log analyzer kubernetes

    1

    Diagnostic agent for Kubernetes and OpenShift logs, identifying root causes for pod failures and security leaks.

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    log analyzer kubernetes

    Example session with this skill installed

    Analyze this: Pod my-app-5f7d is in CrashLoopBackOff. Exit code 137. kubectl describe shows: Limits: cpu: 500m, memory: 256Mi. Events: FailedScheduling: 0/3 nodes are available: 3 Insufficient memory.

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    • Compiled the log analyzer kubernetes

    I have analyzed the pod status and event logs for the my-app-5f7d resource. The primary failure is a resource exhaustion issue at the node level combined with container-level memory limits.

    Analysis Report: Kubernetes Resource Failure

    AttributeDetails
    TechnologyKubernetes (Generic/Unspecified)
    StatusCrashLoopBackOff / FailedScheduling
    Exit Code137 (OOMKilled)
    ConfidencialidadMEDIO (Namespaces/Pod names visible)
    DiagnosisThe pod is being terminated by the OOM Killer (Exit Code 137) because it exceeded its 256Mi memory limit. Simultaneously, new replicas cannot start because the cluster nodes have insufficient memory to satisfy the requests.

    Critical Findings

    • OOMKilled (137): The application is attempting to allocate more than 256Mi of RAM.

    Resource Starvation: The FailedScheduling event confirms no nodes have enough unallocated memory to host the pod.

    Limits vs Requests: The current limits are too low for the application's runtime requirements, but the infrastructure is also at capacity.

    Open Questions

    • Does the pod have resources.requests defined, or only limits?
    • Can you provide the log from the previous instance using kubectl logs my-app-5f7d --previous?
    • Are there ResourceQuota objects active in this namespace?

    Next steps

    1. Review application memory usage and increase resources.limits.memory to at least 512Mi if the node capacity allows.
    2. Check node pressure using kubectl top nodes to verify if scaling the cluster is necessary.
    3. Verify if a memory leak was introduced in the latest deployment.

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    About this skill

    Cloud-native applications often fail with cryptic error codes or silent crashes that leave developers digging through thousands of lines of logs. This skill acts as a Level 2/3 platform support agent to parse logs and resource descriptions, pinpointing the root cause of failures across Kubernetes and OpenShift environments.

    What it does

    • Root cause identification parses exit codes like 137 (OOMKilled) or 127 (command not found) to distinguish between application, platform, and configuration errors.
    • Security auditing identifies and flags sensitive data such as base64 secrets, ServiceAccount tokens, and cloud-provider ARNs that should not be in logs.
    • Resource analysis evaluates describe pod outputs to catch FailedMount, ResourceQuota violations, and taints/tolerations issues.
    • OpenShift specialization handles OCP-specific resources like Routes, DeploymentConfigs, and Security Context Constraints (SCC) validation errors.
    • Cloud-provider diagnostics troubleshoots provider-specific auth issues including EKS IRSA, GKE KSA-GSA bindings, and AKS Workload Identity.

    How it works

    1. Log ingestion where the user provides logs or kubectl/oc command outputs.
    2. Context gathering where the agent asks clarifying questions about the distribution (EKS, GKE, AKS, OCP), resource limits, or CNI plugins.
    3. Structured diagnosis which generates a standardized executive report identifying criticality, findings, and sensitive data risks.

    Frameworks & tools

    Kubernetes (vanilla, EKS, GKE, AKS) and OpenShift (OCP). Supports diagnostic data from kubectl and oc CLI tools, focusing on Pods, Deployments, Ingress, Routes, and SCCs.

    Why this beats prompting it yourself

    Generic prompts often miss the nuances of Kubernetes exit codes or the hierarchical nature of OpenShift SCCs. This skill enforces a structured diagnostic methodology that proactively checks for missing context like --previous logs and identifies sensitive data leaks that standard LLM prompts ignore.

    Use cases

    • Debugging CrashLoopBackOff cycles by analyzing previous container logs and dependencies.
    • Resolving Pending pods by checking ResourceQuota and scheduler constraints.
    • Fixing OpenShift permission issues related to Security Context Constraints.
    • Auditing logs for exposed credentials, IPs, and internal cluster tokens.

    Known limitations

    This skill does not execute commands or modify live resources. It requires users to manually provide log and description outputs for analysis.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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