'monitoring' 검색 결과

Investigate Linux intrusions using auditd records. Configure audit rules, search events with ausearch, summarize activity with aureport, reconstruct a timeline of suspicious actions, and forward logs to a central security monitoring system.

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• Investigating unauthorized access or privilege escalation
• Checking changes to critical files and SSH keys
• Reconstructing an incident timeline

Investigate suspicious IP addresses, domains, URLs, file hashes, and email artifacts using multiple threat intelligence sources. Assess confidence that each indicator is malicious, check links to known campaigns, and document evidence for blocking, monitoring, or allowing it.

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• Triaging indicators from phishing emails
• Investigating suspicious IP addresses and URLs in alerts
• Assessing bulk indicators before adding them to blocklists

Improve PostgreSQL performance through a staged workflow covering slow query analysis, indexes that speed up data access, query rewrites, configuration tuning, routine maintenance, and monitoring for production databases.

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• Investigating slow PostgreSQL queries
• Reviewing database indexes
• Tuning database configuration

Develop Azure Functions with guidance for .NET, Python, and Node.js. Covers asynchronous processing, Durable Functions for coordinating tasks, startup delay optimization, and production concerns such as timeouts, HTTP client reuse, and monitoring configuration.

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• Choose an execution model for a new .NET function
• Coordinate asynchronous tasks with Durable Functions
• Review startup delays and timeout settings

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

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• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times

Design and implement machine learning systems for production use. Cover data preparation, training, prediction services, model comparisons, monitoring, and retraining, with attention to reliability, response time, resource costs, and both technical and business evaluation metrics.

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• Designing a real-time recommendation service
• Comparing model versions with A/B testing
• Detecting changes in data and model performance

Organize PyTorch training code with LightningModule and Trainer. Follow examples for validation, logging, model saving, and distributed training across multiple GPUs, with callbacks that add monitoring or stop training when progress stalls.

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• Converting an existing PyTorch training loop
• Adding validation, logs, and model checkpoints
• Configuring training across multiple GPUs

Connect cognee to additional databases and data sources through community packages. The skill covers installing and registering adapters, importing data through connectors, configuring monitoring, and developing extension packages with examples and tests.

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• Connect cognee to Qdrant or another database
• Import data from Slack or Gmail
• Configure monitoring with Keywords AI

Prepare a deployment checklist covering tests, approvals, database changes, release steps, and monitoring. Tailor it to the release and document rollback triggers, the conditions for reverting a change, before production deployment begins.

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• Prepare checks for an upcoming production release
• Review a release with database migrations
• Verify feature flags and CI status before deployment

Compare saved knowledge with current web sources using Perplexity. This skill produces a cited research page that separates new developments, supporting evidence, and contradictions, with suggested updates to stored information about people, companies, or deals.

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• Updating stored information about people and companies
• Monitoring changes in companies and active deals
• Preparing briefings focused on new developments

Supports preparation for EU medical device regulation reviews by organizing device classification, technical documentation, clinical evidence, and post-market monitoring. Includes a tool to identify documentation and compliance gaps and prioritize the next steps.

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• Documenting a device classification rationale under MDR
• Checking technical documentation before a review
• Planning clinical evaluation and identifying evidence gaps

Help quality leaders at medical technology companies prepare management reviews, define quality objectives, and track performance indicators. Organize audit findings, complaints, corrective actions, and regulatory status into reports and assigned improvement actions.

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• Preparing quality management reviews
• Defining measurable quality objectives and indicators
• Tracking audit findings and corrective actions

Create CI/CD configurations, which automate build, test, and deployment steps, alongside Terraform infrastructure templates and Kubernetes deployment files. Generate health-check and rollback instructions to review before applying changes to cloud infrastructure or application environments.

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• Setting up GitHub Actions or CircleCI pipelines
• Creating Terraform modules for AWS, GCP, or Azure
• Preparing blue/green or rolling deployments

Coordinate a game release from candidate preparation through testing, release approval, and deployment. Assign work to role-based agents and collect quality results, patch notes, launch messaging, and a post-release monitoring plan.

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• Prepare a release candidate and checklist
• Collect testing and release readiness results
• Decide whether a release should proceed

Build Python applications that collect live cryptocurrency prices, trades, and order books with Cryptofeed. Covers consistent data formats across exchanges, price comparisons, and writing incoming data to storage systems for monitoring and analysis.

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• Monitoring prices across cryptocurrency exchanges
• Analyzing order book depth
• Analyzing executed trade flows

Set up Google's agents-cli and find the right workflow for building AI agents. Connects requirements planning, project creation, implementation, evaluation, deployment, publishing, and monitoring through specialized skills for the Gemini Enterprise Agent Platform.

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• Set up agents-cli and its specialized skills
• Document agent requirements before writing code
• Find the workflow for creating and evaluating an ADK agent

Set up an organization-wide Google Cloud foundation with 17 baseline security policies, environment folders and projects, billing links, and centralized audit logging and monitoring. Check the resulting setup against a defined validation checklist.

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• Set up the initial foundation for a Google Cloud organization
• Organize production, non-production, and development resources
• Apply baseline security policies across an organization

Inspect Google Cloud Filestore usage and free space, then propose capacity increases or decreases based on configured thresholds and storage tier limits. Apply resizing after confirmation to address low space or reduce excess capacity.

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• Check free space across Filestore instances
• Increase capacity when storage is running low
• Reduce excess capacity on eligible storage tiers

Design Google Cloud systems where multiple AI agents process live audio, video, or text and respond in real time. Guides requirements discovery, architecture diagrams, infrastructure code, deployment instructions, and validation for technical guidance or safety monitoring workloads.

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• Design real-time technical guidance using live audio and video
• Plan hazard detection and visual inspection from video streams
• Create cloud architecture diagrams and deployment instructions

Review Google Cloud workloads using the Performance Optimization pillar of the Well-Architected Framework. Assess performance requirements and architecture, then get actionable recommendations for resource allocation, caching, independent components, automatic scaling, and ongoing monitoring.

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• Review resource capacity for expected peak demand
• Assess automatic scaling for traffic spikes
• Review component design for independent scaling

Investigate interruptions affecting GPU and TPU workloads on GKE using maintenance labels, monitoring queries, logs, and node scheduling restrictions. The skill helps determine whether host maintenance caused a disruption and recommends workload protection measures when maintenance is confirmed or expected.

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• Investigating unexpected GPU or TPU workload interruptions
• Checking upcoming host maintenance on GKE nodes
• Tracking maintenance interruptions with PromQL

Diagnose interrupted or repeatedly restarting AI training jobs managed by JobSet on GKE. Correlate restart metrics, node events, host health, and worker logs to distinguish Spot VM preemptions, infrastructure failures, and application crashes, then recommend responses.

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• Investigating repeated JobSet restarts
• Checking whether Spot VM preemptions interrupted training
• Tracing recurring failures to a physical host

Assess whether a GKE cluster or application is ready for production. Coordinates specialist skills to review scaling, security, reliability, monitoring, recovery, costs, and maintenance, then reports domain status, a weighted readiness score, and remediation priorities.

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• Review a GKE application before its production launch
• Assess production readiness across an entire cluster
• Prioritize fixes using domain status and a readiness score

Plan and operate Google Cloud Platform infrastructure using services such as Cloud Run and GKE, Google's managed Kubernetes service. Get guidance on deployment, access permissions, networking, monitoring, and cost controls for the system's requirements.

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• Planning an API with Cloud Run and Cloud SQL
• Reviewing GKE access permissions
• Designing event processing with Pub/Sub