kedify/kedify
v0.1.0
Set up and diagnose KEDA autoscaling with the Kedify OTel add-on and existing app metrics. Retrieve Kubernetes CPU and memory recommendations through Kedify MCP or local kedr, apply selected changes through GitOps or kubectl, and inspect Kedify organizations, clusters, agents, savings, and autoscaling through MCP.
hosts
- mcp.kedify.io
plugin.json
- name
- kedify
- version
- 0.1.0
- description
- Set up and diagnose KEDA autoscaling with the Kedify OTel add-on and existing app metrics. Retrieve Kubernetes CPU and memory recommendations through Kedify MCP or local kedr, apply selected changes through GitOps or kubectl, and inspect Kedify organizations, clusters, agents, savings, and autoscaling through MCP.
- author
- Kedifyhttps://kedify.io
- homepage
- https://kedify.io
- repository
- https://github.com/kedify/skills
- extensions
- com.openai
mcp.json
- kedifystreamable-httphttps://mcp.kedify.io/mcp
{
"$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
"name": "kedify",
"version": "0.1.0",
"description": "Set up and diagnose KEDA autoscaling with the Kedify OTel add-on and existing app metrics. Retrieve Kubernetes CPU and memory recommendations through Kedify MCP or local kedr, apply selected changes through GitOps or kubectl, and inspect Kedify organizations, clusters, agents, savings, and autoscaling through MCP.",
"author": {
"name": "Kedify",
"url": "https://kedify.io"
},
"homepage": "https://kedify.io",
"repository": "https://github.com/kedify/skills",
"keywords": ["kubernetes", "resource-optimization", "gitops", "kedr", "mcp", "opentelemetry", "keda", "autoscaling"],
"extensions": {
"com.openai": {
"interface": {
"displayName": "Kedify",
"shortDescription": "Kubernetes tuning and autoscaling",
"longDescription": "Set up and diagnose KEDA autoscaling through the Kedify OTel add-on using existing app metrics and monitoring pipelines. OTel workflows use local tools without a Dashboard account; they can prepare configuration without cluster access. Retrieve CPU and memory recommendations through Kedify MCP or local kedr and apply changes through GitOps or kubectl. Inspect Kedify organizations, clusters, agents, savings, and autoscaling diagnostics through MCP. MCP recommendations require a running Kedify agent with namespace metrics collection and sufficient history. Local kedr and live application require direct cluster access.",
"developerName": "Kedify",
"category": "Developer Tools",
"capabilities": ["Read", "Write"],
"websiteURL": "https://kedify.io",
"defaultPrompt": [
"Get CPU and memory recommendations for my Kubernetes workloads.",
"Set up OTel-based KEDA autoscaling for my app using our existing metrics.",
"Diagnose why my OTel add-on metric is not scaling the workload."
],
"brandColor": "#5B42FF",
"composerIcon": "./assets/kedify.png",
"logo": "./assets/kedify.png"
}
}
}
}
What else this package ships
These files come with the package and this site does not publish them. They are listed so you know what is there before you install it.
- 2YAML files
Client extensions
Data this package carries for particular clients. The directory lists the clients named and never reads what is addressed to them.
- com.openai