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semantic-scholar-search skill

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This skill helps you discover academic literature using Semantic Scholar, analyzing citation networks and extracting trend insights to guide research.

npx playbooks add skill a5c-ai/babysitter --skill semantic-scholar-search

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SKILL.md

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---
name: semantic-scholar-search
description: Academic literature search using Semantic Scholar API for citation-aware paper discovery
allowed-tools:
  - Bash
  - Read
  - Write
  - Edit
  - Glob
  - Grep
  - WebFetch
  - WebSearch
metadata:
  specialization: scientific-discovery
  domain: science
  category: literature-knowledge
  phase: 6
---

# Semantic Scholar Search

## Purpose

Provides academic literature search capabilities using the Semantic Scholar API for citation-aware paper discovery and research trend identification.

## Capabilities

- Keyword and semantic search across papers
- Citation network analysis
- Author influence scoring
- Research trend identification
- Paper recommendation based on reading history
- TLDR summary extraction

## Usage Guidelines

1. **Search Strategy**: Combine keywords with semantic similarity
2. **Citation Analysis**: Build citation networks for key papers
3. **Author Analysis**: Identify influential researchers
4. **Trend Detection**: Track emerging research areas

## Tools/Libraries

- Semantic Scholar API
- NetworkX
- sentence-transformers

Overview

This skill provides academic literature search and discovery using the Semantic Scholar API, focused on citation-aware results and trend detection. It combines keyword and semantic search with citation network analysis to surface influential papers and authors. The skill also extracts TL;DR summaries and recommends papers based on reading history to speed research triage.

How this skill works

The skill issues Semantic Scholar API queries for keyword and embedding-based semantic matches, then augments results with citation graph data to compute influence and connectivity. It can build local citation networks, score authors by citation and network centrality, and generate short summaries for quick assessment. Recommendations use reading history plus semantic similarity and citation context to prioritize relevant follow-ups.

When to use it

When you need citation-aware paper discovery beyond keyword matches.
To map the citation network around a seminal paper or topic.
When identifying influential authors and collaboration hubs matters.
To detect emerging research trends and rising topics.
When you want quick TL;DRs to triage large result sets.

Best practices

Combine exact keyword filters with semantic queries to balance precision and recall.
Seed citation networks with known foundational papers for focused graph construction.
Limit API calls with paging and caching when building citation graphs.
Use author disambiguation heuristics before computing influence scores.
Validate trend signals against publication dates and venue quality.

Example use cases

Discover highly cited papers and their immediate citation neighborhood for a literature review.
Score and rank authors by influence to identify potential collaborators or reviewers.
Generate recommended reading lists tailored to previously read papers.
Detect emerging subtopics by tracking increases in publication and citation velocity.
Produce TL;DR summaries to quickly decide which papers to read in depth.

FAQ

Does this skill require a Semantic Scholar API key?

Yes. Configure your Semantic Scholar API credentials to enable queries and citation retrieval.

Can it handle large citation networks?

It can build and analyze networks, but for large graphs you should page results, cache responses, and apply pruning strategies to remain performant.