Scientific Retrieval
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
scientific-retrieval is a Claude skill that retrieves and recommends relevant documents from scientific, financial, and historical archives with relevance ranking.
About
This skill retrieves and recommends relevant documents from financial, historical, and scientific archives. A user applies it to find literature, filings, or records through keyword, semantic, and citation-based search. It ranks results by relevance, authority, and recency and reports search coverage and gaps.
- Five-step protocol from query analysis to result synthesis
- Multi-strategy search: keyword, semantic, and citation-based
- Covers financial filings, historical archives, literature, and patents
Scientific Retrieval by the numbers
- 16 all-time installs (skills.sh)
- Ranked #1,049 of 1,879 Documentation skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
scientific-retrieval capabilities & compatibility
- Capabilities
- research · web search
- Use cases
- research · web search
- Pricing
- Free
What scientific-retrieval says it does
Retrieve relevant documents, datasets, and resources from large scientific and domain-specific archives.
Multi-strategy search (keyword, semantic, citation-based)
Always report search coverage and potential gaps
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| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Retrieve and rank relevant scientific literature, filings, or archival documents for an information need.
Who is it for?
Finding and ranking relevant literature, filings, or archival documents for a research need.
Skip if: Extracting structured data or generating new content.
When should I use this skill?
Retrieving scientific literature, SEC filings, historical archives, or patents for an information need.
What you get
A ranked set of relevant documents with metadata, plus a report of search coverage and gaps.
- ranked document results with metadata
- citation-chain tracking
- search-coverage report
By the numbers
- five-step retrieval protocol
- four retrieval domains covered
Files
Scientific Retrieval & Recommendation
Purpose
Retrieve relevant documents, datasets, and resources from large scientific and domain-specific archives.
Key Datasets
- Financial Reports SEC (JanosAudran/financial-reports-sec): SEC 10-K filings with 20 sections and sentiment labels
- Historical Newswire (dell-research-harvard/newswire): Historical news article corpus for digital humanities research
Protocol
1. Query analysis — Parse information need, identify key concepts and constraints 2. Source selection — Choose appropriate databases and archives 3. Search execution — Multi-strategy search (keyword, semantic, citation-based) 4. Relevance ranking — Score and rank results by relevance, authority, recency 5. Result synthesis — Organize and present findings with metadata
Retrieval Domains
- Financial documents: SEC filings (10-K, 10-Q, 8-K), earnings calls, analyst reports
- Historical archives: Newspapers, government records, digitized manuscripts
- Scientific literature: Journal articles, preprints, conference proceedings
- Patent databases: USPTO, EPO, WIPO patent documents
Recommendation Types
- Similar documents: Find related papers/reports based on content similarity
- Citation chain: Forward/backward citation tracking
- Cross-domain: Find analogous work in different disciplines
- Temporal: Track how a topic evolves over time
Rules
- Always report search coverage and potential gaps
- Rank by relevance, not just recency
- Include document metadata (date, source, section, author)
- For financial documents, note the filing period and any restatements
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What search strategies does it use?
Keyword, semantic, and citation-based search across selected databases and archives.
How are results ranked?
By relevance, authority, and recency, not just recency, with document metadata included.