Mmcp.market

alphaxiv skill

by wanshuiyin·wanshuiyin/Auto-claude-code-research-in-sleep·17k stars·MIT

Quick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.

A100/100content scan

Is the alphaxiv skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.

No findings.

Install the alphaxiv 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/alphaxiv ~/.claude/skills/alphaxiv
available in every project

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

AlphaXiv Paper Lookup

Lookup paper: $ARGUMENTS

Quick single-paper reader with tiered source fallback (overview → full markdown → LaTeX source). Powered by AlphaXiv.

Role & Positioning

This skill is the quick single-paper reader that returns LLM-optimized summaries:

Do NOT use this skill for topic discovery, broad literature search, or multi-paper surveys — use /research-lit or /arxiv instead.

Constants

  • OVERVIEWURL = https://alphaxiv.org/overview/{PAPERID}.md
  • ABSURL = https://alphaxiv.org/abs/{PAPERID}.md
  • ARXIVSRCURL = https://arxiv.org/src/{PAPER_ID}
  • ALPHAXIVUA = Mozilla/5.0 (Macintosh; Intel Mac OS X 1015_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36 — any modern browser UA works; update the version numbers if AlphaXiv starts blocking this value again

Overrides (append to arguments):

- /alphaxiv 2401.12345 — quick overview

- /alphaxiv "https://arxiv.org/abs/2401.12345" — auto-extract ID

- /alphaxiv 2401.12345 - depth: src — force LaTeX source inspection

- /alphaxiv 2401.12345 - depth: abs — force full markdown

Workflow

Step 1: Parse Arguments & Extract Paper ID

Parse $ARGUMENTS to extract a bare arXiv paper ID. Accept these input formats:

  • https://arxiv.org/abs/2401.12345 or https://arxiv.org/abs/2401.12345v2
  • https://arxiv.org/pdf/2401.12345
  • https://alphaxiv.org/overview/2401.12345
  • https://alphaxiv.org/abs/2401.12345
  • 2401.12345 or 2401.12345v2

Strip version suffixes (v1, v2, ...) for API calls. Store as PAPER_ID.

Parse optional directives:

  • - depth: overview|abs|src: force a specific tier instead of cascading

Step 2: Fetch AlphaXiv Overview (Tier 1 — Fastest)

Use curl with {ALPHAXIV_UA} to fetch the AlphaXiv overview. AlphaXiv may return 403 for non-browser User-Agents; setting a standard browser UA reduces false positives from bot-detection:

curl -sL --max-time 15 -A "{ALPHAXIV_UA}" "https://alphaxiv.org/overview/{PAPER_ID}.md"

This returns a structured, LLM-optimized report designed for machine consumption. Use this as the default and preferred source.

If the overview answers the user's question, stop here. Do not fetch deeper tiers unnecessarily.

If the request fails (HTTP 4xx — 403 bot-block or 404 not-yet-processed) or returns empty content, proceed to Step 3.

Step 3: Fetch Full AlphaXiv Markdown (Tier 2 — More Detail)

Use curl with {ALPHAXIV_UA} to fetch the full paper markdown:

curl -sL --max-time 15 -A "{ALPHAXIV_UA}" "https://alphaxiv.org/abs/{PAPER_ID}.md"

This provides the full paper body as markdown. Use when the user needs:

  • Specific methodology details
  • Detailed experimental results
  • Particular sections not covered in the overview

If this still does not answer the question, proceed to Step 4.

Step 4: Fetch arXiv LaTeX Source (Tier 3 — Deepest)

When the overview and full markdown are both insufficient (e.g., the user asks about equations, proofs, appendix details, or implementation specifics), download the paper's LaTeX source from https://arxiv.org/src/{PAPER_ID}.

The source is a .tar.gz archive. Download it to a temporary directory, extract it, and list the .tex files inside.

Then inspect only the files needed to answer the question. Prioritize:

  1. Top-level *.tex files (usually the main document)
  2. Files referenced by \input{} or \include{}
  3. Appendices, tables, or sections directly related to the user's question

Do NOT read the entire source tree by default. Read selectively.

Temporary source artifacts live under /tmp. Do not rely on persistence.

