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deepxivdeepxiv 搜索

Agent Skill

deepxiv 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

912

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:deepxiv(deepxiv 搜索)
来源仓库:https://github.com/wanshuiyin/auto-claude-code-research-in-sleep
仓库路径:skills/deepxiv
安装命令:
npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill deepxiv
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill deepxiv

简介

DeepXiv 提供论文渐进式阅读能力,从摘要直达章节细粒度解析。

  • 支持 arXiv/Semantic Scholar 检索,自动下载 PDF 并提取元数据。
  • 分层输出:brief→headings→sections,辅以趋势分析与 web 搜索增强。
  • 默认限制单次返回 10 条结果,超出需显式指定 MAX_RESULTS 参数。
  • deepxiv 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

DeepXiv Paper Search & Progressive Reading

Search topic or paper ID: $ARGUMENTS

Role & Positioning

DeepXiv is the progressive-reading literature source:

SkillBest for
/arxivDirect preprint search and PDF download
/semantic-scholarPublished venue metadata, citation counts, DOI links
/deepxivLayered reading: search → brief → head → section, plus trending and web search

Use DeepXiv when you want to avoid loading full papers too early.

Constants

  • FETCH_SCRIPTtools/deepxiv_fetch.py relative to the current project. If unavailable, fall back to the raw deepxiv CLI.
  • MAX_RESULTS = 10 — Default number of results to return.
Overrides (append to arguments): - /deepxiv "agent memory" - max: 5 — top 5 results - /deepxiv "2409.05591" - brief — quick paper summary - /deepxiv "2409.05591" - head — metadata + section overview - /deepxiv "2409.05591" - section: Introduction — read one section only - /deepxiv "trending" - days: 14 - max: 10 — trending papers - /deepxiv "karpathy" - web — DeepXiv web search - /deepxiv "258001" - sc — Semantic Scholar metadata by ID

Setup

DeepXiv is optional. If the CLI is not installed, tell the user:

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:

  • Query or ID: a paper topic, arXiv ID, or Semantic Scholar ID
  • - max: N: override MAX_RESULTS
  • - brief: fetch paper brief
  • - head: fetch metadata and section map
  • - section: NAME: fetch one named section
  • - trending or query trending: fetch trending papers
  • - days: 7|14|30: trending time window
  • - web: run DeepXiv web search
  • - sc: fetch Semantic Scholar metadata by ID

If the main argument looks like an arXiv ID and no explicit mode is given, default to - brief.

Step 2: Locate the Adapter

Prefer the ARIS adapter:

python3 tools/deepxiv_fetch.py --help

If tools/deepxiv_fetch.py is not available, fall back to raw deepxiv commands.

Step 3: Execute the Minimal Command

Search papers

python3 tools/deepxiv_fetch.py search "QUERY" --max MAX_RESULTS

Fallback:

deepxiv search "QUERY" --limit MAX_RESULTS --format json

Brief summary

python3 tools/deepxiv_fetch.py paper-brief ARXIV_ID

Fallback:

deepxiv paper ARXIV_ID --brief --format json

Section map

python3 tools/deepxiv_fetch.py paper-head ARXIV_ID

Fallback:

deepxiv paper ARXIV_ID --head --format json

Specific section

python3 tools/deepxiv_fetch.py paper-section ARXIV_ID "SECTION_NAME"

Fallback:

deepxiv paper ARXIV_ID --section "SECTION_NAME" --format json

Trending

python3 tools/deepxiv_fetch.py trending --days 7 --max MAX_RESULTS

Fallback:

deepxiv trending --days 7 --limit MAX_RESULTS --output json

Web search

python3 tools/deepxiv_fetch.py wsearch "QUERY"

Fallback:

deepxiv wsearch "QUERY" --output json

Semantic Scholar metadata

python3 tools/deepxiv_fetch.py sc "SEMANTIC_SCHOLAR_ID"

Fallback:

deepxiv sc "SEMANTIC_SCHOLAR_ID" --output json

Step 4: Present Results

When searching, present a compact table:

| # | ID | Title | Year | Citations | Notes |
|---|----|-------|------|-----------|-------|

When reading a paper, show:

  • title
  • arXiv ID
  • authors
  • venue/date if available
  • TLDR or abstract summary
  • suggested next step: briefheadsection

Step 5: Escalate Depth Only When Needed

Use this progression:

  1. search
  2. paper-brief
  3. paper-head
  4. paper-section
  5. full paper only if necessary

Do not jump to full-paper reads when a brief or one section answers the question.

Step 6: Update Research Wiki (if active)

Required when research-wiki/ exists in the project; skip silently otherwise. Ingest papers that were meaningfully read (brief / head / section / full) during this invocation — mere search hits without a depth read do not need ingestion:

if [ -d research-wiki/ ]:
    for each arxiv_id the user asked this skill to read in depth:
        python3 tools/research_wiki.py ingest_paper research-wiki/ \
            --arxiv-id "<arxiv_id>"

The helper handles metadata / slug / dedup / page / index / log in one call — do not handwrite papers/<slug>.md. See shared-references/integration-contract.md. Backfill missed ingests with python3 tools/research_wiki.py sync research-wiki/ --arxiv-ids <id1>,<id2>,....

Key Rules

  • Prefer the adapter script over raw deepxiv commands when available.
  • DeepXiv is optional. If unavailable, give the install command and suggest /arxiv or /research-lit "topic" - sources: web.
  • Use section-level reads to save tokens.
  • Treat DeepXiv as complementary to /arxiv and /semantic-scholar, not a replacement.
  • If the result overlaps with a published venue paper from Semantic Scholar, keep the richer venue metadata in the final summary.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

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能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude

32.24%
按下载量换算98

Codex

31.55%
按下载量换算96

Cursor

20.12%
按下载量换算61

Gemini CLI

10.12%
按下载量换算31

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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