Compile core venues and papers into a structured field survey.
npx skills add https://github.com/jssfy/k-skills --skill field-papers-survey
Please help me install this Agent Skill. Skill: field-papers-survey Source: https://github.com/jssfy/k-skills/tree/main/skills/research/field-papers-survey Command: npx skills add https://github.com/jssfy/k-skills --skill field-papers-survey
field-papers-survey enables researchers to automatically panorama survey a target field by compiling core conferences/journals, organizing outputs by subfield, timeline, and development context, and producing a lightweight analysis of top papers.
Provide a research field name (e.g., "computer vision") to trigger a full field survey covering venues, subfields, timelines, and top papers.
High-intent search queries and answers about installing and using this skill.
A panoramic literature survey automatically identifies core conferences and journals across a research field, classifies them by subfield and timeline, and deduplicates cross-channel results to produce a structured field mapping.
Mapping development timelines and subfields involves classifying collected venue outputs into distinct research contexts, organizing top papers chronologically, and generating per-subfield tracking documents to visualize field evolution.
Multi-channel paper gathering uses parallelized collection across defined sources, retrieves top papers based on field relevance, and applies cross-field deduplication to ensure a clean, unified literature dataset.
Yes, automated field survey generation integrates multi-channel information gathering, classifies venues by subfield and timeline, and produces a lightweight top-paper analysis ready for downstream research generation.
Field surveying works for broad research domains by accepting a user-specified field name, identifying its core venues, and organizing the comprehensive literature panorama by subfield, timeline, and development context.
Automated literature mapping relies on multi-channel information gathering and cross-field deduplication, meaning extremely niche subfields with sparse venue representation may yield limited top papers for lightweight analysis.
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