AlphaXiv Paper Lookup
Lookup paper: $ARGUMENTS
Quick single-paper reader with tiered source fallback (overview → full markdown → LaTeX source). Powered by
AlphaXiv.
Role & Positioning
This skill is the quick single-paper reader that returns LLM-optimized summaries:
| Skill | Source | Best for |
|---|
| arXiv API | Batch search, PDF download, metadata |
| DeepXiv SDK | Progressive section-level reading |
| S2 API | Published venue metadata, citation counts |
| alphaxiv.org | Instant LLM-optimized summary of one paper, with LaTeX source fallback |
Do NOT use this skill for topic discovery, broad literature search, or multi-paper surveys — use
or
instead.
Constants
- OVERVIEW_URL =
https://alphaxiv.org/overview/{PAPER_ID}.md
- ABS_URL =
https://alphaxiv.org/abs/{PAPER_ID}.md
- ARXIV_SRC_URL =
https://arxiv.org/src/{PAPER_ID}
Overrides (append to arguments):
- — quick overview
/alphaxiv "https://arxiv.org/abs/2401.12345"
/alphaxiv 2401.12345 - depth: src
/alphaxiv 2401.12345 - depth: abs
Workflow
Step 1: Parse Arguments & Extract Paper ID
Parse
to extract a bare arXiv paper ID. Accept these input formats:
https://arxiv.org/abs/2401.12345
https://arxiv.org/abs/2401.12345v2
https://arxiv.org/pdf/2401.12345
https://alphaxiv.org/overview/2401.12345
https://alphaxiv.org/abs/2401.12345
- or
Strip version suffixes (
,
, ...) for API calls. Store as
.
Parse optional directives:
- depth: overview|abs|src
Step 2: Fetch AlphaXiv Overview (Tier 1 — Fastest)
Fetch the structured overview from
https://alphaxiv.org/overview/{PAPER_ID}.md
.
This returns a structured, LLM-optimized report designed for machine consumption. Use this as the default and preferred source.
If the overview answers the user's question, stop here. Do not fetch deeper tiers unnecessarily.
If the request fails (HTTP 404 — paper not yet processed) or the content is insufficient, proceed to Step 3.
Step 3: Fetch Full AlphaXiv Markdown (Tier 2 — More Detail)
Fetch the full paper markdown from
https://alphaxiv.org/abs/{PAPER_ID}.md
.
This provides the full paper body as markdown. Use when the user needs:
- Specific methodology details
- Detailed experimental results
- Particular sections not covered in the overview
If this still does not answer the question, proceed to Step 4.
Step 4: Fetch arXiv LaTeX Source (Tier 3 — Deepest)
When the overview and full markdown are both insufficient (e.g., the user asks about equations, proofs, appendix details, or implementation specifics), download the paper's LaTeX source from
https://arxiv.org/src/{PAPER_ID}
.
The source is a
archive. Download it to a temporary directory, extract it, and list the
files inside.
Then inspect only the files needed to answer the question. Prioritize:
- Top-level files (usually the main document)
- Files referenced by or
- Appendices, tables, or sections directly related to the user's question
Do NOT read the entire source tree by default. Read selectively.
Temporary source artifacts live under
. Do not rely on persistence.
Step 5: Present Results
Default Answer Shape
markdown
## [Paper Title]
- **arXiv**: [PAPER_ID] — https://arxiv.org/abs/[PAPER_ID]
- **Source depth**: overview | abs | src
### Summary
[2-3 sentence summary]
### Key Points
- [point 1]
- [point 2]
- [point 3]
### Answer to Your Question
[Direct answer if the user asked a specific question]
If the user only asks for one specific detail, answer it directly — skip the full template.
Suggest Follow-Up Skills
text
/arxiv "PAPER_ID" - download - download the PDF to local library
/deepxiv "PAPER_ID" - section: Methods - read a specific section progressively
/research-lit "related topic" - multi-source literature survey
/novelty-check "idea from paper" - verify novelty against this paper's area
Update Research Wiki (if active)
Required when exists in the project; skip silently
otherwise. After presenting the paper summary, ingest the single paper
that was read:
if [ -d research-wiki/ ]:
python3 tools/research_wiki.py ingest_paper research-wiki/ \
--arxiv-id "<paper_arxiv_id>" \
[--thesis "<one-line thesis from the Tier 1 overview>"]
The helper handles metadata fetch, slug, dedup, page creation, index
rebuild, and log append —
do not handwrite . See
shared-references/integration-contract.md
.
If wiki was not present at read time, the user can backfill via
python3 tools/research_wiki.py sync research-wiki/ --arxiv-ids <id>
.
Key Rules
- Overview first: is the fastest path and must always be tried before deeper tiers. Only escalate when needed.
- Minimal reads: At tier, read only the files that answer the question. Full-tree reads waste tokens.
- Cross-platform: When downloading and extracting the source archive, prefer cross-platform approaches (e.g., Python stdlib) over platform-specific commands to ensure Windows/WSL compatibility.
- No PDF parsing: This skill reads structured markdown and LaTeX source, not raw PDFs. For PDF content, suggest with download.
- Rate limiting: arXiv source download may rate-limit. If HTTP 429 occurs, wait 5 seconds and retry once. If still blocked, report the error and suggest as alternative.
- Complementary, not competing: This skill complements (search + download) and (progressive reading). Do not re-implement their functionality.
Integration with Other Skills
As enrichment in
can use this skill's Tier 1 (overview) as a fast enrichment step between search and deep analysis. After finding arXiv papers in Step 1, fetch AlphaXiv overviews to quickly assess relevance before committing to full-text reads:
Step 1: Search → list of arXiv IDs
Step 1.5: AlphaXiv overview for top 5-8 papers (this skill, Tier 1 only)
Step 2: Deep analysis only for papers that pass the relevance filter
This saves significant tokens by filtering out marginally relevant papers before deep reading.
As follow-up from other skills
After
,
, or
surface a specific paper, users can invoke
for a fast deep-dive without re-running the full survey.