literature-survey

Search academic indexes and generate a cited Markdown literature survey.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/UnaryLab/ai-for-research --skill literature-survey-unarylab
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: literature-survey
Source: https://github.com/UnaryLab/ai-for-research/tree/main/skills/literature-survey
Command: npx skills add https://github.com/UnaryLab/ai-for-research --skill literature-survey-unarylab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scripts/paper_db.py, scripts/search_semantic_scholar.py, scripts/search_arxiv.py, scripts/search_openalex.py, scripts/search_crossref.py, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Produces a rigorous, well-structured literature survey with verified, direct inline citations, so researchers can quickly understand the state of the art and write related work without citation errors.

Core Features & Use Cases

  • Multi-source paper search across Semantic Scholar, arXiv, OpenAlex, and Crossref, routed by venue family to improve coverage.
  • Disciplined 6-phase workflow (frontier → survey → deep dive → code/artifacts mapping → synthesis → final report), including mandatory full reading in deep dive.
  • Citation-safe survey writing that uses direct inline hyperlinks for every non-trivial claim and supports optional historical lineage tracing.

Quick Start

Run the skill on a topic like "AI accelerators for sparse attention" to generate a fully cited Markdown survey report with inline hyperlinks.

Frequently Asked Questions about literature-survey

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write a literature survey with verified inline citations?

A literature survey with verified inline citations is generated by searching multiple academic indexes, running a phased deep-dive workflow, and synthesizing findings into a Markdown report with direct hyperlinks. This process ensures every non-trivial claim is backed by a linked source.

Can I search arXiv and Semantic Scholar to map the state of the art for a research topic?

Yes, you can map the state of the art by searching across arXiv, Semantic Scholar, OpenAlex, and Crossref. Searches are routed by venue family to improve coverage, synthesizing cross-family topics spanning AI/ML, computer architecture, and Nature-family science.

What's the best way to generate a related work section without citation errors?

The best way to generate a related work section without citation errors is using a citation-safe writing workflow that enforces direct inline hyperlinks. It requires a full reading during the deep dive phase to ensure citation integrity before synthesis.

How does a systematic review workflow handle historical lineage tracing?

A systematic review workflow handles historical lineage tracing by supporting optional historical lineage tracking during the synthesis phase. It follows a disciplined 6-phase process from frontier search to final report, mapping the evolution of research topics.

Does this literature survey tool work for cross-family topics outside of AI and ML?

Yes, the literature survey tool works for cross-family topics outside AI and ML. It is explicitly applicable to computer architecture and Nature-family science, routing searches by venue family to improve source coverage across different scientific domains.

What are the steps to synthesize academic papers into a cited Markdown report?

To synthesize academic papers into a cited Markdown report, follow a 6-phase workflow: frontier search, survey, mandatory full-reading deep dive, code and artifacts mapping, synthesis, and final report generation with inline-linked narrative and gated outputs.