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.
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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/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 avoid loading full papers too early.
Constants
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).
- DEEPXIVFETCHER — canonical name deepxivfetch.py, resolved per
- MAXRESULTS = 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-sdkOn 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 $DEEPXIV_FETCHER via the canonical strict-safe chain (see shared-references/integration-contract.md §2). Policy D1 cascade: the resolved adapter is preferred; if unresolved (canonical chain exhausted), fall back to raw deepxiv CLI commands documented in Step 3.
cd "$(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: $DEEPXIV_FETCHER" >&2
else
echo "DeepXiv adapter unresolved (canonical chain exhausted); raw deepxiv CLI fallback will be used." >&2
fiStep 3: Execute the Minimal Command
Search papers
python3 "$DEEPXIV_FETCHER" search "QUERY" --max MAX_RESULTSFallback:
deepxiv search "QUERY" --limit MAX_RESULTS --format jsonBrief summary
python3 "$DEEPXIV_FETCHER" paper-brief ARXIV_IDFallback:
deepxiv paper ARXIV_ID --brief --format jsonSection map
python3 "$DEEPXIV_FETCHER" paper-head ARXIV_IDFallback:
deepxiv paper ARXIV_ID --head --format jsonSpecific section
python3 "$DEEPXIV_FETCHER" paper-section ARXIV_ID "SECTION_NAME"Fallback:
deepxiv paper ARXIV_ID --section "SECTION_NAME" --format jsonTrending
python3 "$DEEPXIV_FETCHER" trending --days 7 --max MAX_RESULTSFallback:
deepxiv trending --days 7 --limit MAX_RESULTS --output jsonWeb search
python3 "$DEEPXIV_FETCHER" wsearch "QUERY"Fallback:
deepxiv wsearch "QUERY" --output jsonSemantic Scholar metadata
python3 "$DEEPXIV_FETCHER" sc "SEMANTIC_SCHOLAR_ID"Fallback:
deepxiv sc "SEMANTIC_SCHOLAR_ID" --output jsonStep 4: Present Results
When searching, present a compact table:
More skills from wanshuiyin/Auto-claude-code-research-in-sleep
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- 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.