Is it safe to let the skill update my configurations automatically?

While manual approval is the default, the '--auto' flag only applies suggestions with a confidence score of 0.8 or higher, and the system creates a backup before every modification.

How do I undo an optimization that didn't work as expected?

You can use the '/learn:rollback' command followed by the change ID to immediately restore the previous configuration from the automated backup.

How does the Self-Improve skill identify potential improvements?

It analyzes patterns and success metrics stored in the Supabase backend, identifying correlations between specific configurations and successful task completions or failures.

Can I preview changes before they are applied to my project?

Yes, you can use the '/learn:apply --preview' command to see exactly what modifications will be made to your configuration files without committing them.

Aura Frog Self-Improve

Name: Aura Frog Self-Improve
Author: nguyenthienthanh

bynguyenthienthanh

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Productivity & Workflow

Optimizes the Aura Frog development environment by automatically applying data-driven improvements to agents, rules, and workflows.

About

The Self-Improve skill is a meta-utility designed to evolve the Aura Frog plugin by learning from your project's unique development patterns. It analyzes agent success rates and workflow bottlenecks to suggest precise optimizations, such as reconfiguring agent routing, adjusting TDD workflow timeouts, and updating architectural rules. By transforming historical performance data into actionable configuration changes, it ensures that your Claude Code setup becomes increasingly efficient, specialized, and reliable as your project grows.

Key Features

Data-driven agent routing and scoring optimization
Automated configuration updates for TDD workflows
Comprehensive backup and rollback safety system
3 GitHub stars
Dynamic rule adjustment based on project patterns
Automated knowledge base entry generation

Use Cases

Optimizing agent selection for specific file types based on past success rates
Fine-tuning workflow timeouts and batch sizes to prevent CI/CD bottlenecks
Updating project-wide quality gates like code coverage thresholds automatically

About

Key Features

Data-driven agent routing and scoring optimization
Automated configuration updates for TDD workflows
Comprehensive backup and rollback safety system
3 GitHub stars
Dynamic rule adjustment based on project patterns
Automated knowledge base entry generation

Use Cases

Optimizing agent selection for specific file types based on past success rates
Fine-tuning workflow timeouts and batch sizes to prevent CI/CD bottlenecks
Updating project-wide quality gates like code coverage thresholds automatically