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.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/skills-codex/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.py relative to the current project. If unavailable, fall back to the raw deepxiv CLI.
  • 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:

bash
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

bash
python3 tools/deepxiv_fetch.py --help

If the adapter is unavailable, fall back to raw deepxiv commands.

Step 3: Execute the Minimal Command

bash
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:

bash
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:

  1. search
  2. paper-brief
  3. paper-head
  4. paper-section

Only read the full paper when the user explicitly needs it.

Key Rules

  • Prefer the adapter script over raw deepxiv commands when available.
  • If DeepXiv is missing, give the install command and suggest /arxiv or /research-lit "topic" - sources: web.
  • Use DeepXiv as an additive source, not a replacement for existing ARIS literature tooling.

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