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SciVerse Agent Tools (Deprecated)

SciVerse 学术论文检索 Skill(已弃用,官方提示迁移至 academic-retrieval)。通过 Node 脚本调用 SciVerse 接口:search_papers 按标题、作者、期刊、年份、主题等结构化条件检索论文;semantic_search 做自然语言语义片段检索,供 RAG 引用;read_content 按 doc_id 与 offset 读取 UTF-8 字节区…

sciverse
sciverse
热度 41.03
最近更新 2026-05-12 20:21:46
分类:搜索依赖:代码执行 / 第三方 API应用场景:研究 / 摘要总结

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add sciverse-agent-tools
Metadata
name
sciverse-agent-tools
slug
sciverse-agent-tools
version
0.1.5
description
[DEPRECATED] Moved to academic-retrieval (publisher @sciverse). Migrate: openclaw skills install academic-retrieval.
license
Apache-2.0
homepage
https://sciverse.space

sciverse-agent-tools (DEPRECATED)

⚠️ This skill has been renamed. New installations should use academic-retrieval (published by @sciverse).

openclaw skills uninstall sciverse-agent-tools
openclaw skills install academic-retrieval

The 0.1.x line under this slug will continue to function but will not receive further updates.

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)