alphaxiv（alphaxiv 搜索）

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:

Do NOT use this skill for topic discovery, broad literature search, or multi-paper surveys — use /research-lit or /arxiv 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): - /alphaxiv 2401.12345 — quick overview - /alphaxiv "https://arxiv.org/abs/2401.12345" — auto-extract ID - /alphaxiv 2401.12345 - depth: src — force LaTeX source inspection - /alphaxiv 2401.12345 - depth: abs — force full markdown

Workflow

Step 1: Parse Arguments & Extract Paper ID

Parse $ARGUMENTS to extract a bare arXiv paper ID. Accept these input formats:

• https://arxiv.org/abs/2401.12345 or https://arxiv.org/abs/2401.12345v2
• https://arxiv.org/pdf/2401.12345
• https://alphaxiv.org/overview/2401.12345
• https://alphaxiv.org/abs/2401.12345
• 2401.12345 or 2401.12345v2

Strip version suffixes (v1, v2,...) for API calls. Store as PAPER_ID.

Parse optional directives:

• - depth: overview|abs|src: force a specific tier instead of cascading

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 .tar.gz archive. Download it to a temporary directory, extract it, and list the .tex files inside.

Then inspect only the files needed to answer the question. Prioritize:

1. Top-level *.tex files (usually the main document)
2. Files referenced by \input{} or \include{}
3. 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 /tmp. Do not rely on persistence.

Step 5: Present Results

Default Answer Shape

## [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

/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 research-wiki/ 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 papers/<slug>.md. 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: overview is the fastest path and must always be tried before deeper tiers. Only escalate when needed.
• Minimal reads: At src 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 /arxiv 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 /deepxiv as alternative.
• Complementary, not competing: This skill complements /arxiv (search + download) and /deepxiv (progressive reading). Do not re-implement their functionality.

Integration with Other Skills

As enrichment in /research-lit

/research-lit 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 /research-lit, /novelty-check, or /idea-discovery surface a specific paper, users can invoke /alphaxiv PAPER_ID for a fast deep-dive without re-running the full survey.