sciverse-paper-search

name	sciverse-paper-search
description	Use this skill for scientific literature search, evidence retrieval, paper metadata screening, and cited research synthesis with Sciverse. This LazyLLM-adapted version supports SciverseSearch search, meta_search, meta_catalog, and get_content only; it does not assume full Sciverse MCP resource or attachment APIs are available.

Sciverse Paper Search Skill

Overview

Use this skill when the user needs scientific literature retrieval, paper metadata screening, citation-ready evidence, or a research synthesis grounded in Sciverse search results.

This skill is adapted to the current LazyLLM SciverseSearch implementation. It must only rely on the currently supported tool capabilities:

• sciverse_search.search
• sciverse_search.meta_search
• sciverse_search.meta_catalog
• sciverse_search.get_content

Do not assume Sciverse MCP tools, resource APIs, binary attachment downloads, figure/table downloads, DianShi, or SeqStudio capabilities are available unless the runtime explicitly exposes those tools.

When To Use

Use this skill for:

• Finding scientific papers on a research topic.
• Retrieving citable evidence snippets for a scientific question.
• Screening papers by year, venue, DOI, author, title, or metadata fields.
• Building paper lists for literature reviews.
• Reading fuller text for selected Sciverse results when doc_id is available.
• Producing cited summaries, comparisons, or evidence tables from Sciverse results.

Do not use this skill for:

• Downloading paper images, figures, tables, PDFs, or binary resources.
• Chemical retrosynthesis, molecule/reaction search, or DianShi workflows.
• Protein sequence/structure annotation or SeqStudio workflows.
• Claims that require full-text access when only abstracts or snippets are available.

Available Tool Capabilities

sciverse_search.search

Use this for normal Agent retrieval.

Recommended defaults:

query=<research question or paper topic>
topk=5
search_type="agentic"
include_content=true

Use search_type="agentic" when the user asks a natural-language scientific question and needs evidence passages.

Use search_type="meta" when the user mainly needs paper metadata. You may pass year_from and year_to for year constraints.

Current implementation notes:

• topk is capped at 10.
• Results are normalized to title, url, snippet, source, and extra.
• extra may include doc_id, doi, year, venue, authors, score, chunk_id, page_no, offset, and content.

sciverse_search.meta_search

Use this for advanced metadata search, filtering, pagination, and paper-list tasks.

Important constraints:

• Do not use query together with sort.
• Do not use cursor together with page > 1.
• page_size is capped at 200.
• freshness_boost must be NONE, MILD, or STRONG.

Useful parameters:

query
filters
sort
fields
page
page_size
cursor
freshness_boost
include_content
year_from
year_to

Use year_from and year_to for simple publication-year filtering.

sciverse_search.meta_catalog

Call this before constructing complex filters or sort clauses if you are unsure which fields and operators are supported.

Use:

include_sample_values=false

Set include_sample_values=true only when enum-like sample values are needed.

sciverse_search.get_content

Use this to read fuller text for one search result.

The current implementation:

1. Looks for doc_id in the item or item.extra.doc_id.
2. Calls Sciverse /content with doc_id.
3. Supports chunked reading with offset and limit.
4. Falls back to extra.content, snippet, or URL fetching when /content is unavailable.

Do not claim full text was read unless get_content actually returns fuller content. If the result only contains an abstract or snippet, say that the analysis is based on metadata/snippets.

Workflow

Phase 1: Clarify Search Intent

Classify the user's request:

• Evidence answer: use agentic search.
• Paper list or screening: use meta search.
• Field-specific filtering: call meta catalog first.
• Deep literature review: combine agentic search and meta search.
• Read one selected paper: use get_content on a selected result.

Phase 2: Retrieve Papers

For natural-language evidence questions:

sciverse_search.search(query="<question>", topk=5, search_type="agentic", include_content=true)

For paper screening:

sciverse_search.meta_search(
  query="<topic>",
  fields=["title", "doi", "doc_id", "abstract", "author", "publication_published_year", "publication_venue_name_unified"],
  page_size=25,
  year_from=<optional>,
  year_to=<optional>
)

For precise filters:

1. Call sciverse_search.meta_catalog.
2. Build filters only from supported fields/operators.
3. Call meta_search.

Phase 3: Inspect and Read

For the most relevant results:

1. Extract title, DOI, year, venue, authors, doc_id, and snippet/content.
2. If the user needs deeper analysis, call get_content on selected items.
3. Use offset and limit for chunked reading when needed.

Example:

sciverse_search.get_content(item=<selected_result>, offset=0, limit=2000)

Phase 4: Synthesize With Source Discipline

When answering:

• Separate confirmed full-text evidence from abstract/snippet-only evidence.
• Cite papers using title, year, venue, DOI, and doc_id when available.
• Do not invent bibliographic fields.
• If Sciverse returns limited content, state the limitation.
• For literature reviews, group papers by theme, method, dataset, finding, limitation, and open question.

Output Patterns

Paper Search Results

Use a compact table:

| Paper | Year | Venue | Why relevant | DOI / doc_id |

Evidence Answer

Use:

• Short answer.
• Evidence bullets with paper identifiers.
• Caveats about snippet/full-text availability.
• Suggested next searches if coverage is thin.

Literature Review

Use:

• Search strategy.
• Included papers.
• Thematic synthesis.
• Method and evidence comparison.
• Limitations and open questions.
• Citation table.

Safety and Limitations

• Do not promise resource, attachment, figure, table, or PDF downloads.
• Do not use unsupported Sciverse MCP tool names.
• Do not fabricate citations, DOI values, doc IDs, authors, or venues.
• If authentication fails, tell the user Sciverse API access may need a valid API key or dynamic auth entry.
• If get_content falls back to snippets, clearly label the source as snippet/abstract-based.