deep-research

Research "$ARGUMENTS" in depth using the paper and paper-search CLI tools. Follow this workflow:

1. Broad Discovery

Start with broad searches to map the landscape:

paper-search google web "$ARGUMENTS"
paper-search semanticscholar papers "$ARGUMENTS" --limit 10

Scan titles, snippets, and citation counts. Identify the most relevant papers and key terms.

2. Narrow and Filter

Refine based on what you found:

paper-search semanticscholar papers "<refined query>" --year 2023-2025 --min-citations 10
paper-search semanticscholar snippets "<specific question>"
paper-search pubmed "<query>"   # if biomedical

3. Deep Read

For the most relevant papers (at least 3-5), read in depth:

paper outline <arxiv_id>           # understand structure first
paper skim <arxiv_id> --lines 3    # quick overview
paper read <arxiv_id> <section>    # read key sections

For web sources:

paper-search browse <url>

4. Follow the Citation Graph

For key papers, explore their context:

paper-search semanticscholar citations <paper_id> --limit 10    # who cites this?
paper-search semanticscholar references <paper_id> --limit 10   # what does it build on?
paper-search semanticscholar details <paper_id>                  # full metadata

5. Synthesize

Combine findings into a structured report with:

• Key findings and themes
• Areas of agreement/disagreement
• Gaps in the literature
• Citations for all claims (include paper titles and URLs)
• BibTeX entries for key papers (use paper bibtex <arxiv_id> to generate)

Guidelines

• Always start broad, then narrow. Don't read deeply until you've scanned widely.
• Read at least 3-5 primary sources before synthesizing.
• Cross-reference web sources against academic papers when possible.
• Use paper-search semanticscholar snippets to find specific evidence for claims.
• Track what you've already searched/read to avoid redundancy.
• If a search returns arxiv papers, use paper read to get the full text rather than just the snippet.