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guihousun
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workflow-self-evolution

Filter failures, guide learning decisions, and manage version control for NTL skills.

Updated Feb 19, 2026
Search Tags:#automation#metrics#self-evolution#version-control#adaptive-learning#failure-classification#quality-tracking
One-click install
npx skills add https://github.com/guihousun/NTL-GPT-Clone --skill workflow-self-evolution
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: workflow-self-evolution
Source: https://github.com/guihousun/NTL-GPT-Clone/tree/main/.ntl-gpt/skills/workflow-self-evolution
Command: npx skills add https://github.com/guihousun/NTL-GPT-Clone --skill workflow-self-evolution

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides universal self-evolution and adaptive learning capabilities for all NTL skills, enabling intelligent failure filtering, learning decisions, version control, and metrics tracking to drive continuous improvement.

Core Features & Use Cases

  • Intelligent failure filtering (81% noise reduction) to focus learning on valuable failures.
  • Learning decision engine that determines when to learn, monitor, or provide feedback.
  • Automatic version backups before updates and one-click rollback for safe deployments.
  • Real-time quality metrics tracking and trend analysis to guide optimization.
  • Automated optimization support, including A/B testing frameworks, for continuous improvement across skills.

Quick Start

After every task execution, run the self-evolution protocol to log results, classify failures, and apply the appropriate learning or rollback actions.

Frequently Asked Questions about workflow-self-evolution

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

FAQPage Schema
How do I automate failure classification and adaptive learning for my workflows?▼

Adaptive learning is automated by applying a 5-category failure taxonomy to filter execution results, guiding learning decisions, and triggering mandatory reviews to continuously improve workflow performance.

What is the best way to reduce noise from execution failures before updating versions?▼

Intelligent failure filtering reduces noise by 81% using a taxonomy of systemic, recurring, transient, user_error, and external failures, ensuring only valuable failures drive version updates.

How do I safely rollback workflow updates after an automated optimization?▼

Safe rollback is enabled through automatic version backups created before updates, allowing one-click restoration to previous states if new versions degrade performance or cause issues.

When do I need to trigger a mandatory review during self-evolution?▼

A mandatory review is triggered after every task execution logs results and applies learning or rollback actions, ensuring continuous quality monitoring and safe adaptive learning.

Can I track real-time quality metrics and trends for automated workflows?▼

Real-time quality metrics tracking and trend analysis are supported natively, guiding optimization decisions and providing measurable performance insights across all workflow executions.

Does this self-evolution framework support A/B testing for continuous improvement?▼

Automated optimization support includes an A/B testing framework, enabling continuous improvement by comparing different workflow versions and applying the winning adaptations safely.

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