---
name: lloydchang/summarize-incidents
source: https://app.decimal.ai/s/lloydchang-summarize-incidents@1/SKILL.md
source_sha256: aadedd1126f2
---

# Incident Summary — Multi-Cloud Enterprise Automation with Advanced AI

## Purpose
Enterprise-grade automation solution for incident summary operations across AWS, Azure, GCP, and on-premise environments with advanced AI capabilities including NLP-powered summarization, machine learning root cause analysis, and predictive incident insights to maximize operational efficiency while maintaining security and compliance standards.

## When to Use
- **incident summary operations** across multi-cloud environments
- **Advanced AI analysis** including NLP summarization and ML root cause detection
- **Automation and optimization** of incident summary workflows
- **Monitoring and management** of incident summary resources
- **Compliance and governance** for incident summary activities
- **Predictive incident prevention** using historical pattern analysis

## Inputs
- **operation**: Operation type (required)
- **targetResource**: Target resource identifier (required)
- **cloudProvider**: Cloud provider - `aws|azure|gcp|onprem|all` (optional, default: `all`)
- **parameters**: Operation-specific parameters including AI model configurations (optional)
- **environment**: Target environment (optional, default: `production`)
- **dryRun**: Dry run mode (optional, default: `true`)
- **historicalData**: Include historical incident data for pattern analysis (optional, default: `false`)

## Process
1. **Cloud Provider Detection**: Identify target cloud providers and environments
2. **Input Validation**: Comprehensive parameter validation and security checks
3. **Multi-Cloud Context Analysis**: Analyze current state across all providers
4. **AI-Powered Analysis**: Apply NLP summarization, ML clustering for root causes, and predictive modeling
5. **Operation Planning**: Generate optimized execution plan with AI insights
6. **Safety Assessment**: Risk analysis and impact evaluation across providers
7. **Execution**: Perform operations with monitoring and validation
8. **Results Analysis**: Process results and generate AI-enhanced reports

## Outputs
- **Operation Results**: Detailed execution results and status per provider
- **AI-Enhanced Summaries**: NLP-generated incident summaries and post-mortems
- **ML Root Cause Analysis**: Clustered and prioritized potential causes
- **Predictive Insights**: Recommendations for incident prevention
- **Compliance Reports**: Validation and compliance status across environments
- **Performance Metrics**: Operation performance and efficiency metrics by provider
- **Recommendations**: Optimization suggestions and next steps
- **Audit Trail**: Complete operation history for compliance across all providers

## Environment
- **AWS**: EKS, EC2, Lambda, CloudWatch, IAM, S3
- **Azure**: AKS, VMs, Functions, Monitor, Azure AD
- **GCP**: GKE, Compute Engine, Cloud Functions, Cloud Monitoring
- **On-Premise**: Kubernetes clusters, VMware, OpenStack, Prometheus
- **Multi-Cloud Tools**: Terraform, Ansible, Crossplane, Cluster API

## Dependencies
- **Python 3.8+**: Core execution environment
- **AI/ML Libraries**: scikit-learn, nltk, transformers, pandas, numpy
- **Cloud SDKs**: boto3, azure-sdk, google-cloud
- **NLP Tools**: spaCy, textblob for natural language processing
- **Kubernetes**: kubernetes client for cluster operations
- **Multi-Cloud Libraries**: terraform-python, ansible-python

## Scripts
- `core/scripts/automation/incident-summary.py`: Main automation implementation with AI capabilities
- `core/scripts/automation/incident-summary_handler.py`: Cloud-specific operations
- `core/scripts/automation/multi_cloud_orchestrator.py`: Cross-provider coordination

## Trigger Keywords
incident, summary, automation, enterprise, multi-cloud, aws, azure, gcp, onprem, ai, nlp, ml, predictive, root-cause

## Human Gate Requirements
- **Production changes**: Production environment operations require approval
- **High-impact operations**: Critical operations require review
- **Security changes**: Security modifications need validation
- **AI Model Updates**: Changes to AI models require approval

## Enterprise Features
- **Multi-tenant Support**: Isolated operations per tenant
- **Role-based Access Control**: Enterprise IAM integration
- **Audit Logging**: Complete audit trail for compliance
- **Performance Monitoring**: SLA tracking and metrics
- **Security Hardening**: Encryption and compliance standards
- **Dynamic Code Generation**: Agents can modify logic dynamically
- **Cross-Cloud Orchestration**: Coordinated operations across providers
- **AI-Powered Insights**: Advanced analytics and predictive capabilities
- **Automated Learning**: Continuous improvement from incident patterns

## Best Practices
- **Idempotent Operations**: Safe retry mechanisms
- **Circuit Breaker Patterns**: Resilience against failures
- **Rate Limiting**: Respect API limits and implement backpressure
- **Graceful Degradation**: Fallback strategies when providers are unavailable
- **Comprehensive Testing**: Integration tests and compliance validation
- **Security First**: Zero-trust architecture and principle of least privilege
- **AI Model Validation**: Regular validation of ML models and NLP accuracy
- **Data Privacy**: Ensure incident data handling complies with privacy regulations