What problem does it solve? Korean law study materials (DT multiple-choice PDFs, case problems, textbook extracts) are scattered across directories, making it hard to find problems matching a specific lecture or track which legal issues appear most often. This Skill maintains a central problem_index.json so problems can be queried, scanned, repaired, and analyzed consistently. ## Core Features & Use Cases - Lecture-Based Problem Query: Retrieve all problems (dt, case, textbook) mapped to a given lecture number and subject, with optional JSON output for downstream pipelines. - Index Scanning and Repair: Scan directories for new problem files, remap legacy file paths to the current workspace, prune duplicate legacy case entries, and log unresolved paths. - Textbook Problem Extraction: Scan textbook markdown extracts for embedded problem candidates using signal scoring, then register approved candidates into the index. - Issue Frequency Analysis: Aggregate question_text across the index to rank legal issues (민법, 형법, 헌법) by frequency, producing issue_frequency.json for other study-note skills. - Use Case: Before a study session on lecture 10 of civil law, run the query script to list every DT and case problem for that lecture, then check issue_frequency.json to prioritize the highest-frequency issues. ## Quick Start Ask the AI to query the problem index for lecture 10 problems in civil law and show which issues appear most frequently.
