skill39-wsc2017-mod-c

🥈Silver

@menus12

This repository contains test project marking automation scripts for Skill 39 Module C from WSC 2017.

610Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/menus12/skill39-wsc2017-mod-c.git

Works with:

Claude

Overview

About This Skill

This repository contains test project marking automation scripts for Skill 39 Module C from WSC 2017.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/menus12/skill39-wsc2017-mod-c

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Automate the grading process for [COMPANY]'s [INDUSTRY] training program using the WSC 2017 Skill 39 Module C scripts. The program has [NUMBER] of participants and [NUMBER] of assessments. Provide a step-by-step guide to implement the automation, including data preparation, script execution, and result analysis.

Example Output

## Automated Grading Process for [COMPANY]'s [INDUSTRY] Training Program

### Step 1: Data Preparation

- Gather all assessment data in a structured format (e.g., CSV or Excel).
- Ensure data includes participant IDs, assessment scores, and any additional metadata.
- Clean the data to remove any duplicates or inconsistencies.

### Step 2: Script Execution

- Clone the WSC 2017 Skill 39 Module C repository from GitHub.
- Install any necessary dependencies listed in the repository's README file.
- Run the automation scripts using the command line, specifying the path to your data file.

### Step 3: Result Analysis

- The scripts will generate a summary report with the following information:
  - Total number of participants graded.
  - Average score across all assessments.
  - Distribution of scores (e.g., number of participants scoring above 80%, etc.).
- Review the report to identify any outliers or areas for improvement in the training program.

### Step 4: Integration with Existing Systems

- Export the graded data to your preferred format (e.g., CSV, Excel, or database).
- Integrate the graded data with your existing learning management system (LMS) or HR platform.
- Set up automated notifications to inform participants of their grades and provide feedback.