Screen papers by title and abstract to generate JSON-ready candidate records.
npx skills add https://github.com/Dai0-2/Paper_Reach --skill paper-search-dai0-2
Please help me install this Agent Skill. Skill: paper-search Source: https://github.com/Dai0-2/Paper_Reach/tree/main/skills/paper-search Command: npx skills add https://github.com/Dai0-2/Paper_Reach --skill paper-search-dai0-2
This skill helps researchers and agents rapidly assemble a high-quality set of candidate papers by performing conservative screening using titles and abstracts, reducing time spent on irrelevant results.
Provide a topic and optional filters to generate a candidate list for screening.
High-intent search queries and answers about installing and using this skill.
Screening literature abstracts involves evaluating titles and abstracts against inclusion and exclusion criteria to build a first-pass shortlist. This conservative screening generates structured paper records with flags indicating whether full-text review is needed.
Generating a structured candidate set from local metadata requires normalizing paper records into a JSON-ready format. You apply coarse screening using title and abstract cues to filter local files and output structured records with screening decisions.
Filtering candidate papers during abstract screening accepts inputs like topic, keywords, inclusion criteria, exclusion criteria, and year range. These filters ensure the output paper records strictly match the specified constraints before full-text review.
Normalizing paper records into JSON-ready format transforms screened titles and abstracts into structured data. The process outputs normalized paper records accompanied by screening decisions and need_fulltext flags for downstream processing.
Abstract-level screening works for both online sources and local metadata files. It normalizes inputs from either source into structured JSON-ready paper records, applying consistent screening decisions and need_fulltext flags.
A need_fulltext flag identifies candidate papers that pass conservative title and abstract screening but require deeper evaluation. It separates clearly irrelevant results from those needing full-text review, optimizing the research workflow.
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