Semantic Scholar Agent Skill

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.
• S2_FETCHER — canonical name semantic_scholar_fetch.py, resolved per

shared-references/integration-contract.md §2

(Codex-side chain: $ARIS_REPO/tools/ → tools/ → ~/.codex/skills/semantic-scholar/).

Policy D1 — if unresolved (canonical chain exhausted), fall back to inline Python.

• 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

Resolve $S2_FETCHER via the canonical strict-safe Codex chain

(see shared-references/integration-contract.md §2):

if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
S2_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/semantic_scholar_fetch.py" ] && S2_FETCHER="$ARIS_REPO/tools/semantic_scholar_fetch.py"
[ -z "$S2_FETCHER" ] && [ -f tools/semantic_scholar_fetch.py ] && S2_FETCHER="tools/semantic_scholar_fetch.py"
[ -z "$S2_FETCHER" ] && [ -f ~/.codex/skills/semantic-scholar/semantic_scholar_fetch.py ] && S2_FETCHER="$HOME/.codex/skills/semantic-scholar/semantic_scholar_fetch.py"

Standard search (default — relevance-ranked):

[ -n "$S2_FETCHER" ] && python3 "$S2_FETCHER" 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):

[ -n "$S2_FETCHER" ] && python3 "$S2_FETCHER" 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:

[ -n "$S2_FETCHER" ] && python3 "$S2_FETCHER" 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: 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/ ]:
    WIKI_SCRIPT=""
    [ -n "$ARIS_REPO" ] && [ -f "$ARIS_REPO/tools/research_wiki.py" ] && WIKI_SCRIPT="$ARIS_REPO/tools/research_wiki.py"
    [ -z "$WIKI_SCRIPT" ] && [ -f tools/research_wiki.py ] && WIKI_SCRIPT="tools/research_wiki.py"
    [ -z "$WIKI_SCRIPT" ] && [ -f ~/.codex/skills/research-wiki/research_wiki.py ] && WIKI_SCRIPT="$HOME/.codex/skills/research-wiki/research_wiki.py"
    for each paper in results:
        if paper.externalIds.ArXiv:
            [ -n "$WIKI_SCRIPT" ] && python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
                --arxiv-id "<ArXiv>"
        else:
            [ -n "$WIKI_SCRIPT" ] && python3 "$WIKI_SCRIPT" 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:

/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.