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Investigate stolen Kerberos tickets, authentication credentials that can be reused without a password, by correlating Windows security events. Build Splunk or Elastic detection rules for cross-host reuse, weaker encryption, and unusual request volumes, and report risk-scored findings.

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• Investigating suspected reuse of stolen Kerberos tickets
• Building Splunk or Elastic detection rules
• Finding unusual ticket request volumes

Build and maintain Elasticsearch search and analytics systems with guidance on queries, field types, and aggregations. Plan index migrations, automate data retention, diagnose cluster allocation problems, and investigate slow searches.

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• Designing full-text search and autocomplete
• Aggregating logs and time-series data
• Planning index migrations and data retention

Build or tune a monitoring pipeline that learns normal user behavior from activity logs. Use Elasticsearch or OpenSearch to score unusual activity, compare colleagues in similar roles, and combine warning signs into insider threat alerts.

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• Building continuous monitoring for insider threats
• Scoring unusual logins and large downloads
• Comparing behavior across similar job roles

Create threat detection rules in Sigma, a shared format for security log searches, and convert them for Splunk, Elastic, or Microsoft Sentinel. Validate syntax, test expected matches and false positives, and map coverage to MITRE ATT&CK.

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• Turning threat reports into detection rules
• Reusing detection logic across SIEM platforms
• Testing rule matches and reducing false positives

Builds and deploys containerized workloads on Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Fargate, and ECR (Elastic Container Registry). Covers general EKS knowledge, Karpenter, AWS Load Balancer Controller and leveraging various open source Kubernetes projects with EKS. Covers general ECS knowledge, task definitions, Fargate services, ECS Exec, ECS Express Mode and ECS Managed Instances. Covers general Elastic Beanstalk knowledge, Elastic Beanstalk configuration and platforms supported by Elastic Beanstalk. Covers general ECR knowledge, ECR repository setup and lifecycle policies. Includes recommending, enabling, and reading Amazon ECS Action Logs to troubleshoot control-plane failures (deployment rollback/circuit-breaker, task placement, scaling, task replacement). Applies when deploying, debugging, or optimizing containers on AWS. Should be used instead of relying on internal knowledge for these services.

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Create monitoring dashboards for Grafana, SigNoz, and similar platforms. Choose metrics around operational questions, organize panels by purpose, and define clear titles, units, filters, and thresholds to help operators assess health and bottlenecks.

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• Build a Kafka monitoring dashboard
• Monitor Elasticsearch health and performance
• Track API gateway latency and errors

Deploy Suricata to detect suspicious network traffic or block it inline. Configure protocol inspection, detection rules, and structured EVE JSON logs, then connect logs to analysis platforms and check packet drops and alert quality during operation.

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• Deploying network intrusion monitoring
• Configuring inline blocking of malicious traffic
• Adding custom detection rules and reducing noisy alerts

Elasticsearch provider for obz. Supports log search and trace operations using Elasticsearch Query DSL (Lucene-based). Requires --index flag. This skill should be used when the user mentions "Elasticsearch", "obz -p es", or needs to search logs or traces from an Elasticsearch cluster.

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Identify unused AWS resources, estimate potential savings, and prepare cleanup scripts with preview steps. Covers storage retention policies and scheduled cleanup so teams can review dependencies before removing resources and track the resulting savings.

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• Inventory unused resources and estimate savings
• Prepare cleanup scripts for EBS volumes and Elastic IPs
• Review S3 versions and storage retention rules

Expert knowledge for Azure Elastic SAN development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when creating Elastic SAN volumes, AVS datastores, iSCSI clients, snapshots, or clustered SQL/FC workloads, and other Azure Elastic SAN related development tasks. Not for Azure Blob Storage (use azure-blob-storage), Azure Files (use azure-files), Azure NetApp Files (use azure-netapp-files), Azure Virtual Machines (use azure-virtual-machines).

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Examine Windows and Active Directory logs for attempts to extract passwords and other credentials. Identify suspicious memory access and database exports, correlate events with user and host context, and produce findings with severity ratings and detection queries.

