
Docker Production Readiness Auditor
Audits Dockerfiles and Compose setups for production readiness, security risks, image size, build speed, health checks, secrets handling, and deployment compatibility.
- Convert single-stage Dockerfiles into optimized multi-stage production builds.
- Identify and clean up high-risk patterns like running as root or secret leakage.
- Review Docker Compose files for production-breaking local-only patterns.
Secure checkout via Stripe
Included in download
- Convert single-stage Dockerfiles into optimized multi-stage production builds.
- Identify and clean up high-risk patterns like running as root or secret leakage.
- file_write, file_read automation included
- Ready for Compatible with ChatGPT Custom GPTs
See it in action
A real example of what this skill takes in and produces.
Sample input
Audit this Node.js Dockerfile for production readiness. Check image size, multi-stage build opportunities, dependency layers, .dockerignore, non-root user, exposed ports, secrets handling, health checks, startup command, and deployment compatibility. Do not expose secrets or provide destructive operations.
Sample output
=== DOCKER PRODUCTION READINESS AUDIT === Context: Node.js web API Dockerfile review. Files reviewed: Dockerfile only. No Compose file or deployment config provided. Application type: Long-running API service. Deployment target: Unknown. Overall readiness level: Level 2 — Deployable but fragile. Executive summary: The Dockerfile may build and run locally, but it is not production-ready. The main risks are use of a floating base tag, running as root, copying the full repository before dependency installation, likely missing .dockerignore, possible dev dependency inclusion, no health check, and unclear production command. Critical issues: None confirmed from the provided Dockerfile, assuming no secrets are copied. Secret handling still needs verification. High-risk issues: 1. Runtime likely runs as root. 2. Floating or overly broad base tag may create non-deterministic builds. 3. No health check is defined. 4. Dependency install may include dev dependencies. 5. Build context may include unnecessary or sensitive files if .dockerignore is missing. Medium issues: 1. Build cache likely inefficient if full source is copied before dependency installation. 2. Image may be larger than necessary. 3. Port and runtime command need confirmation. Base image audit: Use an explicit runtime version appropriate for the application. Avoid production reliance on floating tags. Build structure audit: Copy package manifest and lockfile before application source to improve cache reuse. Dependency layer audit: Use deterministic dependency installation based on the lockfile. Ensure production image excludes dev dependencies where appropriate. .dockerignore audit: Recommended exclusions: - local dependencies - Git history - local environment files - logs - coverage - build outputs regenerated during image build - editor files - private keys or certificates Secrets/config audit: Do not copy .env or credentials into the image. Runtime configuration should come from the deployment platform or secret manager. Runtime user and permissions audit: Add a non-root runtime user and ensure file ownership supports the application. Ports/networking audit: Confirm the app listens on the same port documented by EXPOSE and expected by the deployment platform. Health check audit: Add or document a lightweight health endpoint if supported by the application. Entrypoint/CMD audit: Confirm the command starts the production server, not a development server. Safe fix plan: 1. Add .dockerignore. 2. Pin base image version. 3. Reorder dependency install for cache. 4. Use production dependencies in runtime. 5. Add non-root user. 6. Add or document health check. 7. Validate port and production command. 8. Test in staging. Improved Dockerfile draft: The skill returns a reviewable multi-stage Dockerfile draft with no secrets and no destructive operations. Validation checklist: - build succeeds in CI - image starts locally or in staging - app responds on the expected port - health endpoint returns success - container runs as non-root user where possible - no .env or private files are in the image - dependency install is deterministic - production command starts the correct artifact - logs are visible on stdout/stderr Open questions: 1. What framework is this app using? 2. What is the real build output path? 3. Which deployment platform will run this image? 4. Does the app expose a health endpoint?

Docker Production Readiness Auditor
Audits Dockerfiles and Compose setups for production readiness, security risks, image size, build speed, health checks, secrets handling, and deployment compatibility.
Secure checkout via Stripe
Included in download
- Convert single-stage Dockerfiles into optimized multi-stage production builds.
- Identify and clean up high-risk patterns like running as root or secret leakage.
- file_write, file_read automation included
- Ready for Compatible with ChatGPT Custom GPTs
- Instant install
See it in action
A real example of what this skill takes in and produces.
Sample input
Audit this Node.js Dockerfile for production readiness. Check image size, multi-stage build opportunities, dependency layers, .dockerignore, non-root user, exposed ports, secrets handling, health checks, startup command, and deployment compatibility. Do not expose secrets or provide destructive operations.
