sciverse agent tools

SciVerse 学术文献检索：按结构化条件查元数据、自然语言语义检索片段、按字节读取原文。适合需要权威学术文献支撑的 RAG 与 agent 工作流。

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 doc_id, title, author, abstract, publication_venue_name, 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>'

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>'

Composition patterns

Typical RAG flow:

semantic_search(query=...)
    └─▶ hits[i].doc_id, hits[i].offset
            └─▶ read_content(doc_id, offset)

Structured filter + metadata lookup:

search_papers(authors=[...], year_from=2020)
    └─▶ list of hits[].doc_id

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)