Search arXiv literature and output compact Neo4j-ready metadata.
npx skills add https://github.com/YH-05/quants --skill alphaxiv-search
Please help me install this Agent Skill. Skill: alphaxiv-search Source: https://github.com/YH-05/quants/tree/main/.claude/skills/alphaxiv-search Command: npx skills add https://github.com/YH-05/quants --skill alphaxiv-search
This skill solves the challenge of performing comprehensive arXiv literature searches without consuming excessive context tokens or missing relevant work, by guiding tool selection and batching strategies to maximize coverage while preserving context.
Run a broad arXiv search using the Tier 1 embedding similarity strategy to fetch the top 5 relevant papers.
High-intent search queries and answers about installing and using this skill.
To perform an arXiv literature search without consuming excessive context tokens, use a tiered embedding similarity search strategy. This approach maximizes coverage by guiding tool selection and batching calls to preserve context.
Yes, you can format arXiv search results directly for Neo4j knowledge graph integration. The process enforces data formatting requirements to produce a compact, Neo4j-ready set of metadata suitable for Source, Topic, and Method nodes.
The best way to batch arXiv searches for a literature review is to optimize parallel calls with enforced batching strategies. This maximizes search coverage while applying deduplication to avoid duplicate results.
Yes, embedding similarity search works for knowledge-gap analysis across multiple domains. It serves as the primary Tier 1 strategy for broad coverage, with guarded use of auxiliary tools to refine results when needed.
You should use a tiered search strategy for scientific literature discovery because it prioritizes embedding similarity search for broad coverage and uses auxiliary tools to refine results. This prevents missing relevant work while preserving context.
The main limitation when using auxiliary tools to refine arXiv search results is context token consumption. The skill enforces tool-selection constraints and guarded use of auxiliary tools to prevent excessive token usage during ontology enrichment.
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