study-notes

Generates structured Korean law study notes from transcripts, textbooks, and past exam questions.

Updated Jun 15, 2026
One-click install
npx skills add https://github.com/kmjy98-sketch/khulaw --skill study-notes-kmjy98-sketch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: study-notes
Source: https://github.com/kmjy98-sketch/khulaw/tree/main/.agent/skills/study-notes
Command: npx skills add https://github.com/kmjy98-sketch/khulaw --skill study-notes-kmjy98-sketch

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve? Manually organizing lecture transcripts, textbooks, and past exam questions into coherent study notes is time-consuming for Korean law students. This Skill automates the creation of structured markdown notes (concept summaries, case analyses, exam-point reviews) by collecting source context and applying consistent templates. ## Core Features & Use Cases - Three Note Modes: Generate concept notes (개념정리), case analysis notes (사례정리), or exam-point notes (기출핵심) using dedicated markdown templates. - Context Collection Pipeline: The collect.py script reads a target source file, detects the subject (민법/형법/헌법), and loads issue-frequency rankings to prioritize high-frequency exam topics. - Legal Source Enrichment: Automatically calls korean-law-mcp tools to insert statute text and precedent holdings when referenced but missing from sources. - Use Case: A law student finishes a civil law lecture transcript and asks for a 핵심정리; the Skill collects the transcript, cross-references related textbook content, and produces a structured markdown note with page markers, footnotes, and backlinks. ## Quick Start Ask the agent to create a concept summary note from a specific lecture transcript file, for example: "이 전사문 파일로 민법 개념정리 노트 만들어줘."

Frequently Asked Questions about study-notes

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

FAQPage Schema
How do I generate study notes from a lecture transcript?▼

Run collect.py with the target transcript path and a mode (concept, case, or exam), then run generate.py with the same mode and an output path. The agent fills the template using the collected context stored in study-notes-context.json.

What note formats does this study notes workflow support?▼

Three markdown templates are provided: concept.md for concept and lecture summaries, case.md for case-analysis and model-answer organization, and exam.md for past-exam key points ranked by issue frequency.

Does the note generator insert statute text and precedents automatically?▼

Yes, when a statute number or precedent name appears without its text, the workflow calls korean-law-mcp tools such as get_law_detail and search_precedent_tool to retrieve and insert the original text. It skips lookup if the content already exists in search results.

How are notes prioritized by exam frequency?▼

The collect.py script loads issue_frequency.json and extracts the top issues for the detected subject. Templates then order sections so high-frequency exam topics appear first in the generated note.

What are the limitations of this note generation approach?▼

The scripts only collect context and copy templates; the actual note content must be written by the LLM agent. It also depends on local Drive paths and state files, so it will not work without that environment configured.