Agent skill
deepxiv
Search and progressively read open-access academic papers through DeepXiv. Use when the user wants layered paper access, section-level reading, trending papers, or DeepXiv-backed literature retrieval.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/deepxiv
SKILL.md
DeepXiv Paper Search & Progressive Reading
Search topic or paper ID: $ARGUMENTS
Role & Positioning
DeepXiv is the progressive-reading literature source:
| Skill | Best for |
|---|---|
/arxiv |
Direct preprint search and PDF download |
/deepxiv |
Layered reading: search → brief → head → section |
Use DeepXiv when you want to inspect papers incrementally instead of loading the full text immediately.
Constants
- FETCH_SCRIPT —
tools/deepxiv_fetch.pyrelative to the current project. If unavailable, fall back to the rawdeepxivCLI. - MAX_RESULTS = 10 — Default number of search results.
Overrides (append to arguments):
/deepxiv "agent memory" - max: 5/deepxiv "2409.05591" - brief/deepxiv "2409.05591" - head/deepxiv "2409.05591" - section: Introduction/deepxiv "trending" - days: 14 - max: 10/deepxiv "karpathy" - web/deepxiv "258001" - sc
Setup
DeepXiv is optional:
pip install deepxiv-sdk
On first use, deepxiv auto-registers a free token and stores it in ~/.env.
Workflow
Step 1: Parse Arguments
Parse $ARGUMENTS for:
- a paper topic, arXiv ID, or Semantic Scholar ID
- max: N- brief- head- section: NAME- trending- days: 7|14|30- web- sc
If the input looks like an arXiv ID and no explicit mode is provided, default to brief.
Step 2: Prefer the Adapter
python3 tools/deepxiv_fetch.py --help
If the adapter is unavailable, fall back to raw deepxiv commands.
Step 3: Execute the Minimal Command
python3 tools/deepxiv_fetch.py search "QUERY" --max MAX_RESULTS
python3 tools/deepxiv_fetch.py paper-brief ARXIV_ID
python3 tools/deepxiv_fetch.py paper-head ARXIV_ID
python3 tools/deepxiv_fetch.py paper-section ARXIV_ID "SECTION_NAME"
python3 tools/deepxiv_fetch.py trending --days 7 --max MAX_RESULTS
python3 tools/deepxiv_fetch.py wsearch "QUERY"
python3 tools/deepxiv_fetch.py sc "SEMANTIC_SCHOLAR_ID"
Fallbacks:
deepxiv search "QUERY" --limit MAX_RESULTS --format json
deepxiv paper ARXIV_ID --brief --format json
deepxiv paper ARXIV_ID --head --format json
deepxiv paper ARXIV_ID --section "SECTION_NAME" --format json
deepxiv trending --days 7 --limit MAX_RESULTS --output json
deepxiv wsearch "QUERY" --output json
deepxiv sc "SEMANTIC_SCHOLAR_ID" --output json
Step 4: Present Results
For search results, present a compact literature table. For paper reads, summarize the title, authors, date, TLDR, and the next recommended depth step.
Step 5: Escalate Depth Only When Needed
Use the progression:
searchpaper-briefpaper-headpaper-section
Only read the full paper when the user explicitly needs it.
Key Rules
- Prefer the adapter script over raw
deepxivcommands when available. - If DeepXiv is missing, give the install command and suggest
/arxivor/research-lit "topic" - sources: web. - Use DeepXiv as an additive source, not a replacement for existing ARIS literature tooling.
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