scientific-retrieval
Retrieve and recommend relevant documents from financial, historical, and scientific archives
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scientific-retrieval/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scientific-retrieval Compares
| Feature / Agent | scientific-retrieval | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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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