multi-namespace-deployment-analyzer - SKILL.md Agent Skill

name: multi-namespace-deployment-analyzer description: When the user needs to analyze deployments across multiple Kubernetes namespaces with filtering criteria. This skill discovers namespaces matching patterns, retrieves deployment information from each namespace, extracts metadata and annotations, and performs comparative analysis across namespaces. It handles batch operations and consolidates results from multiple namespace queries. Triggers include 'namespaces starting with', 'across namespaces', 'multiple namespaces', 'deployment analysis', 'namespace pattern', 'batch deployment query', 'cross-namespace comparison'.

Instructions

Overview

This skill analyzes Kubernetes deployments across multiple namespaces based on filtering criteria. It discovers namespaces matching patterns, retrieves deployment information, extracts metadata/annotations, performs comparative analysis, and can execute batch operations.

Core Workflow

1. Discover Target Namespaces

Use k8s-kubectl_get to list all namespaces
Filter namespaces based on user criteria (e.g., names starting with 'dev-')
Store the filtered namespace list for subsequent operations

2. Retrieve Deployments from Each Namespace

For each target namespace, use k8s-kubectl_get to list deployments
Collect deployment metadata: name, namespace, status, creation timestamp
Handle empty namespaces gracefully

3. Extract Detailed Deployment Information

For each deployment, use k8s-kubectl_describe to get detailed information
Focus on extracting annotations, particularly app-version-release-date or similar version metadata
Note container images, resource limits, and other relevant configuration

4. Perform Analysis

Calculate age of deployments based on release date annotations
Apply filtering criteria (e.g., "more than 30 days ago")
Sort results chronologically (oldest to newest)
Count affected deployments for reporting

5. Execute Batch Operations (If Required)

Based on analysis results, perform actions like scaling deployments to 0 replicas
Use k8s-kubectl_scale for each outdated deployment
Verify operations completed successfully

6. Identify Responsible Personnel

Search for contact information using multiple approaches:
- Search emails for relevant keywords ("cluster management", "cluster admin", "kubernetes", "infrastructure")
- Examine available files and directories for contact information
- Check PDF documents for organizational charts or responsibility matrices
- Look for configuration files that might contain contact details

7. Generate and Send Report

Compile results in the specified format
Include: number of affected deployments, sorted list with namespace/deployment names and days since release
Use emails-send_email to notify the identified responsible party
Use professional email format with clear subject and body

Key Considerations

Error Handling

Handle cases where no namespaces match the pattern
Handle deployments without version annotations gracefully
Provide clear feedback when contact information cannot be found
Verify email sending was successful

Performance Optimization

Batch namespace queries when possible
Cache results to avoid redundant API calls
Process deployments in parallel where appropriate

Output Formatting

Always sort deployments chronologically from oldest to newest
Include both namespace and deployment name in listings
Calculate days accurately based on current date
Use consistent formatting throughout the report

Common Patterns

Namespace filtering: typically prefixes like 'dev-', 'staging-', 'prod-'
Age filtering: 30, 60, 90 days common thresholds
Actions: scale to 0, delete, annotate, or just report
Contacts: cluster admins, team leads, infrastructure managers