feedback-writer

Name: feedback-writer
Availability: InStock
Author: zxpmail

Capture user feedback on AI performance and record it in a structured format.

18|4|Updated May 16, 2026

Search Tags:#user experience #performance monitoring #AI feedback #feedback analysis #AI assessment

Ready to run

npx skills add https://github.com/zxpmail/ReqForge --skill feedback-writer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: feedback-writer
Source: https://github.com/zxpmail/ReqForge/tree/main/core/skills/feedback-writer
Command: npx skills add https://github.com/zxpmail/ReqForge --skill feedback-writer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

The feedback-writer Skill unit addresses the need to systematically record and evaluate user feedback on AI behavior, aiding in the improvement and refinement of AI models.

Core Features & Use Cases

Feedback Capture: Collects structured feedback when users correct AI behavior or request improvements.
Feedback Analysis: Assesses the quality and relevance of feedback based on predefined criteria.
Feedback Recording: Stores feedback in a structured format for further analysis and model evolution.
Use Case: After a user corrects an AI response, the feedback-writer Skill captures the feedback, evaluates it, and records it for analysis by the feedback-observer sub-agent.

Quick Start

Execute the feedback-writer Skill with the command '/feedback-writer' after the AI has been corrected or a capability assessment is required.

Frequently Asked Questions about feedback-writer

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

FAQPage Schema

How do I record user feedback on AI behavior for continuous improvement?▼

To capture user feedback on AI behavior for continuous improvement, the feedback-writer records corrections in a structured format. It evaluates feedback quality based on predefined criteria to prepare data for model evolution.

What is the best way to structure AI performance monitoring data for analysis?▼

To structure AI performance monitoring data for analysis, use a feedback recording mechanism that evaluates user corrections. This ensures assessments are captured systematically for further analysis by observer sub-agents.

How does AI assessment work when users correct model responses?▼

AI assessment during user corrections works by capturing the feedback, evaluating its quality against predefined criteria, and storing structured data. This enables systematic analysis for AI model refinement.

Do I need a feedback-observer sub-agent to use feedback recording workflows?▼

Yes, a feedback-observer sub-agent is needed to analyze structured feedback recorded by this workflow. The feedback-writer requires access to the feedback directory and context data from the observer to function properly.

When should I trigger structured feedback capture for AI systems?▼

Trigger structured feedback capture for AI systems immediately after a user corrects an AI response or requests a capability assessment. This ensures accurate user experience data is recorded for analysis.