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 skills add kimlawtech/korean-jangbu-for --skill jangbu-importgit clone --depth 1 https://github.com/kimlawtech/korean-jangbu-forWrote 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/skills/kimlawtech/korean-jangbu-for/jangbu-import)<a href="https://agentmods.dev/skills/kimlawtech/korean-jangbu-for/jangbu-import"><img src="https://agentmods.dev/badge/skills/kimlawtech/korean-jangbu-for/jangbu-import/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kimlawtech/korean-jangbu-for/jangbu-import"><img src="https://agentmods.dev/badge/skills/kimlawtech/korean-jangbu-for/jangbu-import.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00097 | $0.02185 |
| Opus 5 | $0.00048 | $0.01092 |
| Sonnet 5 | $0.00019 | $0.00437 |
| Haiku 4.5 | $0.00010 | $0.00218 |
Grade A, and why
jangbu-import 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
jangbu-import
원본 파일 → 표준 거래내역 변환 스킬.
입력 지원
| 소스 | 포맷 | MCP 도구 |
|---|---|---|
| 은행 거래내역 | CSV | ingest_raw(source_type="bank") |
| 카드 거래내역 | CSV | ingest_raw(source_type="card") |
| 수기 장부 | 엑셀 | ingest_raw(source_type="manual") |
| 영수증 | JPG/PNG/PDF | ocr_document(doc_type="receipt") |
| 세금계산서 | ocr_document(doc_type="tax_invoice") |
|
| 통장 스캔 | JPG/PDF | ocr_document(doc_type="bank_statement_scan") |
| 카드사 회원이용내역서 | ocr_document(doc_type="card_statement_scan") |
카드명세서 지원 현황:
- 신한카드 회원이용내역서 — 완전 지원 (표 파서 구현)
- 국민·삼성·현대·롯데·우리·하나·BC카드 — 발급사 자동 탐지만 지원, 파서는 LLM fallback
표준 스키마 (13개 필드)
필수 9 + 확장 4.
- 필수:
transaction_id,date,amount,currency,direction,counterparty,description,source,source_ref - 확장:
raw_description,account_id,matched_account,confidence
인터뷰 플로우
Step 1. 입력 소스 확인
어떤 데이터를 추가하시겠습니까?
[1] 은행 거래내역 (CSV)
[2] 카드 거래내역 (CSV)
[3] 엑셀 장부 (xlsx)
[4] 영수증 이미지/PDF (PaddleOCR 처리)
[5] 세금계산서 PDF (PaddleOCR 처리)
[6] 카드사 회원이용내역서 PDF (PaddleOCR + 표 파서)
[7] 여러 파일 일괄 업로드
번호를 입력하세요.
[6] 카드명세서 PDF 선택 시 경고:
⚠ 카드사 회원이용내역서에는 주민번호·사업자번호 등 민감정보가
포함되어 있습니다. 처리 전 확인:
1. 본인 소유 문서가 맞습니까? [Y/n]
2. 파일은 ~/.jangbu/raw/ocr/ 에만 저장되며
LLM에는 마스킹 뷰와 요약만 전달됩니다.
Step 2. 파일 경로·메타데이터 수집
- 파일 경로 (절대경로 권장)
account_id(내부 계좌/카드 ID, 예:acct_shinhan_001,card_bc_002)- 은행이면 은행명 (generic / kb / shinhan / woori / hana / ibk)
Step 3. MCP 서버 호출
CSV/엑셀 (선택지 1·2·3):
ingest_raw(
file_path="/절대/경로/파일.csv",
source_type="bank", # or card / manual
account_id="acct_shinhan_001",
bank="shinhan" # bank일 때만
)
OCR (선택지 4·5·6·7):
ocr_document(
file_path="/절대/경로/영수증.jpg",
doc_type="receipt", # receipt / tax_invoice / bank_statement_scan / card_statement_scan
account_id="card_bc_002", # 카드명세서면 base prefix로 사용, 카드별 last3 자동 suffix
auto_ingest=True # 영수증·카드명세서면 자동 거래 등록
)
카드명세서 응답 예시:
{
"doc_type": "card_statement_scan",
"parsed_rows": 287,
"ingested": 285,
"duplicates_skipped": 2,
"issuer": "shinhan",
"period": {"start": "2025-07-01", "end": "2025-12-31"},
"card_last3_list": ["039", "122"],
"unparsed_count": 3,
"needs_llm_fallback": false,
"structured": {
"rows_sample": [
{"use_date": "2025-07-01", "merchant": "가상식당 테스트점",
"amount": "6500", "biz_id": "TK_BIZ_ID_a1b2", "card_last3": "122"}
]
}
}
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.
- 12d ago First seen · 220 lines · 97 tokens per session scan A 1be59443f8a6
jangbu-import is a skill published in the GitHub repository kimlawtech/korean-jangbu-for (83 stars, last pushed 13d ago), licensed Apache-2.0. It adds 97 tokens to every session and 2,185 once invoked, about $0.0005 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 2026-08-30.
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