Sample output
=== DOCKER PRODUCTION READINESS AUDIT === Context: Node.js web API Dockerfile review. Files reviewed: Dockerfile only. No Compose file or deployment config provided. Application type: Long-running API service. Deployment target: Unknown. Overall readiness level: Level 2 — Deployable but fragile. Executive summary: The Dockerfile may build and run locally, but it is not production-ready. The main risks are use of a floating base tag, running as root, copying the full repository before dependency installation, likely missing .dockerignore, possible dev dependency inclusion, no health check, and unclear production command. Critical issues: None confirmed from the provided Dockerfile, assuming no secrets are copied. Secret handling still needs verification. High-risk issues: 1. Runtime likely runs as root. 2. Floating or overly broad base tag may create non-deterministic builds. 3. No health check is defined. 4. Dependency install may include dev dependencies. 5. Build context may include unnecessary or sensitive files if .dockerignore is missing. Medium issues: 1. Build cache likely inefficient if full source is copied before dependency installation. 2. Image may be larger than necessary. 3. Port and runtime command need confirmation. Base image audit: Use an explicit runtime version appropriate for the application. Avoid production reliance on floating tags. Build structure audit: Copy package manifest and lockfile before application source to improve cache reuse. Dependency layer audit: Use deterministic dependency installation based on the lockfile. Ensure production image excludes dev dependencies where appropriate. .dockerignore audit: Recommended exclusions: - local dependencies - Git history - local environment files - logs - coverage - build outputs regenerated during image build - editor files - private keys or certificates Secrets/config audit: Do not copy .env or credentials into the image. Runtime configuration should come from the deployment platform or secret manager. Runtime user and permissions audit: Add a non-root runtime user and ensure file ownership supports the application. Ports/networking audit: Confirm the app listens on the same port documented by EXPOSE and expected by the deployment platform. Health check audit: Add or document a lightweight health endpoint if supported by the application. Entrypoint/CMD audit: Confirm the command starts the production server, not a development server. Safe fix plan: 1. Add .dockerignore. 2. Pin base image version. 3. Reorder dependency install for cache. 4. Use production dependencies in runtime. 5. Add non-root user. 6. Add or document health check. 7. Validate port and production command. 8. Test in staging. Improved Dockerfile draft: The skill returns a reviewable multi-stage Dockerfile draft with no secrets and no destructive operations. Validation checklist: - build succeeds in CI - image starts locally or in staging - app responds on the expected port - health endpoint returns success - container runs as non-root user where possible - no .env or private files are in the image - dependency install is deterministic - production command starts the correct artifact - logs are visible on stdout/stderr Open questions: 1. What framework is this app using? 2. What is the real build output path? 3. Which deployment platform will run this image? 4. Does the app expose a health endpoint?
About This Skill
Docker Production Readiness Auditor helps developers, SaaS builders, agencies, startups, DevOps teams, and AI coding agents review Dockerfiles and Docker Compose setups before production deployment. It audits base images, dependency layers, multi-stage build opportunities, build cache structure, .dockerignore files, image size, non-root runtime users, secrets handling, exposed ports, health checks, entrypoints, production commands, Compose services, volumes, networks, resource assumptions, logging, graceful shutdown, and deployment-platform compatibility. The skill produces risk-ranked audits, safe fix plans, improved Dockerfile drafts, Compose improvement notes, validation checklists, PR review comments, and production-readiness recommendations without executing container commands or exposing secrets.
Use Cases
- Convert single-stage Dockerfiles into optimized multi-stage production builds.
- Identify and clean up high-risk patterns like running as root or secret leakage.
- Review Docker Compose files for production-breaking local-only patterns.
- Generate comprehensive production-readiness checklists for DevOps handoffs.
Known Limitations
This skill provides Docker production-readiness analysis and safe improvement recommendations, but it does not execute builds, inspect live containers, access private registries, scan images directly, apply infrastructure changes, approve production deployments, or replace DevOps/security review. Actual production readiness depends on the application stack, deployment platform, CI/CD process, runtime environment, vulnerability scan results, performance testing, secrets management, and organizational security requirements.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/docker-production-readiness-auditor | tar xz -C ~/.claude/skills/Free skills install directly. Paid skills require purchase - use the download button above after buying.
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Security Scanned
Passed automated security review
Permissions
File Scopes
This skill uses file access to read user-provided Dockerfiles, Docker Compose files, .dockerignore files, build logs, package manifests, lockfiles, CI/CD configuration, deployment notes, registry scan summaries, and documentation. It uses write access to create structured Markdown/text outputs such as Docker production-readiness audits, security reviews, safe fix plans, improved Dockerfile drafts, Compose improvement notes, validation checklists, PR review comments, incident reviews, runbooks, and SKILL.md files. Browser access is optional and should only be used for public documentation research when explicitly requested. The default safe setup does not require network access, shell access, environment variable access, registry write access, production host access, or secret-value access.
Tags
Compatible with ChatGPT Custom GPTs, ChatGPT Agents, Claude-style workflows, Cursor, Claude Code, Codex CLI, OpenCode, Replit, DevOps review workflows, CI/CD planning, container security audits, agency deployment handoffs, and other AI agent systems that support structured Markdown instruction files such as SKILL.md. It can also be used manually in any AI chat by pasting the instructions. For real deployments, validate all changes in CI and staging before production.