1417 个 ・ 集成工具: Kubernetes · 按人气排序
Set up and manage a local Kubernetes development environment for OpenShell using k3d, Skaffold, and Helm. Build and deploy changes, inspect traces, configure optional authentication components, and test behavior across multiple gateway replicas.
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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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Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.
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Define reliability targets for production services and calculate error budgets, the allowed amount of failure. Create monitoring rules, incident response runbooks, capacity plans, and automation scripts, with failure experiments to check recovery behavior.
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Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.
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Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.
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Create Kubernetes deployment files and Helm charts, reusable application packages. Configure permissions, network isolation, and persistent storage, then investigate pod failures and resource usage to adjust workloads and verify deployment health.
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Design systems made of independently deployable services, or plan how to split an existing application. The skill produces service boundary diagrams and recommendations for communication, data ownership, failure handling, monitoring, and deployment.
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Design controlled failure experiments for distributed systems to examine how services recover. Produce failure injection scripts, monitoring plans, rollback procedures, and exercise guides, then turn findings into documented improvements.
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Investigate production incidents and bugs using logs, metrics, and code evidence. The skill guides hypothesis testing, root cause analysis, fix validation, and reusable investigation notes, with access to configured observability tools such as Axiom.
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Analyze Axiom data ingestion and query usage to identify unused columns and field values. Build cost dashboards and spike monitors, then summarize recommendations for dropping or sampling data with estimated savings.
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Design and build Axiom dashboards from logs or metrics, the numerical measurements of system behavior. Create charts, filters, and layouts for monitoring and reporting, or migrate existing Splunk and Grafana panels while validating queries against available data.
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Understand how OpenRig organizes a team of coding agents. This skill explains the main terms, shows how to identify an agent’s role and peers, and clarifies where skills and working context come from.
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Look up exact syntax, options, JSON output, defaults, and error meanings for a known OpenRig command. Use the reference to check command behavior and diagnose runtime differences against the installed help and running daemon.
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Assess web applications and APIs for SQL injection, where input changes database queries. The skill covers finding vulnerable inputs, testing different database systems, automating checks, and documenting findings with remediation suggestions.
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Investigate insecure deserialization, where restoring objects from stored or transmitted data can execute unwanted code. Covers multiple languages, input format recognition, vulnerable processing points, verification methods, and documentation of affected libraries and entry points.
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Assess server-side template injection, where user input is interpreted as template code. Identify template engines, evaluate information exposure and code execution risks, and organize evidence alongside detection and prevention measures.
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Assess Kubernetes security from an existing pod or API access point. Covers permission checks, exposed node services, secrets, host access, and cloud metadata to explain how configuration weaknesses can expand access across a cluster.
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Investigate TOCTOU flaws: race conditions where a checked property changes before an action uses it. The skill covers filesystem, kernel, web, and container testing, helping identify vulnerable sequences, reproduce timing issues, and document observed impact.
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Assess AWS, Azure, and GCP environments by identifying identities, permissions, and paths to broader access or data exposure. Covers cloud storage, secrets, cross-account trust, and CI/CD, the automated systems used to build and deploy software.
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Configure how Dynamo routes requests to workers, then check model discovery and a chat response. Supports local and Kubernetes setups, including routing based on cached model data, with clear commands, results, and limitations.
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Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.
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Check whether a Dynamo deployment is ready for disaggregated serving, where input processing and token generation run separately. Read-only probes inspect transport settings, GPU networking capabilities, and NIXL transfer-test readiness, then report gaps and next steps.
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Set up and operate NVIDIA AI-Q Blueprint locally or on your own infrastructure. Choose a deployment mode, configure credentials, check service health, troubleshoot failures, and provide a verified server address for aiq-research.
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