Auto-claude-code-research-in-sleep 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

source · Clone the upstream repo

git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/skills-codex/deepxiv" ~/.claude/skills/wanshuiyin-auto-claude-code-research-in-sleep-deepxiv-da8449 && rm -rf "$T"

manifest: skills/skills-codex/deepxiv/SKILL.md

source content

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:

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:

```
search
```
```
paper-brief
```
```
paper-head
```
```
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.