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• Investigating alerts about suspicious LSASS access
• Finding signs of SAM or NTDS.dit extraction
• Creating detection queries for Splunk or Elastic

Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification, lambda.min, lambda.1se. NOT for: survival/Cox modeling, multiclass outcomes, single-cell data, or non-expression tables.

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Deploy to AWS Elastic Beanstalk, in either Beanstalk Standard mode (EC2) or Beanstalk Cluster mode (managed EKS). Triggers on: elastic beanstalk, EB, beanstalk standard, beanstalk cluster, managed EC2 platform, web app with managed patching, worker on EC2, Heroku alternative, don't want to manage servers or container orchestration, migrate from Heroku, managed operational lifecycle, containerised app without operating Kubernetes, several services sharing a managed cluster, managed EKS without kubectl. Covers web and worker applications on EC2, and containerised single- and multi-service applications on managed EKS.

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Expert knowledge for Azure Container Storage development including troubleshooting, decision making, limits & quotas, security, and configuration. Use when configuring Elastic SAN-backed pools, CMK encryption, LRS/ZRS redundancy, volume resizing, or Prometheus/Grafana monitoring, and other Azure Container Storage related development tasks. Not for Azure Blob Storage (use azure-blob-storage), Azure Files (use azure-files), Azure Elastic SAN (use azure-elastic-san), Azure NetApp Files (use azure-netapp-files).

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Amazon Elasticsearch integration. Manage data, records, and automate workflows. Use when the user wants to interact with Amazon Elasticsearch data.

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Configure and use Elasticsearch for full-text search, custom analyzers, aggregations, and index management. Use when a user needs to design search mappings, write complex queries, build aggregation pipelines, tune relevance scoring, or optimize index performance for search workloads.

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"Calculate price elasticity of demand to quantify how price changes affect sales volume. Use this skill when the user needs to estimate demand sensitivity, set optimal prices, or evaluate the revenue impact of price changes — even if they say 'how sensitive are customers to price', 'will a price increase hurt sales', or 'elasticity calculation'.".

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Use when working with Aws Elasticache Deep — aWS ElastiCache deep analysis for Redis and Memcached clusters, replication health, failover analysis, and performance metrics. Covers node-level metrics, memory utilization, cache hit rates, eviction tracking, connection analysis, and engine-specific diagnostics.

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Expert en Elasticsearch (indexing, mappings, aggregations, Kibana, DSL queries)

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Investigate forged Kerberos authentication tickets in Active Directory by checking Windows logs for unusual encryption, excessive ticket lifetimes, and missing authentication requests. Produce a JSON report of suspicious indicators with risk ratings.

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• Investigating suspected forged authentication tickets
• Checking for possible krbtgt credential theft
• Building detection rules from authentication logs

Turn threat intelligence and detection gaps into testable investigation hypotheses. Use endpoint monitoring and security log queries to examine evidence, validate findings, connect related activity, and document risks and recommended actions in a consistent hunt report.

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• Planning a hunt based on threat intelligence
• Investigating gaps in ATT&CK coverage
• Testing hypotheses prompted by unusual activity

Build incident response dashboards that bring affected systems, containment progress, indicators of compromise, and response timelines into one view. Help security teams coordinate active incidents and prepare operational reports and executive briefings.

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• Coordinating an active security incident
• Tracking affected systems and containment
• Reviewing response timelines after an incident

Adapt machine learning training to run across multiple GPUs or machines with Ray Train. Covers changes to existing code, comparing training settings with Ray Tune, and saving checkpoints so training can resume after failures.

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• Adapting existing PyTorch code for multiple GPUs
• Training HuggingFace models across multiple machines
• Comparing learning rates and batch sizes with Ray Tune

Helps adapt machine learning training to multiple GPUs and machines with Ray Train. Covers resource allocation, recovery from saved training states, and Ray Tune searches that compare settings such as learning rate and batch size.

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• Adapt existing PyTorch training to multiple GPUs
• Run HuggingFace training across multiple machines
• Compare learning rates and batch sizes with Ray Tune