method names, acronyms, benchmarks, authors, or title phrases.

  • embedding searches titles and abstracts semantically, then reranks by

similarity and the requested priority. Use the user's actual question or a concise description of a genuinely missing facet.

  • openalex searches the cross-disciplinary OpenAlex scholarly graph. It is

especially useful for journal/conference papers, citation context, and work outside arXiv.

  • biorxiv searches OpenAlex's bioRxiv source index. bioRxiv has no comparable

native search API; the bioRxiv API is used later when reading a selected DOI.

  • pubmed searches PubMed's biomedical and life-science journal literature

through NCBI E-utilities, ranked by PubMed's Best Match. It accepts PubMed query syntax such as field tags ([ti], [au], [mh]). Result ids are PMIDs.

  • Every primitive returns the same JSON shape: source, self-routing id,

title, abstract, and publication date. alphaXiv results may include votes and full-text snippets; OpenAlex and bioRxiv results may include citations. PubMed results carry no votes or citations.

Date and ranking controls

Retrieval is not date-bounded unless you supply a bound. Add the same controls to any primitive when the question calls for them:

sh
orx discover keyword "<query>" --published-after 2024-01-01 --prioritize recency
orx discover embedding "<query>" --published-before 2012-01-01 --prioritize historical
orx discover openalex "<query>" --published-after 2024-01-01 --prioritize recency
orx discover biorxiv "<query>" --limit 20
orx discover pubmed "<query>" --published-after 2020-01-01 --prioritize recency
  • --published-after and --published-before are inclusive YYYY-MM-DD

bounds. Do not invent a cutoff merely to favour newer work.

  • Older or narrow --published-before embedding searches can return a thin or

empty candidate set because the upper bound is applied after vector retrieval. Report what comes back; do not treat an empty set as proof that no literature exists or retry the identical query and window.

  • --prioritize is default, recency, historical, or popular.
  • --limit can narrow alphaXiv output but cannot widen alphaXiv's fixed

server-side candidate pools. For OpenAlex, bioRxiv, and PubMed it also controls the requested pool size.

  • OpenAlex and bioRxiv implement these controls with OpenAlex publication-date

filters, then rerank the returned relevance pool by date or citations. Their ranking is best-effort and is not identical to alphaXiv's semantic, vote-aware ranking.

  • PubMed applies the window as a publication-date filter and reranks its

relevance pool by date for recency and historical. It has no citation counts, so popular keeps PubMed's relevance order.

  • Use recency for explicitly new/latest work. Use historical for seminal or

foundational work. Use popular only when the user asks about votes, popularity, or community standing.

Main-agent retrieval loop

Set up the retrieval query
  1. If using keyword retrieval, build focused terms using only wording from the

user or prior tool results. Never guess an acronym expansion. General-purpose padding reduces result quality.

  1. For semantic or scholarly-graph retrieval, build one short faithful question

in the user's terms rather than a padded reformulation.

  1. Estimate retrieval difficulty from 1–10. This controls a budget of complete

follow-up rounds: difficulty 1–3 gets 0 rounds, 4–7 gets 1, and 8–10 gets 2.

  1. Resolve one publication window and priority for the request. Every initial

and follow-up call must inherit those exact controls; never widen a window or change priority during the loop. Every returned candidate already satisfies that window, so rank what is available instead of lamenting well-known work that the user excluded.

Run and rank
  1. Choose the initial sources and strategies that fit the query. For arXiv-heavy

ML, CS, math, or physics questions, use alphaXiv keyword, embedding, or both according to whether exact full-text evidence, semantic coverage, or both are useful. Add OpenAlex for broader journal, conference, citation, or cross-disciplinary coverage. Use bioRxiv for biology and adjacent life-science preprints and PubMed for biomedical, clinical, and life-science journal literature, not as ritual calls for unrelated topics. When the corpus is genuinely ambiguous or interdisciplinary, query multiple relevant sources concurrently. If the initial round includes alphaXiv keyword and its terms mix other terms with one or more 2–10 character tokens that start with a letter, contain only letters, digits, or hyphens, and have at least two uppercase letters, concurrently run one additional keyword call whose query is exactly those acronym tokens joined by spaces and nothing else. This recovery call is part of the initial round.

