KS
Killer-Skills

node-llm-web-automation — Categories.community

v1.0.0
GitHub

About this Skill

Perfect for Web Automation Agents needing efficient LLM-driven UI interaction and DOM manipulation. Electron UI + Puppeteer runner for SmartEdu automation

FrontMage FrontMage
[0]
[0]
Updated: 3/5/2026

Quality Score

Top 5%
21
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add FrontMage/codex_helper

Agent Capability Analysis

The node-llm-web-automation MCP Server by FrontMage is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion.

Ideal Agent Persona

Perfect for Web Automation Agents needing efficient LLM-driven UI interaction and DOM manipulation.

Core Value

Empowers agents to automate web workflows using Electron UI and Puppeteer, leveraging a single LLM call to deterministically drive the UI and interact with DOM elements, supporting protocols like HTTP and HTML parsing.

Capabilities Granted for node-llm-web-automation MCP Server

Automating manual Codex-driven page exploration
Driving UI deterministically for SmartEdu automation
Implementing pause and resume checkpoints for manual steps like captcha and 2FA

! Prerequisites & Limits

  • Requires Node.js environment
  • Limited to web automation tasks
  • Needs predefined entry URLs and end conditions
Project
SKILL.md
1.8 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
Readonly

Node LLM Web Automation

Overview

Turn manual Codex-driven page exploration into a low-cost Node.js automation flow that uses a single LLM call to pick DOM candidates, then drives the UI deterministically.

Workflow

1) Define the automation scope

  • List entry URLs (login, course detail, list page).
  • Mark manual steps (captcha, 2FA) and provide a pause + resume checkpoint.
  • Decide the end condition (completion badge, video end, DOM state change).

2) Implement deterministic candidate scanning

  • Collect clickable candidates from all frames (anchors, buttons, list items).
  • Attach metadata: text, href, data-key/resourceId, tag/class, domIndex, frame URL.
  • Run heuristic filtering first; only call LLM if needed.

3) LLM selection (single shot)

  • Send a minimal payload with the candidate list and a strict JSON schema.
  • Parse JSON defensively; fall back to heuristic ordering if parsing fails.
  • Cache the selection per page to avoid repeated calls.

4) Execute playback loop

  • Click the selected item, wait for video to appear, force mute + playbackRate.
  • Poll progress every N seconds; stop when progress reaches 100% or completion DOM appears.
  • On failure: dump DOM + screenshot + current URL for support.

5) Cost controls and reliability

  • Avoid LLM calls during long playback; only use it for selection.
  • Keep payload small (truncate text, drop large attributes).
  • Use timeouts and retries for video start.

References

  • See references/node-llm-runner.md for prompt templates, candidate schema, and error-handling patterns.

Notes for this repo

  • If working inside codex_helper, start from tools/simple-runner-manual.js and adapt it to the target site.
  • Keep the LLM call optional; prefer heuristic selection when possible.

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