agentic-paper-digest-skill

To summarize new arXiv and Hugging Face papers automatically, you can use a tool that fetches recent publications over a configurable time window and applies LLM relevance filtering and summarization to produce a structured JSON digest.

Yes, you can filter research papers by specific topics before generating a summary by configuring predefined topic IDs, descriptions, and keywords, which the system uses as criteria for LLM relevance filtering.

Yes, you need an LLM provider via OPENAI_API_KEY or LITELLM_API_KEY, along with Python 3 and network access, to run a local API server for polling research monitoring results and querying stored papers.

The best way to integrate a paper digest into downstream agent workflows is to output the LLM summarized results as a JSON feed, allowing automated agents to consume the structured data for literature triage and recurring updates.

Yes, there are limits to how many papers are fetched during a daily literature triage, as the system uses configurable source paging, candidate caps, and per-topic result limits defined in its configuration files.