Which online databases does it query?

It primarily utilizes the arXiv API for retrieval and Semantic Scholar (via the r.jina.ai proxy) for citation snowballing and metadata backfilling.

What is the primary goal of the Literature Engineer skill?

Its goal is to build a large, verifiable candidate pool of at least 200 research papers with stable identifiers (DOIs/arXiv IDs) to serve as the evidence foundation for research projects.

Can I use this skill without an active internet connection?

Yes, it supports offline-first workflows by merging and normalizing existing local exports in .bib, .csv, .json, or .jsonl formats.

What formats are the results delivered in?

The skill generates a structured papers_raw.jsonl for programmatic use, a CSV for human review, and a Markdown retrieval report summarizing the data collection stats.

How does it prevent AI hallucinations?

The skill follows an 'evidence-first' methodology. If it cannot find or verify a paper's provenance or ID, it is programmed to block the process and request more data rather than fabricating titles.

Literature Engineer

Name: Literature Engineer
Author: WILLOSCAR

byWILLOSCAR

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Data Science & ML

Automates the gathering, normalization, and verification of large-scale research paper datasets for evidence-based surveys.

About

The Literature Engineer skill is a robust research utility designed to build high-volume, verifiable candidate pools of academic papers. By employing a multi-route approach—integrating offline imports, online API retrieval from sources like arXiv, and citation snowballing—it ensures researchers have a foundation of at least 200 papers before beginning a survey. It prioritizes data integrity by enforcing stable identifiers (DOIs/arXiv IDs) and detailed provenance tracking, effectively preventing AI hallucinations by requiring verifiable evidence before any drafting or synthesis occurs.

Key Features

Evidence-first guardrails that block fabrication of non-existent papers
89 GitHub stars
Support for large-scale candidate pools (target 200+) with structured JSONL output
Multi-route expansion via offline files, online APIs, and citation snowballing
Automated retrieval reports detailing coverage, provenance, and metadata health
Metadata normalization and deduplication using stable IDs (DOI, arXiv ID)

Use Cases

Automated discovery of seminal works via forward and backward citation snowballing
Building comprehensive bibliographies for systematic literature reviews or whitepapers
Merging and deduplicating messy paper exports from multiple academic databases

About

Key Features

Evidence-first guardrails that block fabrication of non-existent papers
89 GitHub stars
Support for large-scale candidate pools (target 200+) with structured JSONL output
Multi-route expansion via offline files, online APIs, and citation snowballing
Automated retrieval reports detailing coverage, provenance, and metadata health
Metadata normalization and deduplication using stable IDs (DOI, arXiv ID)

Use Cases

Automated discovery of seminal works via forward and backward citation snowballing
Building comprehensive bibliographies for systematic literature reviews or whitepapers
Merging and deduplicating messy paper exports from multiple academic databases