Arxiv

arXiv Paper Search & Download

Search topic or arXiv paper ID: $ARGUMENTS

Constants

• PAPER_DIR - Local directory to save downloaded PDFs. Default: papers/ in the current project directory.
• MAX_RESULTS = 10 - Default number of search results.
• FETCH_SCRIPT - tools/arxiv_fetch.py relative to the ARIS install, or the same path relative to the current project. Fall back to inline Python if not found.

Overrides (append to arguments):

• /arxiv "attention mechanism" - max: 20 - return up to 20 results
• /arxiv "2301.07041" - download - download a specific paper by ID
• /arxiv "query" - dir: literature/ - save PDFs to a custom directory
• /arxiv "query" - download: all - download all result PDFs

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS for directives:

• Query or ID: main search term or a bare arXiv ID such as 2301.07041 or cs/0601001
• - max: N: override MAX_RESULTS (e.g., - max: 20)
• - dir: PATH: override PAPER_DIR (e.g., - dir: literature/)
• - download: download the first result's PDF after listing
• - download: all: download PDFs for all results

If the argument matches an arXiv ID pattern (YYMM.NNNNN or category/NNNNNNN), skip the search and go directly to Step 3.

Step 2: Search arXiv

Locate the fetch script:

SCRIPT=$(python3 -c "
import pathlib
candidates = [
    pathlib.Path('tools/arxiv_fetch.py'),
    pathlib.Path.home() / '.claude' / 'skills' / 'arxiv' / 'arxiv_fetch.py',
]
for p in candidates:
    if p.exists():
        print(p)
        break
" 2>/dev/null)

If SCRIPT is found, run:

python3 "$SCRIPT" search "QUERY" --max MAX_RESULTS

If SCRIPT is not found, fall back to inline Python:

python3 - <<'PYEOF'
import json
import urllib.parse
import urllib.request
import xml.etree.ElementTree as ET

NS = "http://www.w3.org/2005/Atom"
query = urllib.parse.quote("QUERY")
url = (f"http://export.arxiv.org/api/query"
       f"?search_query={query}&start=0&max_results=MAX_RESULTS"
       f"&sortBy=relevance&sortOrder=descending")
with urllib.request.urlopen(url, timeout=30) as r:
    root = ET.fromstring(r.read())
papers = []
for entry in root.findall(f"{{{NS}}}entry"):
    aid = entry.findtext(f"{{{NS}}}id", "").split("/abs/")[-1].split("v")[0]
    title = (entry.findtext(f"{{{NS}}}title", "") or "").strip().replace("\n", " ")
    abstract = (entry.findtext(f"{{{NS}}}summary", "") or "").strip().replace("\n", " ")
    authors = [a.findtext(f"{{{NS}}}name", "") for a in entry.findall(f"{{{NS}}}author")]
    published = entry.findtext(f"{{{NS}}}published", "")[:10]
    cats = [c.get("term", "") for c in entry.findall(f"{{{NS}}}category")]
    papers.append({
        "id": aid,
        "title": title,
        "authors": authors,
        "abstract": abstract,
        "published": published,
        "categories": cats,
        "pdf_url": f"https://arxiv.org/pdf/{aid}.pdf",
        "abs_url": f"https://arxiv.org/abs/{aid}",
    })
print(json.dumps(papers, ensure_ascii=False, indent=2))
PYEOF

Present results as a table:

| # | arXiv ID   | Title               | Authors        | Date       | Category |
|---|------------|---------------------|----------------|------------|----------|
| 1 | 2301.07041 | Attention Is All... | Vaswani et al. | 2017-06-12 | cs.LG    |

Step 3: Fetch Details for a Specific ID

When a single paper ID is requested (either directly or from Step 2):

python3 "$SCRIPT" search "id:ARXIV_ID" --max 1
# or fallback:
python3 -c "
import urllib.request, xml.etree.ElementTree as ET
NS = 'http://www.w3.org/2005/Atom'
url = 'http://export.arxiv.org/api/query?id_list=ARXIV_ID'
with urllib.request.urlopen(url, timeout=30) as r:
    root = ET.fromstring(r.read())
# print full details ...
"

Display: title, all authors, categories, full abstract, published date, PDF URL, abstract URL.

Step 4: Download PDFs

When download is requested, for each paper ID to download:

# Using fetch script:
python3 "$SCRIPT" download ARXIV_ID --dir PAPER_DIR

# Fallback:
mkdir -p PAPER_DIR && python3 -c "
import pathlib
import sys
import urllib.request

out = pathlib.Path('PAPER_DIR/ARXIV_ID.pdf')
if out.exists():
    print(f'Already exists: {out}')
    sys.exit(0)
req = urllib.request.Request(
    'https://arxiv.org/pdf/ARXIV_ID.pdf',
    headers={'User-Agent': 'arxiv-skill/1.0'},
)
with urllib.request.urlopen(req, timeout=60) as r:
    out.write_bytes(r.read())
print(f'Downloaded: {out} ({out.stat().st_size // 1024} KB)')
"

After each download:

• Confirm file size > 10 KB (reject smaller files - likely an error HTML page)
• Add a 1-second delay between consecutive downloads to avoid rate limiting
• Report: Downloaded: papers/2301.07041.pdf (842 KB)

Step 5: Summarize

For each paper (downloaded or fetched by API):

## [Title]

- **arXiv**: [ID] - [abs_url]
- **Authors**: [full author list]
- **Date**: [published]
- **Categories**: [cs.LG, cs.AI, ...]
- **Abstract**: [full abstract]
- **Key contributions** (extracted from abstract):
  - [contribution 1]
  - [contribution 2]
  - [contribution 3]
- **Local PDF**: papers/[ID].pdf (if downloaded)

Step 6: Final Output

Summarize what was done:

• Found N papers for "query"
• Downloaded: papers/2301.07041.pdf (842 KB) (for each download)
• Any warnings (rate limit hit, file too small, already exists)

Suggest follow-up skills:

/research-lit "topic"     - multi-source review: Zotero + Obsidian + local PDFs + web
/novelty-check "idea"     - verify your idea is novel against these papers

Key Rules

• Always show the arXiv ID prominently - users need it for citations and reproducibility
• Verify downloaded PDFs: file must be > 10 KB; warn and delete if smaller
• Rate limit: wait 1 second between consecutive PDF downloads; retry once after 5 seconds on HTTP 429
• Never overwrite an existing PDF at the same path - skip it and report "already exists"
• Handle both arXiv ID formats: new (2301.07041) and old (cs/0601001)
• PAPER_DIR is created automatically if it does not exist
• If the arXiv API is unreachable, report the error clearly and suggest using /research-lit with - sources: web as a fallback