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Use when you need to validate deployments before or after release with automated testing, health checks, and smoke tests. Ensures application quality by running integration tests, monitoring metrics, and checking service health across environments.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | validate-deployment |
| description | Use when you need to validate deployments before or after release with automated testing, health checks, and smoke tests. Ensures application quality by running integration tests, monitoring metrics, and checking service health across environments. |
| license | AGPLv3 |
| metadata | {"author":"agentic-reconciliation-engine","version":"1.0","category":"enterprise","risk_level":"medium","autonomy":"conditional","layer":"temporal"} |
| compatibility | Requires Python 3.8+, cloud provider CLI tools (AWS CLI, Azure CLI, gcloud), and access to multi-cloud monitoring systems |
| allowed-tools | Bash Read Write Grep |
Enterprise-grade automation solution for deployment validation operations across AWS, Azure, GCP, and on-premise environments to maximize operational efficiency while maintaining security and compliance standards.
aws|azure|gcp|onprem|all (optional, default: all)production)true)core/scripts/automation/deployment-validation.py: Main automation implementationcore/scripts/automation/deployment-validation_handler.py: Cloud-specific operationscore/scripts/automation/multi_cloud_orchestrator.py: Cross-provider coordinationdeployment, validation, automation, enterprise, multi-cloud, aws, azure, gcp, onprem
Load these files when needed:
scripts/validation-engine.py - Core validation logic and test executionscripts/health-checker.py - Health check implementation and monitoringreferences/test-templates.md - Standard validation test templatesassets/validation-criteria.yaml - Validation rules and pass/fail criteriaexamples/validation-reports/ - Sample validation reports and formatsFully autonomous decision engine with trial-and-error learning capabilities. Executes infrastructure operations without human intervention while leveraging reconciliation engines as safety nets. Learns from outcomes to improve future decisions through pattern recognition and adaptive optimization.
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