des-learning-review

Summarize DES learning progress and identify weak concepts from learning artifacts.

2|Updated May 20, 2026
One-click install
npx skills add https://github.com/DKSang/DES-SKILL --skill des-learning-review
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: des-learning-review
Source: https://github.com/DKSang/DES-SKILL/tree/main/skills-learning/des-learning-review
Command: npx skills add https://github.com/DKSang/DES-SKILL --skill des-learning-review

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you understand where you are in the DES learning workflow by turning scattered learning evidence into a clear, evidence-based progress review with next steps.

Core Features & Use Cases

  • Evidence-based learning readiness: Summarizes overall progress, module progress, and phase readiness without claiming mastery when evidence is missing.
  • Gap and blocker identification: Highlights strengths, weak concepts, unresolved gaps, learning blockers, and specifically calls out open High/Blocking gaps.
  • Actionable next learning plan: Produces a review plan plus a recommended next learning skill and (when safe) the next DES lifecycle phase.

Quick Start

Ask an AI agent to run des-learning-review to summarize your current DES learning progress, identify your weakest concepts, and recommend what to study next.

Frequently Asked Questions about des-learning-review

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

FAQPage Schema
How do I assess my data engineering learning progress and identify weak concepts?

A learning readiness assessment evaluates your accumulated study evidence to produce a progress summary, calling out open blocking gaps and generating an actionable next study plan without falsely claiming mastery.

What is the best way to perform a gap analysis on my study plan before moving to the next phase?

Performing a gap analysis on a study plan involves reviewing evidence from completed modules to identify unresolved learning blockers, ensuring you only advance to the next workflow phase when readiness criteria are safely met.

Can I get a readiness check for my data engineering modules without claiming false mastery?

Yes, a readiness check summarizes your module progress based strictly on available artifacts, explicitly flagging missing evidence and blocking gaps rather than assuming you have mastered the concepts.

Do I need prior learning artifacts to use an agent tutoring review for data engineering?

Yes, you need prior learning artifacts and status evidence from your DES phases, because the review mechanism analyzes these existing materials to produce an evidence-based output file with next study actions.

How do I generate an actionable study plan after completing data engineering workflow phases?

To generate an actionable study plan after workflow phases, run a learning review that maps your current evidence to recommended next learning skills and outputs specific study actions for continuing the lifecycle.