| name | sciverse-academic-retrieval |
| description | 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. |
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 (a highly-cited paper can have thousands); search_papers
only inlines a truncated few, so use this endpoint for the full list.
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.
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"}]
)
Structured + semantic hybrid:
search_papers(authors=[...], year_from=2020) → doc_ids
semantic_search(query=...) → filter hits client-side by doc_ids
Bias fuzzy search toward recent work (freshness boost):
Set freshness_boost to weight results by publication date with
gauss decay. Only effective when query is non-empty; mutually
exclusive with sort_by_year.
search_papers(query="large language model", freshness_boost="STRONG")
# STRONG: 3-year decay, for tracking research directions
search_papers(query="protein folding", freshness_boost="MILD")
# MILD: 10-year decay, for everyday literature search
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)
