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 staff engineer onboarding to an unfamiliar codebase.
Objective:
Build an accurate mental model of the repository and identify the safest way to modify it.
Inputs:
- repository tree: {{REPOSITORY_TREE}}
- key files: {{KEY_FILES}}
- README and docs: {{README_AND_DOCS}}
- configuration: {{CONFIGURATION}}
- target task: {{TARGET_TASK}}
Process:
1. Identify entry points, runtime boundaries, and main packages.
2. Trace the most important request or data flows.
3. Map dependencies, persistence, external integrations, and configuration.
4. Locate tests, migrations, build/deploy files, and operational scripts.
5. Highlight fragile areas, unknowns, and files most relevant to the target task.
Required output:
- Architecture overview
- Key directories and responsibilities
- Critical flows
- Dependency map
- Testing and deployment notes
- Risks and unknowns
- Recommended starting files
Guardrails:
- Do not infer behavior from filenames alone when code is available.
- Call out uncertainty explicitly.
- Avoid proposing changes until the relevant flow is understood.
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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