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Get Started Free →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.
.claude/skills/wanshuiyin-deepxiv/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 118% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 86% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 126% | 0% |
Search topic or paper ID: $ARGUMENTS
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 | 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.
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 raw deepxiv CLI.
> 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
DeepXiv is optional:
bashpip install deepxiv-sdk
On first use, deepxiv auto-registers a free token and stores it in ~/.env.
Parse $ARGUMENTS for:
- max: N- brief- head- section: NAME- trending- days: 7|14|30- web- scIf the input looks like an arXiv ID and no explicit mode is provided, default to brief.
Resolve $DEEPXIV_FETCHER via the canonical strict-safe Codex chain (see shared-references/integration-contract.md §2):
bashif [ -z "${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: $DEEPXIV_FETCHER" >&2 else echo "DeepXiv adapter unresolved (canonical chain exhausted); raw deepxiv CLI fallback will be used." >&2 fi
If the adapter is unresolved, fall back to raw deepxiv commands.
bash[ -n "$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:
bashdeepxiv 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
For search results, present a compact literature table. For paper reads, summarize the title, authors, date, TLDR, and the next recommended depth step.
Use the progression:
searchpaper-briefpaper-headpaper-sectionOnly read the full paper when the user explicitly needs it.
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.
deepxiv commands when available./arxiv or /research-lit "topic" - sources: web.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | fail→pass | 10,064 | 2,648 | -74% | 1 | 1 | 0% | 1,800 | 2,065 | +15% | 0 | 0 | — |
case-01 | fail→fail | 18,887 | 11,581 | -39% | 1 | 1 | 0% | 3,564 | 2,510 | -30% | 0 | 0 | — |
case-02 | fail→fail | 14,844 | 8,858 | -40% | 1 | 1 | 0% | 2,850 | 2,179 | -24% | 0 | 0 | — |
case-07 | pass→pass | 4,422 | 3,353 | -24% | 1 | 1 | 0% | 588 | 2,126 | +262% | 0 | 0 | — |
case-03 | pass→fail | 22,082 | 10,657 | -52% | 1 | 1 | 0% | 4,046 | 2,302 | -43% | 0 | 0 | — |
case-04 | fail→pass | 9,005 | 3,506 | -61% | 1 | 1 | 0% | 1,818 | 2,328 | +28% | 0 | 0 | — |
case-05 | pass→pass | 6,624 | 3,626 | -45% | 1 | 1 | 0% | 1,246 | 2,345 | +88% | 0 | 0 | — |
case-06 | pass→pass | 12,245 | 3,113 | -75% | 1 | 1 | 0% | 2,299 | 2,181 | -5% | 0 | 0 | — |
case-08 | fail→pass | 6,239 | 3,329 | -47% | 1 | 1 | 0% | 1,072 | 2,337 | +118% | 0 | 0 | — |
case-09 | pass→pass | 8,941 | 2,012 | -77% | 1 | 1 | 0% | 1,610 | 2,010 | +25% | 0 | 0 | — |
case-10 | fail→pass | 5,616 | 2,329 | -59% | 1 | 1 | 0% | 1,095 | 2,035 | +86% | 0 | 0 | — |
case-11 | pass→pass | 9,248 | 2,388 | -74% | 1 | 1 | 0% | 1,878 | 2,028 | +8% | 0 | 0 | — |
case-13 | pass→fail | 7,974 | 2,081 | -74% | 1 | 1 | 0% | 1,460 | 1,939 | +33% | 0 | 0 | — |
case-14 | fail→pass | 5,158 | 3,616 | -30% | 1 | 1 | 0% | 936 | 2,117 | +126% | 0 | 0 | — |
case-15 | fail→pass | 6,852 | 2,190 | -68% | 1 | 1 | 0% | 1,143 | 2,021 | +77% | 0 | 0 | — |
case-16 | fail→pass | 7,590 | 1,956 | -74% | 1 | 1 | 0% | 1,267 | 1,894 | +49% | 0 | 0 | — |
case-17 | fail→pass | 3,323 | 1,737 | -48% | 1 | 1 | 0% | 415 | 1,899 | +358% | 0 | 0 | — |
case-18 | fail→pass | 12,833 | 3,247 | -75% | 1 | 1 | 0% | 2,102 | 2,193 | +4% | 0 | 0 | — |
case-19 | fail→pass | 13,704 | 4,548 | -67% | 1 | 1 | 0% | 2,438 | 2,462 | +1% | 0 | 0 | — |
case-20 | fail→pass | 9,171 | 1,911 | -79% | 1 | 1 | 0% | 1,685 | 1,946 | +15% | 0 | 0 | — |
case-21 | pass→pass | 5,249 | 3,153 | -40% | 1 | 1 | 0% | 807 | 2,106 | +161% | 0 | 0 | — |
case-22 | fail→pass | 15,330 | 5,406 | -65% | 1 | 1 | 0% | 2,399 | 2,605 | +9% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +45 percentage points is the difference between those two pass rates over the 19 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.