scientific-retrieval

Retrieve and recommend relevant documents from financial, historical, and scientific archives

564 stars

Best use case

scientific-retrieval is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Retrieve and recommend relevant documents from financial, historical, and scientific archives

Teams using scientific-retrieval should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/scientific-retrieval/SKILL.md --create-dirs "https://raw.githubusercontent.com/beita6969/ScienceClaw/main/skills/scientific-retrieval/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/scientific-retrieval/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How scientific-retrieval Compares

Feature / Agentscientific-retrievalStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Retrieve and recommend relevant documents from financial, historical, and scientific archives

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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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