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name semantic-scholar
description Search published venue papers (IEEE, ACM, Springer, etc.) via Semantic Scholar API. Complements /arxiv (preprints) with citation counts, venue metadata, and TLDR. Use when user says "search semantic scholar", "find IEEE papers", "find journal papers", "venue papers", "citation search", or wants published literature beyond arXiv preprints.
argument-hint query-or-paper-id
allowed-tools Bash(*), Read, Write

Semantic Scholar Paper Search

Search topic or paper ID: $ARGUMENTS

Role & Positioning

This skill is the published venue counterpart to /arxiv:

Skill Source Best for
/arxiv arXiv API Latest preprints, cutting-edge unrefereed work
/semantic-scholar Semantic Scholar API Published journal/conference papers (IEEE, ACM, Springer, etc.) with citation counts, venue info, TLDR

Do NOT duplicate arXiv's job. If results contain an externalIds.ArXiv field, the paper is also on arXiv — note this but do not re-fetch from arXiv.

Constants

  • MAX_RESULTS = 10 — Default number of search results.
  • FETCH_SCRIPT — tools/semantic_scholar_fetch.py relative to the project root. Fall back to inline Python if not found.
  • DEFAULT_FILTERS — For general research queries, apply these by default to reduce noise:
    • --fields-of-study "Computer Science,Engineering"
    • --publication-types JournalArticle,Conference

Overrides (append to arguments):

  • /semantic-scholar "topic" - max: 20 — return up to 20 results
  • /semantic-scholar "topic" - type: journal — only journal articles
  • /semantic-scholar "topic" - type: conference — only conference papers
  • /semantic-scholar "topic" - min-citations: 50 — only highly-cited papers
  • /semantic-scholar "topic" - year: 2022- — papers from 2022 onward
  • /semantic-scholar "topic" - fields: all — remove default field-of-study filter
  • /semantic-scholar "topic" - sort: citations — bulk search sorted by citation count
  • /semantic-scholar "DOI:10.1109/..." — fetch a single paper by DOI

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS for directives:

  • Query or ID: main search term, or a paper identifier:
    • DOI: 10.1109/TWC.2024.1234567
    • Semantic Scholar ID: f9314fd99be5f2b1b3efcfab87197d578160d553
    • ArXiv: ARXIV:2006.10685
    • Corpus: CorpusId:219792180
  • - max: N: override MAX_RESULTS
  • - type: journal|conference|review|all: map to --publication-types
  • - min-citations: N: map to --min-citations
  • - year: RANGE: map to --year (e.g. 2022-, 2020-2024)
  • - fields: FIELDS: override --fields-of-study (use all to remove filter)
  • - sort: citations|date: use search-bulk with --sort citationCount:desc or publicationDate:desc

If the argument matches a DOI pattern (10.XXXX/...), a Semantic Scholar ID (40-char hex), or a prefixed ID (ARXIV:..., CorpusId:...), skip search and go directly to Step 3.

Step 2: Search Papers

Locate the fetch script:

SCRIPT=$(find tools/ -name "semantic_scholar_fetch.py" 2>/dev/null | head -1)
[ -z "$SCRIPT" ] && SCRIPT=$(find ~/.claude/skills/semantic-scholar/ -name "semantic_scholar_fetch.py" 2>/dev/null | head -1)

Standard search (default — relevance-ranked):

python3 "$SCRIPT" search "QUERY" --max MAX_RESULTS \
  --fields-of-study "Computer Science,Engineering" \
  --publication-types JournalArticle,Conference

Bulk search (when - sort: is specified, or MAX_RESULTS > 100):

python3 "$SCRIPT" search-bulk "QUERY" --max MAX_RESULTS \
  --sort citationCount:desc \
  --fields-of-study "Computer Science" \
  --year "2020-"

If semantic_scholar_fetch.py is not found, fall back to inline Python using urllib against https://api.semanticscholar.org/graph/v1/paper/search.

