interactive-feedback-evaluator

Name: interactive-feedback-evaluator
Availability: InStock
Author: ttmouse

Collect and summarize feedback via sidebar web forms with Flask and JSON storage.

35|10|Updated Dec 29, 2025

Search Tags:#automation #feedback #evaluation #reporting #data collection #forms

Ready to run

npx skills add https://github.com/ttmouse/skills --skill interactive-feedback-evaluator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: interactive-feedback-evaluator
Source: https://github.com/ttmouse/skills/tree/main/interactive-feedback-evaluator
Command: npx skills add https://github.com/ttmouse/skills --skill interactive-feedback-evaluator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires flask, requests, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a standardized, interactive system for collecting and summarizing feedback directly within the Alma sidebar, eliminating manual data collection and analysis.

Core Features & Use Cases

Interactive Forms: Users can fill out evaluation forms directly in the sidebar.
Automated Data Collection: Feedback is automatically captured and stored.
AI-Powered Summaries: Generates structured summaries from collected feedback.
Use Case: Evaluate analysis reports, skill effectiveness, or product features using pre-defined or custom templates, with AI providing a concise summary of the feedback.

Quick Start

Use the interactive-feedback-evaluator skill to evaluate the latest analysis report.

Frequently Asked Questions about interactive-feedback-evaluator

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

FAQPage Schema

How do I collect and summarize user feedback using an interactive web form?▼

To collect and summarize user feedback, this system provides interactive web forms integrated into the AI sidebar, automatically capturing submissions and generating AI-powered structured summaries from the data.

How do I set up a Flask backend to automate feedback collection and reporting?▼

You need to run the provided scripts to start a Flask-based backend server that handles web form generation and submission, persisting all collected feedback data locally in JSON format.

Can I evaluate skill effectiveness and product features directly within the AI assistant sidebar?▼

Yes, you can evaluate skill effectiveness, product features, and analysis reports directly within the sidebar using pre-defined or custom evaluation templates without requiring external interfaces.

Does the automated feedback evaluation system require an external database?▼

No, the automated feedback evaluation system does not require an external database; all submitted form data is persisted locally on your server in a lightweight JSON file format.

What is the best way to generate AI summaries from collected evaluation data?▼

The best way to generate AI summaries is to use this built-in system, which automatically processes the locally stored JSON feedback data to produce concise, structured summaries.

What are the limitations of using JSON for persistent feedback data storage?▼

Using JSON for persistent feedback data storage limits scalability and concurrent write performance compared to relational databases, making it less suitable for high-traffic, enterprise-level data collection.