Descriptions:
All About AI walks through a practical, reproducible system for teaching an AI agent new browser-navigation skills without touching model weights. The agent — Claude Code running via a setup called OpenClaw on a dedicated Mac Mini — already holds skill files for Twitter/X, Gmail, GitHub, YouTube, and more. Each skill is a markdown file encoding the exact interaction patterns that worked: which JavaScript calls to make, which DOM elements to target, which CDP commands to issue through Chrome.
The video follows the full lifecycle of adding a LinkedIn skill from zero. The agent is given access to a JavaScript browser-control helper file, then asked to draft and post to LinkedIn autonomously. It spends roughly six minutes iterating through failed JavaScript injection attempts — trying different element selectors, switching to Chrome DevTools Protocol text insertion — until it succeeds. The creator immediately instructs it to document the winning workflow into `linkedin_skill.md`. On a fresh context window with no prior memory, the same task completes in about 40 seconds.
The broader implication is a lightweight alternative to fine-tuning: procedural browser knowledge accumulates as plain text, loads as context, and transfers across sessions. The approach works with any long-context model that supports tool use and requires no GPU infrastructure. For developers building persistent computer-use or browser-automation agents, the video provides a clear architecture and a concrete before/after demonstration of the time savings skill caching enables.
📺 Source: All About AI · Published February 01, 2026
🏷️ Format: Tutorial Demo
