Home › Skills › Context Compactor
Research & Knowledge

Context Compactor skill for OpenClaw

⬇ 3.1K downloads ★ 0 stars Version 0.3.8 Rank #3418 of 10,000+

What this skill does

Token-based context compaction for local models (MLX, llama.cpp, Ollama) that don't report context limits.

The Context Compactor 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 Context Compactor skill is ranked #3418 by downloads in the OpenClaw skill catalog (3.1K total downloads, 0 stars). It belongs to the Research & Knowledge category alongside 1884 other top-10000 skills.

How to install the Context Compactor 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 context-compactor 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 Context Compactor skill with a single command:

openclaw skills add context-compactor

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

How to use the Context Compactor skill

Once installed, the Context Compactor 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 context-compactor-0.3.8.zip directly from our S3 mirror.
  2. Unzip into ~/.openclaw/skills/context-compactor/ (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 Context Compactor?

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

Is the Context Compactor 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 Context Compactor do?

Token-based context compaction for local models (MLX, llama.cpp, Ollama) that don't report context limits.

Related: more research & knowledge skills

If the Context Compactor 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 Context Compactor 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