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Alphaxiv

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wanshuiyin
alphaxiv

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.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill namealphaxiv
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Alphaxiv AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
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
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alphaxiv in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Alphaxiv on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Alphaxiv is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

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

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

Constants

  • OVERVIEW_URL = https://alphaxiv.org/overview/{PAPER_ID}.md
  • ABS_URL = https://alphaxiv.org/abs/{PAPER_ID}.md
  • ARXIV_SRC_URL = https://arxiv.org/src/{PAPER_ID}
  • ALPHAXIV_UA = Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_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:

bash
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:

bash
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
markdown
## [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 <paper_arxiv_id> and <thesis>; keep ${ARIS_REPO:-...} as-is so an already-set env var is preserved.

bash
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/<slug>.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 "$WIKI_SCRIPT" sync research-wiki/ --arxiv-ids <id> after resolving $WIKI_SCRIPT as above.

Suggest Follow-Up Skills (after Step 6 completes)
text
/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.

Frequently asked questions

What does the Alphaxiv AI skill do?

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.

Why use Alphaxiv on TypingMind?

Because you install it once and use it with any model. Alphaxiv is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Alphaxiv in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/alphaxiv. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Alphaxiv?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Alphaxiv?

As many as you like. As long as a model supports skills, you can use Alphaxiv with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Alphaxiv AI skill free?

Yes. It is published on GitHub by wanshuiyin under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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