Deepxiv

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 | | /semantic-scholar | Published venue metadata, citation counts, DOI links | | /deepxiv | Layered reading: search → brief → head → section, plus trending and web search |

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

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 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-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

Prefer the ARIS adapter:

python3 tools/deepxiv_fetch.py --help

If tools/deepxiv_fetch.py is not available, fall back to raw deepxiv commands.

Step 3: Execute the Minimal Command

Search papers

python3 tools/deepxiv_fetch.py search "QUERY" --max MAX_RESULTS

Fallback:

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

Brief summary

python3 tools/deepxiv_fetch.py paper-brief ARXIV_ID

Fallback:

deepxiv paper ARXIV_ID --brief --format json

Section map

python3 tools/deepxiv_fetch.py paper-head ARXIV_ID

Fallback:

deepxiv paper ARXIV_ID --head --format json

Specific section

python3 tools/deepxiv_fetch.py paper-section ARXIV_ID "SECTION_NAME"

Fallback:

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

Trending

python3 tools/deepxiv_fetch.py trending --days 7 --max MAX_RESULTS

Fallback:

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

Web search

python3 tools/deepxiv_fetch.py wsearch "QUERY"

Fallback:

deepxiv wsearch "QUERY" --output json

Semantic Scholar metadata

python3 tools/deepxiv_fetch.py sc "SEMANTIC_SCHOLAR_ID"

Fallback:

deepxiv sc "SEMANTIC_SCHOLAR_ID" --output json

Step 4: Present Results

When searching, present a compact table:

| # | 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: brief → head → section

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](../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:

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 $2; exit}' .aris/installed-skills.txt 2>/dev/null)}"
  WIKI_SCRIPT=".aris/tools/research_wiki.py"
  [ -f "$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, export ARIS_REPO, or cp /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 ""
  fi
fi

The helper handles metadata / slug / dedup / page / index / log in one call — do not handwrite papers/.md. See [shared-references/integration-contract.md](../shared-references/integration-contract.md). Backfill missed ingests with python3 "$WIKI_SCRIPT" sync research-wiki/ --arxiv-ids ,,... 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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: raja21068
• Source: raja21068/AutoResearch
• License: MIT

Install and usage instructions live in the source repository linked above.