Deepxiv
Search and progressively read open-access academic papers through DeepXiv.
by wanshuiyin · MIT
The 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.
DeepXiv Paper Search & Progressive Reading
Search topic or paper ID: $ARGUMENTS
Role & Positioning
DeepXiv is the progressive-reading literature source:
| Skill | Source | Best for |
|---|---|---|
/arxiv |
arXiv API | Batch search, PDF download, metadata |
/deepxiv |
DeepXiv SDK | Progressive section-level reading |
/semantic-scholar |
S2 API | Published venue metadata, citation counts |
/alphaxiv |
www.alphaxiv.org | Instant LLM-optimized summary of one paper, with LaTeX source fallback |
Use DeepXiv when you want to inspect papers incrementally instead of loading the full text immediately.
Constants
- DEEPXIV_FETCHER — canonical name
deepxiv_fetch.py, resolved pershared-references/integration-contract.md§2 (Codex-side chain:$ARIS_REPO/tools/→tools/→~/.codex/skills/deepxiv/). Policy D1 — if unresolved (canonical chain exhausted), fall back to rawdeepxivCLI. - 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: 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: $DEEPXIV_FETCHER" >&2
else
echo "DeepXiv adapter unresolved (canonical chain exhausted); raw deepxiv CLI fallback will be used." >&2
fi
If the adapter is unresolved, fall back to raw deepxiv commands.
Step 3: Execute the Minimal Command
[ -n "$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 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:
searchpaper-briefpaper-headpaper-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
deepxivcommands when available. - If DeepXiv is missing, give the install command and suggest
/arxivor/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.
Use this skill
Connect your agent to the Skill Store MCP server and ask for it by ref: wanshuiyin/deepxiv-41212675378.
Details
- Publisher
- wanshuiyin
- Category
- Science & Education › Research & academia
- License
- MIT
- Updated
- 2026-10-19
References
More from wanshuiyin
- Ablation Planner Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies…
- Alphaxiv Quick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback.
- Analyze Results Analyze ML experiment results, compute statistics, generate comparison tables and insights.
- Arxiv Search, download, and summarize academic papers from arXiv.