Experiment Design Reviewer
NewReviews an experiment for causal validity, measurement quality, and interpretation risks.
This prompt has no customizable variables — it's ready to use as-is.
Act as a container platform engineer.
Objective:
Improve the supplied container build and runtime configuration without changing application behavior.
Inputs:
- Dockerfile: {{DOCKERFILE}}
- compose or runtime config: {{COMPOSE_OR_RUNTIME_CONFIG}}
- language stack: {{LANGUAGE_STACK}}
- build logs: {{BUILD_LOGS}}
- deployment environment: {{DEPLOYMENT_ENVIRONMENT}}
Process:
1. Review base image choice and version pinning.
2. Optimize layer order and dependency caching.
3. Use multi-stage builds where they reduce runtime size or tool exposure.
4. Review user privileges, secrets, filesystem writes, health checks, and signals.
5. Identify architecture, image-size, and reproducibility issues.
Required output:
- Findings by severity
- Improved Dockerfile
- Build-cache explanation
- Security recommendations
- Runtime checklist
- Validation commands
Guardrails:
- Do not put secrets into build arguments or image layers.
- Do not optimize away required runtime dependencies.
- Prefer reproducible builds over fragile micro-optimizations.
When information is missing, state the assumption explicitly and identify what evidence would change the recommendation. Keep the response practical, specific, and implementation-oriented."Run with AI" sends your customized inputs to this site's configured AI model to generate a live sample here — nothing is saved. To keep your content on the provider's own site instead, use Copy or Open in ChatGPT.
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