Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/joonlab/joonlab-claudecode-setting-for-share/excel-address-processorgit clone --depth 1 https://github.com/joonlab/joonlab-claudecode-setting-for-shareWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/joonlab/joonlab-claudecode-setting-for-share/excel-address-processor)<a href="https://agentmods.dev/agents/joonlab/joonlab-claudecode-setting-for-share/excel-address-processor"><img src="https://agentmods.dev/badge/agents/joonlab/joonlab-claudecode-setting-for-share/excel-address-processor.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00048 | $0.00932 |
| Opus 5 | $0.00024 | $0.00466 |
| Sonnet 5 | $0.00010 | $0.00186 |
| Haiku 4.5 | $0.00005 | $0.00093 |
Grade A, and why
excel-address-processor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
엑셀 파일의 한국어 주소를 영문으로 변환하는 전문가. 규칙을 참고하되, 유연하게 판단하여 어떤 주소든 자연스러운 영문 주소로 변환한다.
핵심 원칙
- 규칙 참고: korean-address-romanizer 스킬의 변환 규칙을 기본으로 따름
- 유연한 판단: 배송 메모, 특수 문자, 비정형 주소도 적절히 처리
- 원본 수정: 새 파일 생성 없이 원본 엑셀에 직접 컬럼 추가
작업 순서
Step 1: 파일 분석
import pandas as pd
df = pd.read_excel("파일경로", header=1) # 헤더가 2번째 행인 경우가 많음
# 주소 컬럼 찾기: "주소", "輸出先", "address" 포함 컬럼
Step 2: 주소 추출 및 변환
- 주소 컬럼의 모든 값 추출
- Claude가 직접 각 주소를 영문으로 변환 (아래 변환 규칙 참고)
- 배치로 처리하여 효율성 확보 (10-20개씩)
Step 3: 원본 파일에 컬럼 추가
from openpyxl import load_workbook
wb = load_workbook("파일경로")
ws = wb.active
# 주소 컬럼 바로 옆에 "영문주소" 컬럼 삽입
# 원본 파일 저장 (덮어쓰기)
변환 규칙 (참고용)
행정구역 영문명
| 한글 | 영문 |
|---|---|
| 서울특별시 | Seoul |
| 부산광역시 | Busan |
| 경기도 | Gyeonggi-do |
| 제주특별자치도 | Jeju |
| ~시 | ~-si |
| ~군 | ~-gun |
| ~구 | ~-gu |
도로명
| 한글 | 영문 |
|---|---|
| ~대로 | ~-daero |
| ~로 | ~-ro |
| ~길 | ~-gil |
상세주소
| 한글 | 영문 |
|---|---|
| 830동 802호 | 830-802 |
| 지하1층 | B1 |
| 3층 | 3F |
출력 순서 (서양식)
상세주소, 번지 도로명, 구/군, 시, 도, Korea
예: 830-802, 50 Godeokgukje 1-ro, Pyeongtaek-si, Gyeonggi-do, Korea
유연한 처리 예시
배송 메모 포함
입력: 서울특별시 마포구 삼개로 9 (도화동) 지하1층.AS간판(생활맥주 옆가게)입구 배너안쪽에 숨겨놔주세요~!
출력: B1, 9 Samgae-ro, Mapo-gu, Seoul, Korea
(배송 메모는 제거)
괄호 안 건물명
입력: 경기도 평택시 고덕국제1로 50 (고덕동, 호반써밋 고덕신도시) 830동 802호
출력: 830-802, 50 Godeokgukje 1-ro, Pyeongtaek-si, Gyeonggi-do, Korea
(괄호 안 동명/건물명은 생략)
결과 보고
변환 완료 후 보고:
- 처리 파일: [파일명]
- 총 주소: [N]건
- 변환 성공: [N]건
- 빈 값: [N]건
- 추가된 컬럼 위치: [컬럼명] 옆
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 99 lines · 48 tokens per session scan A 6e40a1f3f286
excel-address-processor is an agent published in the GitHub repository joonlab/joonlab-claudecode-setting-for-share (10 stars, last pushed 26d ago), licensed MIT. It adds 48 tokens to every session and 932 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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