Home › Skills › Dizest Summarize
Research & Knowledge

Dizest Summarize skill for OpenClaw

⬇ 2.2K downloads ★ 0 stars Version 1.0.1 Rank #6936 of 10,000+

What this skill does

Summarize long-form content — articles, podcasts, research papers, PDFs, notes, and more — using the Dizest API.

The Dizest Summarize skill is part of the Research & Knowledge category — research and knowledge skills that gather, search, and summarize information. You can install it on its own or alongside other research & knowledge skills from the OpenClaw catalog.

The Dizest Summarize skill is ranked #6936 by downloads in the OpenClaw skill catalog (2.2K total downloads, 0 stars). It belongs to the Research & Knowledge category alongside 1884 other top-10000 skills.

How to install the Dizest Summarize skill

The easiest path is via the OpenClaw Easy desktop app — one click, no terminal required:

  1. Download OpenClaw Easy for macOS or Windows (free, one-click installer, ~30 seconds).
  2. Open the in-app Skills panel.
  3. Search for dizest-summarize and click Install.
  4. The skill activates automatically when an incoming message matches its description.

Install from the command line

If you already run the OpenClaw CLI, add the Dizest Summarize skill with a single command:

openclaw skills add dizest-summarize

This pulls dizest-summarize from ClawHub and installs it into ~/.openclaw/skills/dizest-summarize/. Restart the OpenClaw gateway afterwards so the new skill is discovered.

How to use the Dizest Summarize skill

Once installed, the Dizest Summarize skill activates on its own: when an incoming message on WhatsApp, Telegram, Slack, Discord, Feishu or Line matches the skill's description, your OpenClaw agent loads it and runs the workflow. You can also trigger it explicitly by describing the task in chat. No extra configuration is required after install.

Manual install (advanced)

If you prefer manual installation:

  1. Click the Download skill .zip button above to grab dizest-summarize-1.0.1.zip directly from our S3 mirror.
  2. Unzip into ~/.openclaw/skills/dizest-summarize/ (create the directory if it does not exist).
  3. Restart OpenClaw Easy (or the OpenClaw CLI gateway) so the new skill is discovered.

Frequently asked questions

How do I install Dizest Summarize?

Install Dizest Summarize in the OpenClaw Easy desktop app by opening the Skills panel, searching for dizest-summarize, and clicking Install. From a terminal you can run: openclaw skills add dizest-summarize. Either way the skill is placed in ~/.openclaw/skills/dizest-summarize/.

Is the Dizest Summarize skill free?

Yes. It is free to download and run through ClawHub, with no account or payment required. Each skill is published by its own author under its own licence — see its ClawHub page for the licence and source.

What does Dizest Summarize do?

Summarize long-form content — articles, podcasts, research papers, PDFs, notes, and more — using the Dizest API.

Related: more research & knowledge skills

If the Dizest Summarize skill looks useful, you may also want to check out other research & knowledge skills in the OpenClaw catalog:

Skills in this catalog are community-contributed integrations published on ClawHub and distributed under their own open-source licences. Product and company names, and any third-party service a skill connects to, are trademarks of their respective owners; a listing here does not imply affiliation with, sponsorship by, or endorsement from them. This page does not distribute any third-party application. Rights holders can reach us at hello@openclaw-easy.com.

Browse the full OpenClaw skill catalog

This page covers just one skill. The OpenClaw skill hub has 10,000+ more — search, sort by downloads or stars, and install any of them in one click. There is also a curated awesome-openclaw-skills list grouped by use case.

Get OpenClaw Easy — Free

Install Dizest Summarize and 10,000+ other OpenClaw skills in one click. Free, open-source, runs locally on macOS & Windows.

Free, open-source · Apache-2.0 · Works with Claude, ChatGPT, Gemini, or local Ollama models