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/deepxiv-trending-digest

Summarize recent hot academic papers using deepxiv trending, brief, head, and section reads, then produce a markdown digest highlighting what each paper is about and which papers deserve deeper reading.

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deepxiv-sdk
8093 skills
Install
$ npx -y skills add deepxiv/deepxiv_sdk --skill deepxiv-trending-digest --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/deepxiv-trending-digest

Context preview

The summary Claude sees to decide when to auto-load this skill.

Summarize recent hot academic papers using deepxiv trending, brief, head, and section reads, then produce a markdown digest highlighting what each paper is about and which papers deserve deeper reading.

SKILL.md

deepxiv-trending-digest.SKILL.md
name: deepxiv-trending-digest
description: Summarize recent hot academic papers using deepxiv trending, brief, head, and section reads, then produce a markdown digest highlighting what each paper is about and which papers deserve deeper reading.

DeepXiv Trending Digest

Use this skill when the user wants a recent hot-paper roundup, trending paper summary, weekly paper digest, or a markdown report based on DeepXiv trending papers.

Goal

Turn recent DeepXiv trending papers into a concise markdown digest:

1. Find currently hot papers with `deepxiv trending` 2. Brief each candidate with `deepxiv paper <id> --brief` 3. Select the most promising papers for deeper inspection 4. Inspect structure with `deepxiv paper <id> --head` 5. Read only the most relevant sections with `deepxiv paper <id> --section ...` 6. Write a clean `.md` summary with recommendations

Default Workflow

1. Pull trending papers

Start with a small, recent set unless the user asks otherwise.

deepxiv trending --days 7 --limit 10 --json

Default heuristics:

  • Use `--days 7` for "recent hot papers"
  • Use `--limit 10` for a manageable first pass
  • If the user asks for a broader roundup, use `--days 14` or `--days 30`

2. Brief every candidate

For each selected arXiv ID, fetch a brief first:

deepxiv paper <arxiv_id> --brief

Capture:

  • title
  • arXiv ID
  • publish date
  • keywords
  • TLDR
  • citations if present
  • GitHub URL if present

Do not jump to full text yet. `--brief` is the default screening step.

3. Rank for deeper reading

After reading briefs, choose the top 1-3 papers for deeper inspection.

Use signals like:

  • strong novelty or surprising result
  • practical relevance
  • likely user interest
  • unusually clear contribution
  • useful code release
  • especially strong social or research momentum

If a paper sounds incremental, unclear, or out of scope, keep it in the digest but skip deep reading.

4. Inspect paper structure

For the chosen papers:

deepxiv paper <arxiv_id> --head

Use `--head` to decide whether to drill down and which section matters most.

Look for sections such as:

  • Introduction
  • Method / Approach / Framework
  • Experiments / Results / Evaluation
  • Discussion / Limitations

Prefer section reads over full paper reads.

5. Read only high-value sections

Read at most 1-2 sections per paper unless the user explicitly asks for a deep dive.

Examples:

deepxiv paper <arxiv_id> --section Introduction
deepxiv paper <arxiv_id> --section Method
deepxiv paper <arxiv_id> --section Results

Selection guidance:

  • Read `Introduction` if the contribution is still fuzzy
  • Read `Method` if the core idea matters
  • Read `Results` if the claim sounds strong and needs verification
  • Read `Limitations` or `Discussion` if tradeoffs matter

Avoid reading everything.

Markdown Output

Write a markdown file in the current workspace unless the user gave a different path.

Recommended filename:

trending-paper-digest-YYYY-MM-DD.md

Recommended structure:

# Trending Paper Digest

Date: YYYY-MM-DD
Window: Last 7 days
Source: deepxiv trending

## Executive Summary

- 2-4 bullets with the main themes across the trending list
- Mention the 1-3 most promising papers

## Papers Reviewed

### 1. Paper Title (`arXiv:xxxx.xxxxx`)

What it is about:
Short paragraph based mainly on `--brief`.

Why it matters:
- Bullet
- Bullet

Worth deeper reading?
Yes/Maybe/No, with one sentence.

If deeper review was done:

Sections checked:
- Introduction
- Method

Deeper notes:
Short paragraph with the key insight, evidence, or caveat.

### 2. ...

## Recommended Deep Dives

### Paper Title

- Why it stands out
- Which section to read next
- What question it could answer

## Cross-Cutting Trends

- Repeated themes
- Common methods
- Shared limitations or hype signals

Writing Rules

  • Keep the digest skimmable
  • Prefer short paragraphs and flat bullets
  • Separate "what it says" from "whether it is worth deeper reading"
  • Be explicit when a conclusion is based only on `--brief`
  • Be explicit when a conclusion is based on `--head` or a section read
  • Do not pretend to have verified claims you have not checked

Decision Rules

  • If briefs are enough for the user's request, stop there
  • Use `--head` only for the most promising papers
  • Use `--section` only after `--head` suggests a high-value section
  • Do not read full paper markdown unless the user explicitly asks for a full analysis

Minimal Example

deepxiv trending --days 7 --limit 5 --json
deepxiv paper 2603.20639 --brief
deepxiv paper 2603.26221 --brief
deepxiv paper 2603.20639 --head
deepxiv paper 2603.20639 --section Introduction

Then write the digest as markdown and clearly label:

  • all reviewed papers
  • which papers were only briefed
  • which papers received deeper inspection
Read more
Ships withdeepxiv-sdk

Talk to research papers like talking to authors - Python package with AI agent for arXiv papers

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