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    Docker Ops Playbook Pro

    2

    A cautious Docker operations assistant for container diagnosis, lifecycle management, and safe environment cleanup.

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    Docker Ops Playbook Pro

    Docker Ops Playbook Pro

    Example session with this skill installed

    My 'api-gateway' container is showing 'unhealthy' in docker ps, but the logs don't show an obvious crash. Here is the output of docker inspect for that container.

    • Read your context and instructions
    • Compiled the docker ops playbook

    I have analyzed the docker inspect output for the api-gateway container. The container is running, but the health check is failing repeatedly.

    Diagnosis: Health Check Timeout

    The inspection reveals that the Healthcheck configuration has a Timeout of 2s and an Interval of 30s. The LastOutput from the health check shows a 143 exit code, indicating the curl command is being killed before it completes. This suggests the application is responding slower than the 2-second threshold, or the network route to the health endpoint is congested.

    Status:

    • Observed Evidence: State.Health.Status = "unhealthy", ExitCode = 143.
    • Likely Cause: Overly aggressive health check timeout for the current application latency.
    • Confidence: Likely.

    Recommended Action

    Increase the health check timeout to allow the application more time to respond during peak load.

    Commands

    Modify your docker-compose.yml or container start command:

    services:
  api-gateway:
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
      interval: 30s
      timeout: 10s
      retries: 3

    Verification

    After applying the change and recreating the container, monitor the health transitions:

    docker inspect --format '{{json .State.Health.Log}}' api-gateway

    Next steps

    1. Update the timeout value in your Compose file to 10s.
    2. Run docker compose up -d api-gateway to recreate the container with the new config.
    3. Check docker stats api-gateway to ensure CPU throttling isn't causing the latency.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Managing containerized environments often leads to "command-line anxiety" where one wrong prune or rm wipes out production volumes or disrupts critical services. Developers struggle to diagnose unhealthy containers or network isolation issues without clear, step-by-step visibility.

    What it does

    • Performs deep inspection of containers, images, volumes, and networks to identify dependencies before suggesting changes.
    • Diagnoses unhealthy services by analyzing health-check commands, exit codes, and resource pressure logs.
    • Generates targeted Docker CLI and Docker Compose commands for lifecycle management and troubleshooting.
    • Drafts graduated cleanup plans that distinguish between disposable cache and critical persistent data.
    • Audits Docker Compose files for configuration errors, port binding conflicts, and resource limit gaps.

    Frameworks & tools

    Docker Engine, Docker Compose, Docker CLI, and container runtime environments.

    Why this beats prompting it yourself

    Generic LLMs often suggest destructive system prune commands without checking for attached volumes or build-cache requirements. This skill enforces an Inspect-Plan-Verify loop that treats persistent data as high-value and prioritizes minimal-risk operational steps over broad resets.

    Use cases

    • Recovering from 'unhealthy' container states by isolating root causes in logs or health-check configs.
    • Safely reclaiming disk space by identifying truly orphaned resources vs. inactive rollback images.
    • Troubleshooting complex Docker Compose networking or DNS resolution between microservices.
    • Auditing resource limits (CPU/Memory) to prevent OOM kills in shared environments.

    Known limitations

    Cannot execute commands directly without an execution environment. Does not replace managed orchestration tools like Kubernetes for cluster-wide scheduling.

    Requires user-provided CLI output for diagnosis. Cannot directly execute commands. Scope limited to Docker Engine and Compose, not Kubernetes or cloud-specific container services.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Permissions

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    Listed1 month ago

    What's inside

    Frequently Asked Questions

    The skill enforces an 'Inspect-Plan-Verify' loop, meaning it checks for dependencies and volume attachments before suggesting cleanup commands, preventing the accidental data loss common with generic LLM 'prune' suggestions.

    It is compatible with any AI agent or platform that supports system prompts and tool-calling for DevOps tasks, specifically those interacting with Docker Engine and Docker Compose environments.

    The skill includes a specialized configuration file and instructional set designed to guide your AI agent through complex container diagnostics, resource auditing, and lifecycle management workflows.

    No, the skill generates safe, targeted CLI commands and plans for you to review; it does not have the authority to execute commands directly on your host machine or server.

    It excels at identifying resource pressure like OOM (Out of Memory) kills, diagnosing health-check failures, and resolving networking conflicts between services in complex Docker Compose setups.

    While it handles local and remote Docker Engine and Compose setups, it is not intended for managing cluster-wide orchestration platforms like Kubernetes or OpenShift.

    New in DevOps & Cloud