get-review-theme_skill

This skill extracts structured review themes from diverse inputs and outputs topic, keywords, and core questions for literature reviews.
  • Python

1.3k

GitHub Stars

3

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill huangwb8/chineseresearchlatex --skill get-review-theme

  • config.yaml260 B
  • README.md7.4 KB
  • SKILL.md9.2 KB

Overview

This skill extracts structured review themes from files, images, webpages, or plain descriptions and produces a ready-to-use “topic + keywords + core questions” output for literature review workflows. It auto-detects input type, extracts content, and returns concise, retrieval-ready keywords and concrete research challenges. Outputs can be formatted as text, YAML, or JSON to integrate with downstream systematic-review tools.

How this skill works

The skill first identifies the input type (text, image, URL, PDF, Word, folder) and applies the appropriate extractor or LLM vision capability to obtain source content. It semantically analyzes the content to identify research object, methods, important terms, and challenges, then distills a one-sentence topic, 5–10 standardized English keywords, and 2–5 concrete core questions. Finally it formats the result into the requested output style (text, YAML, or JSON) and performs basic validation.

When to use it

  • You need a concise review topic and search keywords from any document, image, or webpage
  • Preparing inputs for a systematic literature review or query formulation
  • Rapidly summarizing research focus across a folder of papers or project notes
  • Converting a figure, slide, or screenshot into a reviewable theme
  • Generating search terms and concrete review questions for database queries

Best practices

  • Provide the exact input source (file path, URL, or paste text) and desired output format (text/yaml/json)
  • If extraction tools fail for protected or complex files, paste the key text snippets manually
  • For images, include captions or brief descriptions when visual content is dense
  • Prefer supplying a folder with representative files rather than thousands of mixed documents
  • Use the generated keywords to seed database queries and validate by inspecting initial search results

Example use cases

  • Extract a review topic and keywords from a single PDF paper to seed a systematic review
  • Scan a research folder and produce an aggregated topic plus core open questions for a grant literature survey
  • Convert a conference slide image into a concise topic and targeted search keywords
  • Pull a structured topic and keyword list from a lab notebook Markdown file to design inclusion/exclusion criteria
  • Analyze a webpage or blog post to derive academic keywords and possible methodological gaps

FAQ

Supported inputs include plain text, images (png/jpg/jpeg/webp/gif), URLs, .md/.txt/.tex, .pdf, .doc/.docx, and folders containing these files. If extraction fails, paste the content directly.

Can outputs be used directly in a systematic-review pipeline?

Yes. The topic maps to the review title, keywords serve as search terms, and core questions inform scope and inclusion criteria. Choose JSON or YAML for smooth integration.

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