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Parses Terraform, CloudFormation, Kubernetes manifests, and other IaC formats to identify resource modifications, deletions, and creations, then simulates the changes against current infrastructure state to detect conflicts, resource constraints, and potential service disruptions.","intents":["I want to understand what infrastructure changes will happen before applying them","I need to detect if an IaC change will cause service downtime or resource conflicts","I want to validate that infrastructure changes are compatible with current deployments"],"best_for":["teams using IaC for infrastructure management (Terraform, CloudFormation, Helm)","organizations needing pre-apply validation of infrastructure changes","DevOps teams managing complex cloud infrastructure with multiple environments"],"limitations":["impact analysis accuracy depends on complete and current state representation — drift between IaC and actual infrastructure reduces accuracy","cannot predict dynamic resource behavior (auto-scaling, load balancing) without runtime simulation","cross-cloud provider analysis requires separate parsers and state models for each cloud (AWS, GCP, Azure)","cannot detect impacts on external systems or third-party services not represented in IaC"],"requires":["Infrastructure-as-Code files (Terraform, CloudFormation, Kubernetes YAML, Helm charts, etc.)","current infrastructure state (from cloud provider APIs or state files)","resource dependency metadata","service health check definitions","change diff between current and proposed IaC"],"input_types":["IaC files (HCL, JSON, YAML)","infrastructure state snapshots","change diffs","resource constraints and limits","service dependency definitions"],"output_types":["impact assessment report","resource change summary (create/modify/delete)","risk classification and affected services","downtime prediction","remediation suggestions"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prodeai__cap_6","uri":"capability://planning.reasoning.performance.regression.detection.and.analysis","name":"performance regression detection and analysis","description":"Monitors application performance metrics and automatically detects regressions by comparing current performance against historical baselines. 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Detects unauthorized changes, missing configurations, and inconsistencies across services, then automatically remediates drift by reapplying the correct configuration or alerting for manual review.","intents":["I want to detect when someone manually changes production configuration outside of version control","I need to ensure all services are running with the correct configuration","I want to automatically fix configuration drift without manual intervention"],"best_for":["teams using IaC and configuration management requiring drift detection","organizations with compliance requirements for configuration auditing","production teams needing automated configuration consistency enforcement"],"limitations":["drift detection requires continuous monitoring and state comparison — adds overhead to production systems","cannot distinguish between intentional manual changes and accidental drift without change tracking","automatic remediation may conflict with legitimate manual changes — requires careful tuning of remediation policies","distributed systems with eventual consistency may show false positives during propagation delays"],"requires":["access to production configuration (environment variables, config files, cloud provider settings)","declared configuration source (IaC, config management system, version control)","monitoring infrastructure for continuous state comparison","change tracking and audit logs","remediation mechanisms (configuration reapplication, service restart)"],"input_types":["actual configuration state","declared configuration","change logs and audit trails","service definitions","environment specifications"],"output_types":["drift detection report","configuration differences (actual vs declared)","remediation actions and status","audit trail of configuration changes","compliance verification"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prodeai__cap_8","uri":"capability://planning.reasoning.service.dependency.mapping.and.visualization","name":"service dependency mapping and visualization","description":"Automatically discovers and maps service dependencies by analyzing code imports, API calls, database connections, and message queue subscriptions across the codebase. Builds a dynamic dependency graph that reflects actual service interactions, identifies circular dependencies and single points of failure, and visualizes the service topology to help teams understand system architecture and impact of changes.","intents":["I need to understand how services are connected and what the blast radius of a change would be","I want to identify circular dependencies and single points of failure in my architecture","I need to visualize the service topology for documentation and onboarding"],"best_for":["teams managing microservices architectures with complex dependencies","organizations needing architecture documentation and visualization","teams performing impact analysis for deployments and changes"],"limitations":["dependency discovery accuracy depends on code analysis quality — dynamic dependencies (reflection, plugin systems) may be missed","cannot detect runtime dependencies that are not represented in code (manual API calls, undocumented integrations)","dependency graph may become stale if services are added/removed without code updates","visualization complexity increases exponentially with service count — large systems may be difficult to visualize effectively"],"requires":["access to source code across all services","code analysis infrastructure (AST parsing, import resolution)","API and integration metadata","deployment and service registry information","visualization infrastructure"],"input_types":["source code","API definitions (OpenAPI, gRPC, etc.)","database schemas","message queue configurations","service registry data"],"output_types":["dependency graph (nodes and edges)","service topology visualization","dependency analysis (circular dependencies, single points of failure)","impact analysis for changes","architecture documentation"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prodeai__cap_9","uri":"capability://data.processing.analysis.intelligent.log.aggregation.and.pattern.extraction","name":"intelligent log aggregation and pattern extraction","description":"Aggregates logs from multiple sources and automatically extracts meaningful patterns using statistical analysis and machine learning. Groups similar log entries to reduce noise, identifies recurring error patterns and anomalies, and correlates logs across services to trace requests through the system. 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