One-click install
npx skills add https://github.com/TheoLee021/study-consultant --skill study-review
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: study-review
Source: https://github.com/TheoLee021/study-consultant/tree/main/.claude/skills/study-review
Command: npx skills add https://github.com/TheoLee021/study-consultant --skill study-review

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

주간 학습 기록을 분석하고 피드백을 생성하는 자동화된 리뷰 도구로, 학습 패턴과 진척을 직관적으로 파악하고 개선점을 제시합니다.

Core Features & Use Cases

  • 데이터 소스 기반 주간 분석: recent 7 days study-log, goals.md, mastery.md, spaced-queue.md, feedback의 과거 피드백 등을 종합해 주간 리포트를 만듭니다.
  • 패턴 감지 및 메타인지 피드백: 반복되는 학습 습관을 발견하고 메타인지 인사이트를 제공합니다.
  • 피드백 기록 및 비교: 이전 주 피드백과의 변화 추이를 저장하고 진척을 추적합니다.

Quick Start

요청 시 이번 주 학습 기록을 분석하고 주간 피드백을 자동으로 생성하라.

Frequently Asked Questions about study-review

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

FAQPage Schema
How do I generate a weekly learning review from study logs?

To generate a weekly learning review, the Skill consumes your recent 7-day study logs, goals.md, mastery.md, and spaced-queue.md files to produce data-driven feedback and store the insights as a weekly appendix in study-consultant/feedback/YYYY-MM.md.

What is metacognition feedback and how does it improve study patterns?

Metacognition feedback analyzes your study logs to detect repeated learning habits and patterns, providing data-driven insights that guide study improvement and help you track mastery progress across courses.

Can I track mastery progress and compare weekly feedback trends?

Yes, you can track mastery progress and compare trends by storing weekly feedback appendices; the Skill references prior feedback alongside mastery.md to measure learning changes and improvements over time.

Do I need to prepare specific markdown files for automated study analysis?

Yes, automated study analysis requires specific markdown files: study-log for recent 7 days, goals.md, mastery.md, and spaced-queue.md, which are consumed together to generate a structured weekly report.

How does spaced repetition queue data integrate into weekly study reports?

Spaced repetition queue data from spaced-queue.md is consumed alongside study logs and mastery tracking files to analyze weekly learning patterns, generating structured feedback that guides your ongoing study improvements.