Semantic Scholar Paper Search

Search topic or paper ID: $ARGUMENTS

Role & Positioning

This skill targets published venue scholarship:

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):

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 the preprint.
• If absent → paper is venue-only (e.g. a journal article or book without a 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: Update Research Wiki (if active)

Required when research-wiki/ exists in the project; skip silently otherwise. Ingest the papers presented to the user. For results with an externalIds.ArXiv field, use --arxiv-id; for venue-only papers (no arXiv mirror — common for IEEE/ACM), fall back to manual metadata:

if [ -d research-wiki/ ]:
    for each paper in results:
        if paper.externalIds.ArXiv:
            python3 tools/research_wiki.py ingest_paper research-wiki/ \
                --arxiv-id "<ArXiv>"
        else:
            python3 tools/research_wiki.py ingest_paper research-wiki/ \
                --title "<title>" --authors "<authors joined by , >" \
                --year <year> --venue "<venue>" \
                [--external-id-doi "<externalIds.DOI>"]

The helper handles slug / dedup / page / index / log — do not handwrite papers/<slug>.md. See shared-references/integration-contract.md. Backfill with /research-wiki sync --arxiv-ids <id1>,<id2>,... for arXiv-available papers.

Step 8: 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)
• Wiki-ingested N papers (if research-wiki/ was present)

Suggest follow-up skills:

/research-lit "topic"    - multi-source review: Zotero + local PDFs + web + 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 /research-lit "topic" - sources: web as fallback.