A specialized utility for reducing AI agent token consumption, now transitioned to the canonical openclaw-agent-optimize skill.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-agent-token-optimizer
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install openclaw-agent-token-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Openclaw Agent Token Optimizer was originally developed to address the challenges of high token usage and context window limitations in long-running AI interactions. As part of the evolving library of Openclaw Skills, it provided a framework for developers to monitor and prune unnecessary data from agent sessions.
Please note that this specific listing has been deprecated in favor of a consolidated version. Users looking for the latest features in token management and context optimization should transition to the canonical version to ensure full compatibility with modern Openclaw Skills standards.
This skill is deprecated. To install the current, fully supported version of this utility, use the following command in your terminal:
clawhub install phenomenoner/openclaw-agent-optimize
| Data Component | Description |
|---|---|
| Versioning | Tracks legacy versioning for backward compatibility. |
| Redirect URI | Points to the canonical phenomenoner/openclaw-agent-optimize skill. |
| Metadata | Integrated with the global registry for Openclaw Skills. |
Loading
A strategic advisory skill designed to audit and refine OpenClaw workspaces for peak efficiency, lower costs, and reduced context bloat.

A utility focused on refining and optimizing the performance of AI agents within the Openclaw ecosystem.

A reliability-first checklist for hardening POSIX-based OpenClaw background workers and unattended automation against shell-specific failures.

A portable tagging protocol that defines explicit context boundaries and memory persistence rules for AI chat sessions.

A browser-automation tool for extracting and analyzing Amazon ASIN data to uncover competitor vulnerabilities and pricing strategies.

Effortlessly extract structured business data and leads from Google Maps using natural language commands.








































