Where is my feedback data stored?

All feedback data, user preferences, and consent logs are stored locally in the .claude/feedback/ directory within your project.

Can I reset the learned patterns?

Yes, you can use the /feedback reset command to clear all learned permission patterns, agent metrics, and style preferences to start fresh.

Is sharing anonymous analytics required?

No, sharing is strictly opt-in. By default, all feedback data stays completely local to your machine unless you explicitly choose to support improvement via /feedback opt-in.

Does the feedback skill share my code?

No, the feedback system is designed with a privacy-first approach; it never shares code, file contents, or personal information, even when anonymous analytics are enabled.

How does the learning system benefit me?

It learns your preferred coding styles and auto-approves frequent commands, reducing manual overhead and saving significant context tokens during AI sessions.

SkillForge Feedback Manager

Name: SkillForge Feedback Manager
Author: yonatangross

byyonatangross

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Analytics & Monitoring

Manages the adaptive learning system and privacy settings to improve AI performance based on user interactions.

About

The Feedback skill provides a comprehensive interface for managing how SkillForge learns from your development patterns and usage habits. It allows users to monitor agent performance metrics, toggle learning modes, manage data retention, and control privacy through granular opt-in/opt-out settings for anonymous analytics, ensuring the AI assistant evolves according to individual preferences while maintaining strict security boundaries and local data control.

Key Features

Exportable feedback data and analytics for transparent user review
Configurable data retention periods and master privacy switches
Real-time status monitoring of learned patterns and agent performance
GDPR-compliant anonymous analytics management with explicit opt-in requirements
Granular control over learning behaviors with pause, resume, and reset options
29 GitHub stars

Use Cases

Clearing outdated code style preferences or auto-approval rules to retrain the AI
Optimizing AI agent performance based on local project success rates and interaction history
Managing privacy and consent logs for organizational security compliance

About

Key Features

Exportable feedback data and analytics for transparent user review
Configurable data retention periods and master privacy switches
Real-time status monitoring of learned patterns and agent performance
GDPR-compliant anonymous analytics management with explicit opt-in requirements
Granular control over learning behaviors with pause, resume, and reset options
29 GitHub stars

Use Cases

Clearing outdated code style preferences or auto-approval rules to retrain the AI
Optimizing AI agent performance based on local project success rates and interaction history
Managing privacy and consent logs for organizational security compliance