SkillsLib.ai

Docker Container Troubleshooting & Performance Optimization

Debug containers faster—diagnose failures and optimize performance in minutes

0.0(0 reviews)
100+ downloads
Updated Nov 2026

What You Can Do

Analyze Docker container logs instantly to identify root causes of failures, crashes, and resource bottlenecks. Get actionable recommendations for resource allocation, network configuration, and architectural improvements that eliminate production issues before they cascade.

Features

Log parsing and error diagnosis

Claude identifies stack traces, error patterns, and failure root causes from container logs

Resource optimization analysis

Analyzes CPU/memory/disk usage patterns to right-size container limits and requests

Health check design

Generate optimal health check configurations (startup, liveness, readiness probes)

Production hardening templates

Pre-built configurations for security, restart policies, and orchestration best practices

Performance profiling guidance

Identify bottlenecks in application and container-level metrics

Network troubleshooting

Diagnose port conflicts, DNS issues, overlay network problems, and connectivity failures

Image optimization strategies

Reduce image size, layer analysis, and multi-stage build recommendations

Example Output

Example 1: Container OOM Kill Analysis

Input: "My PostgreSQL container keeps getting OOMKilled. Here are the logs..."

Output:

  • Root cause: Process memory limit (256Mi) insufficient for buffer cache
  • Current usage: 312Mi (122% of limit)
  • Recommendation: Increase memory request to 512Mi, limit to 1Gi
  • Config snippet: resources: {requests: {memory: "512Mi"}, limits: {memory: "1Gi"}}
  • Timeline: Issue first appeared after 5.2GB dataset import

Example 2: Dockerfile Performance Optimization

Input: "My build takes 15 minutes. Here's my Dockerfile..."

Output:

  • Layer analysis: Base image (1.2GB) + dependencies (800MB) + app code (150MB)
  • Optimization: Reorder RUN commands, use BuildKit cache mount
  • Size reduction: 2.2GB → 380MB (82% reduction)
  • Build time: 15min → 2.3min (85% faster)
  • Refactored Dockerfile with inline comments and best practices

What's Included

  • SKILL.md file with diagnostic workflows and decision trees:
  • Container failure troubleshooting checklist (OOM, CrashLoop, ImagePullBackOff, Pending, etc.):
  • Resource optimization templates (requests/limits for common services: PostgreSQL, Redis, Node.js, Python, Go):
  • Dockerfile analysis and multi-stage build templates:
  • Health check configuration templates (Kubernetes probes + Docker HEALTHCHECK):
  • Network troubleshooting flowchart and common connectivity patterns:
  • Performance baseline metrics collection guide:
  • Production hardening security checklist:

Who It's For

  • DevOps engineers — Rapidly diagnose container failures in CI/CD pipelines and production
  • Platform engineers — Design resource policies and health checks for microservices
  • SREs — Troubleshoot multi-container production incidents and optimize infrastructure
  • Backend developers — Debug containerized applications locally and optimize Dockerfiles
  • Cloud architects — Implement container best practices and production hardening

Best For

  • Debugging container crashes and restarts (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending)
  • Right-sizing CPU/memory requests and limits for containerized workloads
  • Optimizing Docker image size and build performance
  • Designing health checks and startup probes for reliable container orchestration
  • Analyzing container logs for security, performance, and reliability issues

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