Step 5: Present Results

Default Answer Shape

## [Paper Title]

- **arXiv**: [PAPER_ID] — https://arxiv.org/abs/[PAPER_ID]
- **Source depth**: overview | abs | src

### Summary
[2-3 sentence summary]

### Key Points
- [point 1]
- [point 2]
- [point 3]

### Answer to Your Question
[Direct answer if the user asked a specific question]

If the user only asks for one specific detail, answer it directly — skip the full template.

After presenting the summary, you MUST proceed to Step 6 before ending the turn.

Step 6: Research Wiki Ingest

You MUST always run the bash block below — it checks for research-wiki/ internally and exits silently when absent. Do NOT skip this step based on your own directory check; the bash block handles that for you.

Substitute only and ; keep ${ARIS_REPO:-...} as-is so an already-set env var is preserved.

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; paper 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=""
  }
  [ -n "$WIKI_SCRIPT" ] && python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
      --arxiv-id "<paper_arxiv_id>" \
      [--thesis "<one-line thesis from the Tier 1 overview>"]
fi

The helper handles metadata fetch, slug, dedup, page creation, index rebuild, and log append — do not handwrite papers/.md. See shared-references/integration-contract.md. If wiki was not present at read time (or the helper was unreachable), the user can backfill via python3 "$WIKISCRIPT" sync research-wiki/ --arxiv-ids after resolving $WIKISCRIPT as above.

Suggest Follow-Up Skills (after Step 6 completes)

/arxiv "PAPER_ID" - download          - download the PDF to local library
/deepxiv "PAPER_ID" - section: Methods  - read a specific section progressively
/research-lit "related topic"        - multi-source literature survey
/novelty-check "idea from paper"     - verify novelty against this paper's area

Key Rules

  • Overview first: overview is the fastest path and must always be tried before deeper tiers. Only escalate when needed.
  • Minimal reads: At src tier, read only the files that answer the question. Full-tree reads waste tokens.
  • Cross-platform: When downloading and extracting the source archive, prefer cross-platform approaches (e.g., Python stdlib) over platform-specific commands to ensure Windows/WSL compatibility.
  • No PDF parsing: This skill reads structured markdown and LaTeX source, not raw PDFs. For PDF content, suggest /arxiv with download.
  • Rate limiting: arXiv source download may rate-limit. If HTTP 429 occurs, wait 5 seconds and retry once. If still blocked, report the error and suggest /deepxiv as alternative.
  • Complementary, not competing: This skill complements /arxiv (search + download) and /deepxiv (progressive reading). Do not re-implement their functionality.

Integration with Other Skills

As enrichment in /research-lit

/research-lit can use this skill's Tier 1 (overview) as a fast enrichment step between search and deep analysis. After finding arXiv papers in Step 1, fetch AlphaXiv overviews to quickly assess relevance before committing to full-text reads:

Step 1: Search → list of arXiv IDs
Step 1.5: AlphaXiv overview for top 5-8 papers (this skill, Tier 1 only)
Step 2: Deep analysis only for papers that pass the relevance filter

This saves significant tokens by filtering out marginally relevant papers before deep reading.

As follow-up from other skills

After /research-lit, /novelty-check, or /idea-discovery surface a specific paper, users can invoke /alphaxiv PAPER_ID for a fast deep-dive without re-running the full survey.

More skills from wanshuiyin/Auto-claude-code-research-in-sleep

  • Aablation-plannerUse when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
  • Aablation-plannerUse when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.
  • AalphaxivQuick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
  • Aanalyze-resultsAnalyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
  • Aanalyze-resultsAnalyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says \"analyze results\", \"compare\", or needs to interpret experimental data.
  • AarxivSearch, download, and summarize academic papers from arXiv. Use when user says "search arxiv", "download paper", "fetch arxiv", "arxiv search", "get paper pdf", or wants to find and save papers from arXiv to the local paper library.
  • AarxivSearch, download, and summarize academic papers from arXiv. Use when user says \"search arxiv\", \"download paper\", \"fetch arxiv\", \"arxiv search\", \"get paper pdf\", or wants to find and save papers from arXiv to the local paper library.
  • 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.
  • Aauto-review-loopAutonomous multi-round research review loop. In Copilot CLI it defaults to the native complementary rubber-duck subagent with host-event model evidence; elsewhere it uses Codex, while explicit external reviewer overrides remain available. Implements fixes and re-reviews until a policy-approved positive assessment or max rounds is reached.

All agent skills → · MCP servers