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Deepxiv

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wanshuiyin
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.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill namedeepxiv
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Deepxiv AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/deepxiv .claude/skills/deepxiv
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deepxiv in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deepxiv on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deepxiv is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DeepXiv Paper Search & Progressive Reading

Search topic or paper ID: $ARGUMENTS

Role & Positioning

DeepXiv is the progressive-reading literature source:

SkillSourceBest for
/arxivarXiv APIBatch search, PDF download, metadata
/deepxivDeepXiv SDKProgressive section-level reading
/semantic-scholarS2 APIPublished venue metadata, citation counts
/alphaxivalphaxiv.orgInstant LLM-optimized summary of one paper, with LaTeX source fallback

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

Constants

  • DEEPXIV_FETCHER — canonical name deepxiv_fetch.py, resolved per shared-references/integration-contract.md §2 (Policy D1 — primary + fallback cascade). If unresolved (canonical chain exhausted), fall back to the raw deepxiv CLI (documented per command below).
  • 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:

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:

  • 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

Resolve $DEEPXIV_FETCHER via the canonical strict-safe chain (see shared-references/integration-contract.md §2). Policy D1 cascade: the resolved adapter is preferred; if unresolved (canonical chain exhausted), fall back to raw deepxiv CLI commands documented in Step 3.

bash
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true
fi
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
    ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
DEEPXIV_FETCHER=".aris/tools/deepxiv_fetch.py"
[ -f "$DEEPXIV_FETCHER" ] || DEEPXIV_FETCHER="tools/deepxiv_fetch.py"
[ -f "$DEEPXIV_FETCHER" ] || { [ -n "${ARIS_REPO:-}" ] && DEEPXIV_FETCHER="$ARIS_REPO/tools/deepxiv_fetch.py"; }
[ -f "$DEEPXIV_FETCHER" ] || DEEPXIV_FETCHER=""

# Smoke test (optional — adapter resolution shown to user). The cascade
# in Step 3 below branches purely on `[ -n "$DEEPXIV_FETCHER" ]`; a
# resolved-but-non-functional adapter is not currently auto-demoted.
if [ -n "$DEEPXIV_FETCHER" ]; then
  echo "DeepXiv adapter resolved at: $DEEPXIV_FETCHER" >&2
else
  echo "DeepXiv adapter unresolved (canonical chain exhausted); raw deepxiv CLI fallback will be used." >&2
fi

Step 3: Execute the Minimal Command

Search papers

bash
python3 "$DEEPXIV_FETCHER" search "QUERY" --max MAX_RESULTS

Fallback:

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

Brief summary

bash
python3 "$DEEPXIV_FETCHER" paper-brief ARXIV_ID

Fallback:

bash
deepxiv paper ARXIV_ID --brief --format json

Section map

bash
python3 "$DEEPXIV_FETCHER" paper-head ARXIV_ID

Fallback:

bash
deepxiv paper ARXIV_ID --head --format json

Specific section

bash
python3 "$DEEPXIV_FETCHER" paper-section ARXIV_ID "SECTION_NAME"

Fallback:

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

Trending

bash
python3 "$DEEPXIV_FETCHER" trending --days 7 --max MAX_RESULTS

Fallback:

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

Web search

bash
python3 "$DEEPXIV_FETCHER" wsearch "QUERY"

Fallback:

bash
deepxiv wsearch "QUERY" --output json

Semantic Scholar metadata

bash
python3 "$DEEPXIV_FETCHER" sc "SEMANTIC_SCHOLAR_ID"

Fallback:

bash
deepxiv sc "SEMANTIC_SCHOLAR_ID" --output json

Step 4: Present Results

When searching, present a compact table:

text
| # | 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. When the wiki dir exists, resolve $WIKI_SCRIPT per the canonical chain at shared-references/wiki-helper-resolution.md (Variant B — warn-and-skip). Ingest papers that were meaningfully read (brief / head / section / full) during this invocation — mere search hits without a depth read do not need ingestion:

bash
if [ -d research-wiki/ ]; then
  cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
  ARIS_REPO="${ARIS_REPO:-$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null)}"
  if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
    ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
  fi
  WIKI_SCRIPT=".aris/tools/research_wiki.py"
  [ -f "$WIKI_SCRIPT" ] || WIKI_SCRIPT="tools/research_wiki.py"
  [ -f "$WIKI_SCRIPT" ] || { [ -n "${ARIS_REPO:-}" ] && WIKI_SCRIPT="$ARIS_REPO/tools/research_wiki.py"; }
  [ -f "$WIKI_SCRIPT" ] || {
    echo "WARN: research_wiki.py not found; depth-read summary delivered, wiki ingest skipped. Fix: bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or cp <ARIS-repo>/tools/research_wiki.py tools/." >&2
    WIKI_SCRIPT=""
  }
  if [ -n "$WIKI_SCRIPT" ]; then
    for each arxiv_id the user asked this skill to read in depth:
        python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
            --arxiv-id "<arxiv_id>"
  fi
fi

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 "$WIKI_SCRIPT" sync research-wiki/ --arxiv-ids <id1>,<id2>,... after resolving $WIKI_SCRIPT as above.

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.

Frequently asked questions

What does the Deepxiv AI skill do?

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.

Why use Deepxiv on TypingMind?

Because you install it once and use it with any model. Deepxiv is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deepxiv in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/deepxiv. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deepxiv?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deepxiv?

As many as you like. As long as a model supports skills, you can use Deepxiv with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deepxiv AI skill free?

Yes. It is published on GitHub by wanshuiyin under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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