Skill: sciverse/sciverse-agent-tools (version 0.1.6)

--- 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`](https://clawhub.ai/academic-retrieval) (published by **@sciverse**). > > ```bash > 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)


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)