Recommended filter combos (from testing):

Goal Flags
High-quality journal papers --publication-types JournalArticle --min-citations 10
CS/EE papers, recent --fields-of-study "Computer Science,Engineering" --year "2022-"
Foundational / high-impact search-bulk --sort citationCount:desc --fields-of-study "Computer Science"
Conference papers only --publication-types Conference

Note: --venue requires exact venue names (e.g. "IEEE Transactions on Signal Processing"), not partial matches like "IEEE". Avoid using --venue in automated flows — prefer --publication-types + --fields-of-study.

Step 3: Fetch Details for a Specific Paper

When a single paper ID is requested:

python3 "$SCRIPT" paper "PAPER_ID"

Where PAPER_ID can be:

  • DOI: 10.1109/TSP.2021.3071210
  • ArXiv: ARXIV:2006.10685
  • CorpusId: CorpusId:219792180
  • S2 ID: f9314fd99be5f2b1b3efcfab87197d578160d553

Step 4: De-duplicate Against arXiv

For each result, check externalIds.ArXiv:

  • If present → paper is also on arXiv. Note this in output but do NOT re-fetch via /arxiv.
  • If absent → paper is venue-only (e.g. IEEE without preprint). This is the unique value of this skill.

Step 5: Present Results

Present results as a table:

| # | Title | Venue | Year | Citations | Authors | Type |
|---|-------|-------|------|-----------|---------|------|
| 1 | Deep Learning Enabled... | IEEE Trans. Signal Process. | 2021 | 1364 | Xie et al. | Journal |

For each paper, also show:

  • DOI link: https://doi.org/DOI (for IEEE/ACM papers, this is the canonical link)
  • Open Access PDF: if openAccessPdf.url is non-empty, show it
  • TLDR: if available, show the one-line summary
  • Also on arXiv: if externalIds.ArXiv exists, note the arXiv ID

Step 6: Detailed Summary

For each paper (or top 5 if many results):

## [Title]

- **Venue**: [venue name] ([publicationVenue.type]: journal/conference)
- **Year**: [year] | **Citations**: [citationCount]
- **Authors**: [full author list]
- **DOI**: [doi link]
- **Fields**: [fieldsOfStudy]
- **TLDR**: [tldr.text if available]
- **Abstract**: [abstract]
- **Open Access**: [openAccessPdf.url or "Not available"]
- **Also on arXiv**: [ArXiv ID if exists, else "No"]

Step 7: Final Output

Summarize what was done:

  • Found N published papers for "query"
  • Filters applied: [publication types, fields, year range, etc.]
  • N papers are venue-only (not on arXiv)

Suggest follow-up skills:

/arxiv "topic"           - search arXiv preprints (complements this search)
/research-lit "topic"    - multi-source review: Zotero + local PDFs + arXiv + S2
/novelty-check "idea"    - verify novelty against literature

Key Rules

  • Default to filtered search: Always apply --fields-of-study and --publication-types unless user says - fields: all. Without filters, S2 returns cross-discipline noise (linguistics, psychology, etc.).
  • Citation count is gold: S2's citation data is its main advantage over arXiv. Always show citationCount prominently and use it to rank/prioritize results.
  • Venue metadata matters: Show venue and publicationVenue.type (journal vs conference) — this helps users assess paper quality.
  • DOI is the canonical ID for published papers: Always show DOI links for IEEE/ACM/Springer papers.
  • Rate limiting: S2 API without key is heavily rate-limited (~1 req/s, strict cooldown). If HTTP 429 occurs, wait and retry. Recommend users set SEMANTIC_SCHOLAR_API_KEY env var for higher limits (free at https://www.semanticscholar.org/product/api#api-key-form).
  • TLDR may be null: Some publishers (notably IEEE) elide the TLDR field. Fall back to showing the first sentence of the abstract.
  • openAccessPdf may be empty: Many IEEE papers are closed access. Always provide the DOI link as fallback.
  • If the S2 API is unreachable, suggest using /arxiv or /research-lit "topic" - sources: web as fallback.
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