opendatalab academic-retrieval
Sciverse academic paper retrieval: structured metadata search, semantic chunk retrieval for RAG, and byte-range content reading. For agent workflows that need citation-grade scientific literature.
When to use
Trigger this skill when the user's request involves any of:
- Locating academic papers by structured criteria (authors, year, journal, subjects)
- Grounding answers in paper excerpts (RAG / citations)
- Expanding the original text around a known doc_id (more bytes before/after a chunk)
Authentication
This skill requires the SCIVERSE_API_TOKEN environment variable
(obtain from https://sciverse.space). Optionally set SCIVERSE_BASE_URL
to override the default API base URL.
Tools
search_papers
Search academic papers by structured filters (title, authors, journal, year, subjects, etc.). Use when: "find Hinton's papers from 2020-2023", "Nature papers on CRISPR". Not for: natural-language Q&A retrieval (use semantic_search) or full-text snippets (use read_content). Returns: list of papers; each entry has unique_id (always present), doc_id (only when full text exists), title, author, abstract, publication_venue_name_unified, publication_published_year.
Invoke: node scripts/search_papers.mjs '<JSON args>'
semantic_search
Natural-language semantic search returning relevant paper chunks for RAG-style answering. Use when: "How does Transformer attention work?", "What are recent methods for protein structure prediction?". Not for: precise field filtering (use search_papers) or fetching full original text (use read_content). Returns: list of chunks; each entry has chunk_id, doc_id, abstract, chunk, score, title, offset. Typical chain: semantic_search → pick chunk → read_content(doc_id, offset).
Invoke: node scripts/semantic_search.mjs '<JSON args>'
list_catalog
Returns the schema catalog for search_papers: every field name, type, whether it's filterable / sortable, default-return status, human description, and applicable FilterOperators. Use when: "Which field do I filter by DOI?", "What values can access_oa_status take?", "What's the right enum for metadata_type?". Not for: actually searching papers (use search_papers / semantic_search). Typical pattern: call once when first encountering Sciverse or facing an ambiguous field need, then construct precise search_papers filters from the returned schema. Pass include_sample_values=true to also fetch top-20 values for enum-like fields (OpenSearch terms aggregation, 24h cached).
Invoke: node scripts/list_catalog.mjs '<JSON args>'
list_paper_relations
Paginate the full relation list of a paper. citations/references/related_works are unbounded arrays (up to 340k entries for a single paper) and are NOT projectable in search_papers, so this endpoint is the only way to read them. Use when: "What does paper X cite?" (relation=REFERENCES), "Which papers cite paper X?" (relation=CITATIONS), "Works related to paper X" (relation=RELATED_WORKS). Note: CITATIONS (incoming: who cites me) and REFERENCES (outgoing: who I cite) are opposite directions. Typical chain: get unique_id from search_papers / semantic_search, then paginate here by relation. Two limits (CITATIONS only; REFERENCES/RELATED_WORKS max out at 11833/20 in practice): more than 10000 relations returns 429; page*page_size above 10000 returns 400. In both cases switch to search_papers with filters_advanced on references_unique_id — it supports deep paging and arbitrary sorting. total_count counts in-corpus matches only, so it can differ from the paper's own citation_count by about 1%.
Invoke: node scripts/list_paper_relations.mjs '<JSON args>'
read_content
Read a UTF-8 byte range of a paper's original text. Typically used with a doc_id/offset returned by semantic_search to expand context (read more bytes before or after a chunk). Returns: text fragment, bytes_returned, next_offset, more (boolean).
Invoke: node scripts/read_content.mjs '<JSON args>'
get_resource
Returns the binary bytes of a paper figure / table image referenced
inside read_content's Markdown via  placeholders.
Use when the user asks to see / display / describe a figure and
read_content output contains an image reference.
Input file_name comes from the Markdown URL part (relative path,
no \\ or ..).
Returns: raw image stream + image/* Content-Type. The SDK / MCP
server wraps the bytes as base64 + mimeType so Claude (multimodal)
can read the image directly.
Invoke: node scripts/get_resource.mjs '<JSON args>'
Bootstrap: learn the schema first
If you're unsure which fields exist or what values an enum takes
(e.g. metadata_type, language, access_oa_status), call
list_catalog once at the start. Sample values are returned for
low-cardinality fields. Use it instead of guessing field names —
guessing wastes turns.
list_catalog(include_sample_values=true)
└─▶ fields[].name + sample_values → precise filter construction
Recipes
RAG flow (natural-language Q&A):
semantic_search(query=...) → hits[i].doc_id, hits[i].offset
└─▶ read_content(doc_id, offset)
Lookup by DOI:
search_papers(filters_advanced=[{field: "doi", value: "10.1038/..."}])
OA + year filter:
search_papers(
year_from=2024,
filters_advanced=[{field: "access_is_oa", value: "true"}]
)
Scoped semantic search (constrained corpus):
semantic_search(
query="...",
filters={"author": ["Hinton"],
"publication_published_year": {"gte": 2020}}
) # applied at recall time, server-side; AND across fields
Soft semantics: chunks missing that metadata are NOT excluded. For a hard guarantee, or meta-only constraints (fwci, citation graph, complex hit-sets), scope by doc_id — a HARD recall-time filter:
search_papers(..., fields=["doc_id","title"]) → collect doc_id
semantic_search(query=..., filters={"doc_id": [...]})
# hits never leave the set; empty list → empty hits (never global);
# up to 1000 deduped ids (400 SCOPE_TOO_LARGE beyond)
Bias fuzzy search ranking (soft boosts — stackable):
Three multiplicative boosts (freshness_boost / impact_boost /
language_affinity, each NONE/MILD/STRONG) reorder fuzzy-search
results while keeping relevance. Only effective when query is
non-empty; ignored when any sort is set; shallow paging while active.
sort_by_year defaults to auto (relevance with query, newest-first
for pure filters); query+desc is an anti-pattern — it degrades the
query to a match filter and disables all boosts; use freshness_boost.
search_papers(query="large language model", freshness_boost="STRONG")
# recent first: STRONG=3-year decay, MILD=10-year
search_papers(query="protein folding", impact_boost="MILD")
# highly-cited float up (bounded; zero-citation stays neutral)
search_papers(query="深度学习", language_affinity="MILD")
# demote (never exclude) results not in the query's language;
# unknown-language papers stay neutral; hard-exclude via
# filters_advanced=[{"field":"language","value":"zh"}]
Search authors or journals (collection):
Set collection to authors or sources (default papers) to search
those entities. Each has its own fields — call
list_catalog(collection="authors") first; use filters_advanced +
sort_advanced (papers convenience fields apply to papers only).
search_papers(collection="authors",
filters_advanced=[{field: "summary_stats.h_index", operator: "FILTER_OP_GTE", value: 50}],
sort_advanced=[{field: "cited_by_count", order: "SORT_ORDER_DESC"}])
Fetch a paper figure / image:
When read_content Markdown contains , call
get_resource with the file_name to fetch image binary.
read_content(doc_id, offset) → markdown 
└─▶ get_resource(file_name="dt=xxx/p/f3.png")
Reading fulltext (check first):
Each search_papers hit carries is_content_accessible (bool): true only when
the paper has fulltext AND the caller is authorized. Check it before
read_content(doc_id, ...) — false means no fulltext or no read permission.
Exit codes
0— success; stdout is the JSON response1— HTTP 4xx/5xx; stderr contains status code and response body2— argument error (missing token, malformed JSON, required field absent)