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respond-to-eval

Analyze course evaluations into thematic clusters and generate teaching improvement plans.

Updated Jun 27, 2026

Search Tags:#education automation #course evaluation analysis #teaching improvement plan #student feedback analysis #evaluation clustering

Ready to run

npx skills add https://github.com/fredmilhome/laffer_tobacco --skill respond-to-eval

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: respond-to-eval
Source: https://github.com/fredmilhome/laffer_tobacco/tree/main/.claude/skills/respond-to-eval
Command: npx skills add https://github.com/fredmilhome/laffer_tobacco --skill respond-to-eval

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the analysis of student course evaluations and generates a structured teaching improvement plan based on the feedback received.

Core Features & Use Cases

Analyze Free-Text & Numeric Evaluations: Process free-text and numeric feedback from course evaluations.
Theme Clustering: Group comments into themes based on frequency and sentiment.
Actionable Plan Generation: Classify themes as 'Keep', 'Change', 'Investigate', or 'Out-of-scope', and generate a plan with targeted changes.
Use Case: After receiving semester-end course evaluations, use this Skill to quickly assess the feedback and produce a plan for teaching improvements.

Quick Start

Analyze your course evaluations by running respond-to-eval course-evaluation.csv

Frequently Asked Questions about respond-to-eval

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze course evaluations and generate a teaching improvement plan?▼

The Skill processes course evaluations by parsing free-text and numeric feedback from a CSV file, clustering comments into themes based on sentiment and frequency, and generating a structured teaching improvement plan with targeted actions.

How does student feedback analysis group comments into themes?▼

Student feedback analysis groups comments into themes by evaluating frequency and sentiment, then classifying each thematic cluster into categories like 'Keep', 'Change', 'Investigate', or 'Out-of-scope' to guide teaching improvements.

Can I process both numeric ratings and free-text comments from course evaluations?▼

Yes, the course evaluation analysis supports processing both free-text and numeric feedback, enabling comprehensive assessment of student responses to produce a structured teaching improvement plan.

Do I need Python libraries to run course evaluation clustering?▼

Yes, you need Python libraries for file parsing and text processing to run course evaluation clustering, as the script relies on these dependencies to analyze feedback and generate the improvement plan.

What is the best way to turn end-of-semester course evaluations into actionable changes?▼

The best way to turn end-of-semester course evaluations into actionable changes is to use an automation script that categorizes feedback into thematic clusters and outputs a structured plan classifying items as 'Keep', 'Change', or 'Investigate'.

What format should course evaluations be in for automated teaching improvement analysis?▼

Course evaluations should be in a CSV format for automated teaching improvement analysis, as demonstrated by the quick start command which processes a file named course-evaluation.csv to parse student feedback.