Generate ranked paper shortlists from anchor, topic, or wiki seeds.
npx skills add https://github.com/Lambenthan/empiricalwiki --skill discover-lambenthan
Please help me install this Agent Skill. Skill: discover Source: https://github.com/Lambenthan/empiricalwiki/tree/main/.claude/skills/discover Command: npx skills add https://github.com/Lambenthan/empiricalwiki --skill discover-lambenthan
It helps you quickly determine which papers to read next by generating a ranked shortlist based on anchors, a topic query, or your current wiki state.
Use discover to get a reading shortlist anchored on a paper ID by asking: “Run /discover with --anchor 2106.09685 and --limit 10 so I can see the best next related papers with rationales.”
High-intent search queries and answers about installing and using this skill.
You can generate a ranked shortlist of candidate papers for next-reading decisions using an anchor paper ID, a topic query, or your recently edited wiki pages. The shortlist includes rationales to help you judge relevance before reading.
To get paper recommendations based on your wiki, use wiki-derived discovery mode. It generates candidates from recently edited wiki pages while deduplicating against papers already present in your wiki to avoid wasting review time on duplicates.
Yes, you can get paper discovery results without ingesting them. The discovery process is non-ingesting by design, outputting only machine-readable checkpoints and a user-facing rationale so you can choose whether to ingest candidates afterward.
To explore related work around a research topic, use topic-based exploration mode. It generates a ranked shortlist of candidate papers with rationales, allowing you to survey the direction and judge relevance before committing to reading or ingesting.
Yes, the paper discovery shortlist filters out duplicates from your existing wiki. It deduplicates candidates against papers already present in your wiki pages, ensuring your reading shortlist only contains new candidate papers to review.
LambenthanGenerates ordered candidate paper shortlists from seed sources for downstream review and ingestion planning.
LambenthanAutomate daily arXiv paper discovery, relevance scoring, and controlled ingest into a research wiki.
LambenthanGenerate slugged wiki foundation pages from Wikipedia or LLM definitions.
LigphiDonkFetch and rank new arXiv and Semantic Scholar papers by relevance, recency, popularity, and quality.
