deepxiv skill
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.
Is the deepxiv skill safe?
Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.
No findings.
Install the deepxiv skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
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/skills-codex/deepxiv ~/.claude/skills/deepxiv
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
DeepXiv Paper Search & Progressive Reading
Search topic or paper ID: $ARGUMENTS
Role & Positioning
DeepXiv is the progressive-reading literature source:
Use DeepXiv when you want to inspect papers incrementally instead of loading the full text immediately.
Constants
shared-references/integration-contract.md §2 (Codex-side chain: $ARIS_REPO/tools/ → tools/ → ~/.codex/skills/deepxiv/). Policy D1 — if unresolved (canonical chain exhausted), fall back to raw deepxiv CLI.
- DEEPXIVFETCHER — canonical name deepxivfetch.py, resolved per
- MAXRESULTS = 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-sdkOn 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: Locate the Adapter
Resolve $DEEPXIV_FETCHER via the canonical strict-safe Codex chain (see shared-references/integration-contract.md §2):
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
DEEPXIV_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/deepxiv_fetch.py" ] && DEEPXIV_FETCHER="$ARIS_REPO/tools/deepxiv_fetch.py"
[ -z "$DEEPXIV_FETCHER" ] && [ -f tools/deepxiv_fetch.py ] && DEEPXIV_FETCHER="tools/deepxiv_fetch.py"
[ -z "$DEEPXIV_FETCHER" ] && [ -f ~/.codex/skills/deepxiv/deepxiv_fetch.py ] && DEEPXIV_FETCHER="$HOME/.codex/skills/deepxiv/deepxiv_fetch.py"
# Smoke test (optional): resolved-but-non-functional adapter is not currently auto-demoted.
if [ -n "$DEEPXIV_FETCHER" ]; then
echo "DeepXiv adapter resolved at: $DEEPXIV_FETCHER" >&2
else
echo "DeepXiv adapter unresolved (canonical chain exhausted); raw deepxiv CLI fallback will be used." >&2
fiIf the adapter is unresolved, fall back to raw deepxiv commands.
Step 3: Execute the Minimal Command
[ -n "$DEEPXIV_FETCHER" ] && python3 "$DEEPXIV_FETCHER" search "QUERY" --max MAX_RESULTS
[ -n "$DEEPXIV_FETCHER" ] && python3 "$DEEPXIV_FETCHER" paper-brief ARXIV_ID
[ -n "$DEEPXIV_FETCHER" ] && python3 "$DEEPXIV_FETCHER" paper-head ARXIV_ID
[ -n "$DEEPXIV_FETCHER" ] && python3 "$DEEPXIV_FETCHER" paper-section ARXIV_ID "SECTION_NAME"
[ -n "$DEEPXIV_FETCHER" ] && python3 "$DEEPXIV_FETCHER" trending --days 7 --max MAX_RESULTS
[ -n "$DEEPXIV_FETCHER" ] && python3 "$DEEPXIV_FETCHER" wsearch "QUERY"
[ -n "$DEEPXIV_FETCHER" ] && python3 "$DEEPXIV_FETCHER" 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 jsonStep 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.
Step 6: Update Research Wiki (if active)
If the project has an active research wiki and the user is building a literature set, add DeepXiv findings as source-backed entries with arXiv/Semantic Scholar IDs, retrieved sections, and the recommended next depth step.
Follow shared-references/integration-contract.md. If the wiki path or schema is unclear, ask before writing.
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.
- If the result overlaps with a published venue paper from Semantic Scholar, keep the richer venue metadata in the final summary.
More skills from wanshuiyin/Auto-claude-code-research-in-sleep
- Aablation-plannerUse when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
- Aablation-plannerUse when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.
- AalphaxivQuick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
- AalphaxivQuick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
- Aanalyze-resultsAnalyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
- Aanalyze-resultsAnalyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says \"analyze results\", \"compare\", or needs to interpret experimental data.
- AarxivSearch, download, and summarize academic papers from arXiv. Use when user says "search arxiv", "download paper", "fetch arxiv", "arxiv search", "get paper pdf", or wants to find and save papers from arXiv to the local paper library.
- AarxivSearch, download, and summarize academic papers from arXiv. Use when user says \"search arxiv\", \"download paper\", \"fetch arxiv\", \"arxiv search\", \"get paper pdf\", or wants to find and save papers from arXiv to the local paper library.
- Aauto-paper-improvement-loopAutonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
- Aauto-paper-improvement-loopAutonomously improve a generated paper via Claude review through claude-review MCP → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
- Aauto-paper-improvement-loopAutonomously improve a generated paper via Gemini review through gemini-review MCP → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
- Aauto-paper-improvement-loopAutonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.