  1. Treat initial calls independently: retain every successful result set when

another call fails. If none returns results and follow-up budget remains, use a round only when a focused recovery query is likely to work.

  1. Inspect and deduplicate every candidate. Match exact id first, then a DOI

or arXiv id visible in the metadata, then exact normalized title as a cross-source fallback. Prefer the alphaXiv representation of an arXiv duplicate because it supports full-text reading. bioRxiv is a subset of OpenAlex, and PubMed journal records largely overlap OpenAlex, so overlap between those calls is expected. Within each source, the API order already blends topical relevance with the requested priority:

  • With recency, freshness is already upranked and old accumulated votes

are damped. Reorder only for topical fit; do not exclude an older but much better match.

  • With popular, votes or citations dominate among topically plausible

results. Keep high-impact relevant papers, but drop off-topic ones.

  • Otherwise, topical relevance remains primary with freshness and votes

already nudging the order. Do not apply those preferences a second time.

  1. If the initial candidates provide solid topical coverage, stop immediately

and rank 5–15 IDs. Fast and slightly less complete is better than an exploratory search. Prefer fewer strong papers over padding.

  1. Otherwise, spend at most the difficulty-derived number of follow-up rounds.

One round targets one concrete missing acronym, method, benchmark, organization, title phrase, venue, or subtopic. Choose one or more sources based on the gap: alphaXiv keyword for exact/full-text evidence, alphaXiv embedding for a semantic arXiv angle, OpenAlex for broad scholarly or citation coverage, bioRxiv for recent biology preprints, and PubMed for biomedical or clinical journal literature. Later rounds do not need to query all sources. Calls for the same missing angle count together as one round. Never spend a round merely rephrasing an existing search. Re-evaluate after each round and stop as soon as coverage is sufficient. The budget is a hard cap, not a target.

  1. Drop each selected ID that did not appear in a successful initial or

follow-up result, retaining the surviving IDs in your chosen rank order. If no selected ID survives, fall back to the first 15 unique IDs in observation order, with initial results before follow-up results. Never invent or recall an ID.

Batch all facets into one broad retrieval loop and plan against a cap of two complete loops per user turn. If a genuinely distinct topic still forces a third or fourth loop, run it in shallow mode: initial searches only, with zero follow-up rounds. This degradation is a backstop, not permission to plan extra loops. Refuse a fifth loop and answer from the papers already found.

For a set-of-papers request such as “find papers,” “top papers,” “what is out there,” or “what should I read,” return the ranked discovery results and stop. Depth on individual papers is not part of the discovery loop. When the request instead needs claim-level synthesis, methodological details, or comparison, finish retrieval first and then read the 3–5 most load-bearing candidates with orx paper <id> (or the number the user requested). Do not narrow to 3–5 papers before retrieval has produced its ranked 5–15 candidate set.

Do not compare alphaXiv votes numerically with OpenAlex citations; they measure different things. Topical fit is the cross-source ranking signal.

Reading selected papers

orx paper auto-detects an arXiv id/URL, bioRxiv DOI, other DOI, OpenAlex W… id, or PubMed PMID/URL. For alphaXiv it returns a compact structured report; use --full for exact wording and surrounding context, even when a report exists. Without --full, a missing report automatically falls back to extracted full text in the same command. --full skips the report entirely rather than acting as a superset of the default. If extracted text is also unavailable, locate the original PDF through the returned paper link.

orx paper prints the alphaXiv link before the content. When alphaXiv has an associated repository, it then prints GitHub: <url>. This is the most-starred associated repository and can be a framework rather than the paper's own code, so sanity-check it before treating it as the implementation.

All discovery and paper commands honor the user's disabled literature-source settings; do not work around an error saying a source is disabled.

Read a paper before using it as claim-level support. Discovery lists may link candidate titles, but must not imply that methods or findings were verified from snippets alone.

Original visuals

This is the start of the file. Read the full SKILL.md on GitHub.

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