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Deepxiv Trending Digest

Organization
DeepXiv
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.

Overview

PublisherDeepXiv
Repositorydeepxiv_sdk
Skill namedeepxiv-trending-digest
Stars
804
Forks
45
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 DeepXiv on GitHub. Read the source before you install it.

Installation

Install the Deepxiv Trending Digest 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.
    https://github.com/DeepXiv/deepxiv_sdk/tree/main/skills/deepxiv-trending-digest
  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/DeepXiv/deepxiv_sdk.git /tmp/deepxiv_sdk
mkdir -p .claude/skills
cp -r /tmp/deepxiv_sdk/skills/deepxiv-trending-digest .claude/skills/deepxiv-trending-digest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deepxiv Trending Digest 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 Deepxiv Trending Digest 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 Deepxiv Trending Digest 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.

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.

bash
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:

bash
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:

bash
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:

bash
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:

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

Recommended structure:

md
# 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

bash
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

Frequently asked questions

What does the Deepxiv Trending Digest AI skill do?

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.

Why use Deepxiv Trending Digest on TypingMind?

Because you install it once and use it with any model. Deepxiv Trending Digest 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 Deepxiv Trending Digest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DeepXiv/deepxiv_sdk/tree/main/skills/deepxiv-trending-digest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deepxiv Trending Digest?

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 Deepxiv Trending Digest?

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

Is the Deepxiv Trending Digest AI skill free?

Yes. It is published on GitHub by DeepXiv 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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