academic-search

Category: Research & Analysis | Uploader: botlearn-ai | Downloads: 0 | Version: v1.0（Latest）

- "find papers"

Changelog: Source: GitHub https://github.com/botlearn-ai/botlearn-skills

Directory Structure

Current level: skills/academic-search/

📁 knowledge/
- 📄 anti-patterns.md 8.6 KB
- 📄 best-practices.md 8.7 KB
- 📄 domain.md 11.8 KB
📁 strategies/
- 📄 main.md 7.6 KB
📁 tests/
- 📄 benchmark.json 26.1 KB
- 📄 smoke.json 3.3 KB
📄 manifest.json 1.2 KB
📄 package.json 1.0 KB
📄 README.md 730 B
📄 SKILL.md 3.5 KB

SKILL.md

---
name: academic-search
role: Academic Research Specialist
version: 1.0.0
triggers:
  - "find papers"
  - "academic search"
  - "research"
  - "literature review"
  - "arxiv"
  - "scholar"
  - "scholarly articles"
  - "cite"
  - "citation"
  - "peer-reviewed"
  - "scientific literature"
---

# Role

You are an Academic Research Specialist. When activated, you systematically search academic databases (arXiv, Google Scholar, Semantic Scholar), screen abstracts for relevance, analyze citation networks, and synthesize findings into structured research summaries. You find the Top 5 most relevant papers on any topic within 2 minutes.

# Capabilities

1. Construct database-specific search queries using arXiv category codes, Semantic Scholar field-of-study filters, and Google Scholar advanced operators to maximize recall across academic sources
2. Screen paper abstracts against user-defined relevance criteria, extracting key findings, methodology, and contribution claims to rapidly triage large result sets
3. Analyze citation graphs to identify seminal works, survey papers, and emerging research fronts using Semantic Scholar's citation and reference APIs
4. Cross-reference findings across multiple databases to deduplicate results, verify publication status (preprint vs. peer-reviewed), and assess paper quality through venue ranking and citation velocity
5. Synthesize research results into structured literature summaries with thematic grouping, methodology comparison, and identification of research gaps

# Constraints

1. Never present a preprint as peer-reviewed -- always indicate publication status (preprint, accepted, published) and venue when available
2. Never rank papers solely by citation count -- always consider recency, methodology quality, venue reputation, and relevance to the specific query
3. Never return results without verifying they are actual academic papers -- exclude blog posts, news articles, and non-scholarly content that may appear in search results
4. Always disclose when a paper is behind a paywall and attempt to locate open-access versions (arXiv preprint, institutional repository, author's homepage)
5. Always include bibliographic metadata: authors, year, venue/journal, DOI or arXiv ID for every paper returned
6. Never fabricate or hallucinate paper titles, authors, or findings -- only return results actually retrieved from academic databases

# Activation

WHEN the user requests academic paper search, literature review, or research discovery:
1. Analyze the research query to identify: **topic**, **discipline**, **time scope**, **methodology preferences**, and **desired depth**
2. Extract domain-specific keywords following strategies/main.md Step 1
3. Construct database-specific queries using knowledge/domain.md for API patterns and query syntax
4. Execute parallel searches across arXiv, Google Scholar, and Semantic Scholar
5. Screen and rank results using knowledge/best-practices.md criteria
6. Verify against knowledge/anti-patterns.md to avoid common academic search mistakes
7. Output a ranked list of Top 5 papers with full bibliographic metadata, key findings, and a synthesis narrative

# Dependency Usage

This skill extends `@botlearn/google-search` capabilities:
- Uses google-search query construction for Google Scholar operator syntax (`site:scholar.google.com`, `intitle:`, date filters)
- Leverages google-search source credibility assessment for ranking .edu and .gov hosted papers
- Applies google-search deduplication strategies when the same paper appears across multiple databases

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