Regression Gates Skill

Detect model metric regressions and block CI/CD deployments.

2|1|Updated Apr 19, 2026
One-click install
npx skills add https://github.com/reaatech/agents-md-kit --skill regression-gates-skill
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Regression Gates Skill
Source: https://github.com/reaatech/agents-md-kit/tree/main/examples/evaluator/skills/regression-gates
Command: npx skills add https://github.com/reaatech/agents-md-kit --skill regression-gates-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps DevOps and engineers prevent flawed deployments by detecting regressions in model metrics and halting release pipelines.

Core Features & Use Cases

  • Automated regression checks against baselines for model metrics (accuracy, F1, latency, cost).
  • CI/CD gate integration that blocks deployments when regressions are detected and provides detailed feedback.
  • Baseline management via MCP tools to set, get, and compare baselines for consistent quality gates.

Quick Start

Set up automated regression checks in your CI/CD to block deployments when current metrics regress relative to the baseline.

Frequently Asked Questions about Regression Gates Skill

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

FAQPage Schema
How do I block deployments when model metrics regress in a CI/CD pipeline?

You can block deployments from model metrics regressions by integrating automated quality gates into your CI/CD pipeline that compare current metrics against baselines and halt releases on failure.

What is a regression gate and how does it prevent degraded performance?

A regression gate is an automated quality check that detects degraded model performance by comparing current metrics like accuracy or latency against a baseline, returning a pass or fail result.

How do I set up baseline management for automated regression checks?

Automated regression checks require baseline management via MCP tools to set, get, and compare baseline metrics, ensuring consistent quality gate evaluations across versions.

Can I configure custom thresholds for model metric regressions?

Yes, configurable thresholds are supported for model metric regression checks, allowing you to define acceptable variance limits for metrics like accuracy, F1, latency, and cost before blocking a release.

What metrics can I monitor to detect regressions before a release?

You can monitor model metrics such as accuracy, F1, latency, and cost to detect regressions, utilizing real-time metric comparisons to prevent degraded performance from reaching production.