sciverse-academic-retrieval

# academic-retrieval Sciverse academic paper retrieval: structured metadata search, semantic chunk retrieval for RAG, and character-range content reading (offsets in Unicode code points). 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 text 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 range of a paper's original text addressed in Unicode code points (offset/limit count characters like Python len(), not bytes). Typically used with a doc_id/offset returned by semantic_search to expand context (read more text before or after a chunk). Returns: text fragment, bytes_returned (UTF-8 byte length of text, for reference only), next_offset (code-point offset of the next fragment — page with it, never with bytes_returned), more (boolean). Server behaviour: limit above 524288 is silently clamped; omitting offset returns the whole document ignoring limit — the SDKs / MCP server send offset=0 and limit=4096 by default, so pass offset explicitly when calling the HTTP API directly. **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 response - `1` — HTTP 4xx/5xx; stderr contains status code and response body - `2` — argument error (missing token, malformed JSON, required field absent)