review-project

Generate a CRISP-DM deployment retrospective markdown document at docs/crisp-dm/6-deployment/6.4-review-project.md.

Updated Mar 20, 2026

Search Tags:#documentation #deployment #retrospective #review #project-management #lessons-learned #crisp-dm

Ready to run

npx skills add https://github.com/thbraet/claude-template --skill review-project-thbraet

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: review-project
Source: https://github.com/thbraet/claude-template/tree/main/skills/review-project
Command: npx skills add https://github.com/thbraet/claude-template --skill review-project-thbraet

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Structured project retrospectives for CRISP-DM deployment to preserve knowledge, align stakeholders, and drive continuous improvements for future data science initiatives.

Core Features & Use Cases

Stepwise review framework that surfaces what went well, what didn't, and suggested improvements across all CRISP-DM deployment activities.
Generates a shareable project review document that captures objectives, timelines, risks, outcomes, and lessons learned for knowledge transfer.
Integrates with source documents (final report, project plan, objectives) to contextualize findings.

Quick Start

Run the review workflow to generate the deployment-phase project review document at docs/crisp-dm/6-deployment/6.4-review-project.md.

Frequently Asked Questions about review-project

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

FAQPage Schema

How do I run a CRISP-DM deployment retrospective for a data science project?▼

Run a structured CRISP-DM deployment retrospective by reviewing project objectives, data preparation, modeling, and evaluation outcomes. This process surfaces lessons learned and generates a comprehensive project review document for knowledge transfer.

What is a project review document in CRISP-DM and when do I need it?▼

A CRISP-DM project review document captures what went well, what didn't, and suggested improvements across deployment activities. You need it during the deployment phase to preserve knowledge, align stakeholders, and drive continuous improvements for future data science initiatives.

Can I capture lessons learned from data understanding and modeling phases in a single review?▼

Yes, the stepwise review framework guides structured assessments across all CRISP-DM phases, including data understanding, data preparation, modeling, and evaluation. It consolidates these findings into a single shareable project review document.

What source documents do I need to contextualize a deployment project review?▼

To contextualize a deployment project review, you need key source documents such as the final report, project plan, and objectives. The review framework integrates these documents to validate findings and align stakeholder perspectives.

What is the best way to document deployment phase risks and outcomes for future data science projects?▼

The best way to document deployment risks and outcomes is using a structured retrospective framework that generates a standardized markdown file. This captures timelines, risks, outcomes, and lessons learned to drive continuous improvements for future data science initiatives.