Skip to content
FunCoding

Search

Search docs, Skills and MCP

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.

文档与办公17kskills/deepxiv/SKILL.md

Install

Send this to Claude Code, Codex or Cursor. The agent checks the Skill for safety first and installs it only after you confirm.

读取 https://funcoding.ai/skills/wanshuiyin/auto-claude-code-research-in-sleep/deepxiv/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

DeepXiv Paper Search & Progressive Reading

Search topic or paper ID: $ARGUMENTS

Role & Positioning

DeepXiv is the progressive-reading literature source:

SkillSourceBest for
/arxivarXiv APIBatch search, PDF download, metadata
/deepxivDeepXiv SDKProgressive section-level reading
/semantic-scholarS2 APIPublished venue metadata, citation counts
/alphaxivwww.alphaxiv.orgInstant LLM-optimized summary of one paper, with LaTeX source fallback

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

Constants

  • DEEPXIV_FETCHER — canonical name deepxiv_fetch.py, resolved per 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).
  • 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

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
fi

Step 3: Execute the Minimal Command

Search papers

python3 "$DEEPXIV_FETCHER" search "QUERY" --max MAX_RESULTS

Fallback:

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

Brief summary

python3 "$DEEPXIV_FETCHER" paper-brief ARXIV_ID

Fallback:

deepxiv paper ARXIV_ID --brief --format json

Section map

python3 "$DEEPXIV_FETCHER" paper-head ARXIV_ID

Fallback:

deepxiv paper ARXIV_ID --head --format json

Specific section

python3 "$DEEPXIV_FETCHER" paper-section ARXIV_ID "SECTION_NAME"

Fallback:

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

Trending

python3 "$DEEPXIV_FETCHER" trending --days 7 --max MAX_RESULTS

Fallback:

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

Web search

python3 "$DEEPXIV_FETCHER" wsearch "QUERY"

Fallback:

deepxiv wsearch "QUERY" --output json

Semantic Scholar metadata

python3 "$DEEPXIV_FETCHER" 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 (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 $2; exit}' .aris/installed-skills.txt 2>/dev/null)}"
  if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
    ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
  fi
  WIKI_SCRIPT=".aris/tools/research_wiki.py"
  [ -f "$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 or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or cp <ARIS-repo>/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 "<arxiv_id>"
  fi
fi

The helper handles metadata / slug / dedup / page / index / log in one call — do not handwrite papers/<slug>.md. See shared-references/integration-contract.md. Backfill missed ingests with python3 "$WIKI_SCRIPT" sync research-wiki/ --arxiv-ids <id1>,<id2>,... 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.

Similar Skills

theme-factory
anthropics/skills180k

theme-factory

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

Docs & office

canvas-design
anthropics/skills180k

canvas-design

Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.

Docs & office

doc-coauthoring
anthropics/skills180k

doc-coauthoring

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

Docs & office

docx
anthropics/skills180k

docx

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Docs & office

pdf
anthropics/skills180k

pdf

Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

Docs & office

discernment-nudge
anthropics/skills180k

discernment-nudge

After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.

Docs & office