How does the Feedback Collector trigger automatically?

It triggers at the end of a conversation, after completing significant work, or when the system detects user frustration or manual corrections.

How does the skill help implement changes?

After capturing feedback, the skill proposes specific actionable steps, such as updating a plugin or simplifying a workflow, and asks for your approval to implement them.

What kind of feedback does this skill categorize?

It organizes feedback into specific categories including Usability, Accuracy, Missing Features, Documentation, Agent Behavior, Process, and Performance.

Where is the gathered feedback stored?

Feedback is stored inline within conversation-exit documents or aggregated in a central 'Brain Repo' for long-term pattern recognition and tracking.

Feedback Collector

Name: Feedback Collector
Author: violetio

byvioletio

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Productivity & Workflow

Systematically gathers, categorizes, and acts on user feedback to continuously improve AI agent interactions and performance.

About

The Feedback Collector skill empowers AI agents to proactively solicit and manage user feedback throughout the development lifecycle. By automatically triggering at key milestones—such as session endings, task completions, or user corrections—it ensures that critical insights are captured and organized. The skill classifies feedback into structured categories like usability, accuracy, and performance, identifies recurring patterns, and proposes specific actionable improvements to refine agent behavior and streamline workflows.

Key Features

Pattern recognition to identify recurring blockers and friction points
1 GitHub stars
Structured categorization across usability, accuracy, and performance
Integrated feedback logging for long-term knowledge base refinement
Actionable improvement proposals with built-in implementation tracking
Automated feedback triggers at session exits and task milestones

Use Cases

Optimizing team workflows by tracking and resolving process bottlenecks
Improving AI agent behavior based on real-world user corrections and frustrations
Identifying knowledge gaps or missing features during complex development sessions

About

Key Features

Pattern recognition to identify recurring blockers and friction points
1 GitHub stars
Structured categorization across usability, accuracy, and performance
Integrated feedback logging for long-term knowledge base refinement
Actionable improvement proposals with built-in implementation tracking
Automated feedback triggers at session exits and task milestones

Use Cases

Optimizing team workflows by tracking and resolving process bottlenecks
Improving AI agent behavior based on real-world user corrections and frustrations
Identifying knowledge gaps or missing features during complex development sessions