What is a mega-skill in the context of Claude Flow?

A mega-skill is a specialized capability that consolidates multiple related sub-skills into a unified workflow, such as combining deployment and promotion into one lifecycle manager.

How does this skill handle deployment failures?

It utilizes atomic transactions to ensure that status transitions only finalize if the entire process succeeds, preventing corrupted or partial deployments.

Can I enforce specific performance metrics during promotion?

Yes, the skill includes built-in SLA enforcement to ensure that skills meet required standards before moving from staging to production.

Does this replace the older cfn-deployment and cfn-promotion skills?

Yes, version 1.0.0 is designed as a unified replacement that consolidates the functionality of both legacy skills into a single directory structure.

Deployment Lifecycle Manager

Name: Deployment Lifecycle Manager
Author: masharratt

bymasharratt

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Deployment & DevOps

Manages the end-to-end skill deployment and promotion lifecycle with atomic transactions and SLA enforcement.

About

The Deployment Lifecycle Manager is a comprehensive mega-skill designed to automate the transition of AI capabilities from approved status to full production deployment. By consolidating deployment and promotion logic, it ensures that skills move through staging and production environments with strict Service Level Agreement (SLA) enforcement and atomic transactions. This skill is essential for developers looking to maintain high reliability and consistent performance when orchestrating complex AI agent workflows.

Key Features

Simplified migration path for consolidated legacy skills
Automated promotion workflows from staging to production environments
Unified management of deployment and promotion logic
12 GitHub stars
Strict SLA enforcement during the deployment process
Atomic transactions for stable APPROVED to DEPLOYED transitions

Use Cases

Enforcing organizational performance SLAs during infrastructure updates
Safely promoting AI agents from staging to high-stakes production environments
Transitioning validated skills from development to active deployment status

About

Key Features

Simplified migration path for consolidated legacy skills
Automated promotion workflows from staging to production environments
Unified management of deployment and promotion logic
12 GitHub stars
Strict SLA enforcement during the deployment process
Atomic transactions for stable APPROVED to DEPLOYED transitions

Use Cases

Enforcing organizational performance SLAs during infrastructure updates
Safely promoting AI agents from staging to high-stakes production environments
Transitioning validated skills from development to active deployment status