Best use case
literature is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Teams using literature 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/literature/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How literature Compares
| Feature / Agent | literature | 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?
This skill provides specific capabilities for your AI agent. See the About section for full details.
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
# Literature Search & Review ## Overview Comprehensive academic literature search and synthesis across 15+ sources. ## Capabilities - Multi-database parallel search (PubMed, arXiv, bioRxiv, medRxiv, OpenAlex, Semantic Scholar, Crossref, DBLP, CORE, DOAJ, Europe PMC) - Web search via Agent-Reach (Exa semantic search, Jina Reader for any URL/PDF) - Social academic search (Twitter/X threads, YouTube talks, GitHub repos) - Structured literature reviews with citation networks - Knowledge gap identification - Hypothesis generation from literature analysis ## Search Strategy 1. **Query optimization**: Short, keyword-based queries (max 7 words). PubMed-friendly syntax. 2. **Multi-source fan-out**: Parallel queries across all sources for maximum coverage. 3. **Deduplication**: By PMID, DOI, then normalized title. 4. **Reflection loop**: Evaluate coverage → identify gaps → generate follow-up queries. ## Citation Rules - ZERO hallucinated citations. Every reference must come from real search data. - Unified article schema: source_type, title, authors, year, journal, url, doi, pmid, abstract. - Numbered references [1], [2], [3] — built only from retrieved articles. ## Best Practices - Start broad, then narrow. First search finds the landscape; follow-up queries fill gaps. - Cross-domain search. The breakthrough paper might be in an unexpected field. - Check preprints AND published papers. Recent findings may only be on bioRxiv/arXiv. - Verify high-impact claims. Use Semantic Scholar's citation count to identify landmark papers.
Related Skills
research-literature
COPYRIGHT NOTICE
literature-search
Comprehensive multi-database scientific literature search orchestrating Semantic Scholar, OpenAlex, arXiv, PubMed, and CrossRef. Use when: (1) systematic literature review, (2) finding all relevant papers on a topic, (3) checking state of the art, (4) building comprehensive bibliographies. NOT for: single-database queries (use specific search skills), data analysis (use code-execution).
literature-review
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
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Query Wikidata for structured knowledge using SPARQL and entity search. Use when: (1) finding structured facts about entities (people, places, organizations), (2) querying relationships between entities, (3) cross-referencing external identifiers (Wikipedia, VIAF, GND, ORCID), (4) building knowledge graphs from linked data. NOT for: full-text article content (use Wikipedia API), scientific literature (use semantic-scholar), geospatial data (use OpenStreetMap).
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