cross-sectional-consistency

---

name: cross-sectional-consistency description: Calculate cross-sectional consistency for categorical string variables in a QUIQ-format table using Claude CLI to group semantically equivalent values. No API key required. Use for data quality assessment when the same real-world concept may be expressed in multiple ways (e.g., 'F', 'Female', '여자'). tier: community category: lydus parameters: quiq_path: description: Path to QUIQ-format CSV file (output of quiq skill). type: string via_path: description: Path to Variable Information Annotation CSV (columns: Original_table_name, Variable_name, Description). type: string save_path: description: Directory path to save output files. type: string

Cross-Sectional Consistency

Calculates cross-sectional consistency for categorical string variables in a QUIQ-format table. Uses Claude CLI to group unique values by semantic equivalence, then measures how consistently a single canonical form is used within each semantic group.

When to Use This Skill

• The same concept is expressed in multiple forms (e.g., 'F', 'Female', '여자')
• To detect cross-institutional or cross-user naming inconsistencies
• As part of LYDUS quality management assessment

Input Requirements

QUIQ CSV — standard QUIQ-format output. Variables analyzed must meet all of:

1. Variable_type contains string or str
2. Mapping_info_1 does NOT contain note, code, or date
3. Is_categorical == 1
4. Value is not null
5. At least 2 unique values (variables with only 1 unique value are skipped)

VIA CSV (Variable Information Annotation) — provides natural-language descriptions per variable to help the LLM make accurate groupings.

Column	Description
Original_table_name	Table name (matches QUIQ)
Variable_name	Variable name (matches QUIQ)
Description	Natural language description of the variable

How It Works

For each (Original_table_name, Variable_name) group:

1. Unique values are sent to the LLM with a system prompt and variable description
2. LLM groups semantically equivalent values (e.g., ['F', 'Female', '여'] → one group)
3. Within each group, inner consistency is calculated:
   • For each semantic group, measure how dominant the most frequent form is
   • Score = Σ (count²/total) / total across all groups → closer to 1.0 = one canonical form dominates
4. LLM is retried up to 5 times if no values match

Average Cross-Sectional Consistency = unweighted mean across all valid variables.

Output

File	Description
cross_sectional_consistency_total.txt	Average Cross-Sectional Consistency (%)
cross_sectional_consistency_summary.csv	Per-variable consistency scores
cross_sectional_consistency_detail.txt	LLM-assigned semantic groups per variable

How to Run

import pandas as pd
from scripts.cross_sectional_consistency import get_cross_sectional_consistency

quiq = pd.read_csv("/path/to/quiq.csv")
via  = pd.read_csv("/path/to/via.csv")

average_consistency, results_df, detail = get_cross_sectional_consistency(
    quiq=quiq,
    via=via
)

print(f"Average Cross-Sectional Consistency (%) = {round(average_consistency * 100, 2)}")

As a script with config

# config.yaml
quiq_path: /path/to/quiq.csv
via_path:  /path/to/via.csv
save_path: /path/to/output

python scripts/cross_sectional_consistency.py --config config.yaml

Critical Notes

1. SQL 변환 불가 — Claude CLI 호출 및 응답 파싱이 핵심 로직이므로 DuckDB/BigQuery SQL로 표현할 수 없음.

2. VIA 파일 필수 — via_path가 없으면 모든 변수에 "No description available"이 전달되어 LLM 정확도가 낮아짐.

3. LLM 재시도 로직 — 응답이 실제 값과 매칭되지 않으면 최대 5회 재시도. 여전히 실패하면 해당 변수는 결과에서 제외됨.

4. API 키 불필요 — LLM 호출은 현재 Claude Code 세션의 Claude CLI(claude -p)를 통해 처리되므로 별도 API 키가 필요 없음.

5. Is_categorical 타입 — 원본 코드에서 Is_categorical == 1 비교 전 pd.to_numeric() 변환을 누락한 버그가 있었음. 스킬 버전에서 수정됨.

References

• LYDUS 품질관리 프로그램 활용 가이드라인 (비공개 내부 문서)
• Original Python implementation: LYDUS_Cross_Sectional_Consistency.py (이성민 작성)