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plan-data-engineer-review

Identify reliability gaps and production-readiness issues in Tower data apps.

19|Updated Apr 1, 2026
Search Tags:#devops#observability#review#data-engineering#production-readiness#data-app#tower
One-click install
npx skills add https://github.com/tower/agentic-data-engineering --skill plan-data-engineer-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: plan-data-engineer-review
Source: https://github.com/tower/agentic-data-engineering/tree/main/.claude/skills/plan-data-engineer-review
Command: npx skills add https://github.com/tower/agentic-data-engineering --skill plan-data-engineer-review

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Tower data apps often slip into production with subtle reliability gaps; this Skill provides structured reviews to identify and quantify those risks. The reviewer focuses on data pipelines, observability, and operational readiness, translating findings into actionable changes and artifacts.

Core Features & Use Cases

  • Structured, multi-mode assessment (DEV REVIEW, PROD READINESS, INCIDENT, OPTIMIZATION) for Tower data apps.
  • Generates scoring across gradient dimensions plus pass/fail checks, with concrete remediation guidance.
  • Produces review artifacts and recommendations to accelerate production hardening and post-incident learning.

Quick Start

Invoke the skill with your app name and mode to start a review.

Frequently Asked Questions about plan-data-engineer-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I check production-readiness for data pipelines before deployment?▼

Production-readiness reviews identify reliability gaps in data pipelines by scoring gradient dimensions and running pass/fail checks. They analyze logs, secrets management, and metadata to quantify operational risks and generate actionable remediation guidance.

What is a data engineering production-readiness review?▼

A production-readiness review is a structured assessment that identifies reliability gaps in data apps. It scores five gradient dimensions and six pass/fail checks across modes like DEV REVIEW and PROD READINESS to translate findings into actionable remediation artifacts.

How do I conduct an incident review for a data pipeline failure?▼

Conduct an incident review by invoking the INCIDENT assessment mode to analyze pipeline failures. This mode evaluates logs, secrets management status, and Tower metadata to produce post-incident learning artifacts and targeted optimization recommendations.

Does this production-readiness review require access to secrets management and logs?▼

Yes, production-readiness reviews require reading logs, secrets management status, and Tower metadata to generate accurate recommendations. Access to these operational data sources is essential for identifying reliability gaps and quantifying production risks.

What is the best way to optimize data app observability and reliability?▼

The best way to optimize observability is using the OPTIMIZATION assessment mode to review data apps. It scores gradient dimensions and translates findings into actionable changes and artifacts that accelerate production hardening and operational reliability improvements.

Can I use this review process for both development and production data apps?▼

Yes, the review process supports both development and production contexts through DEV REVIEW and PROD READINESS modes. These structured assessments identify reliability gaps and produce review artifacts applicable across the entire data app lifecycle.

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