jangbu-tag

jangbu-tag is a skill for Claude Code, Codex from kimlawtech/korean-jangbu-for. It costs 80 tokens per session (1,645 once invoked), scanned A, original, Apache-2.0.

A bookkeeping tool that assigns Korean business transactions to accounting categories using rules first, with masked data sent for further classification only when rules cannot decide.

In plain words
What is it for?
Use it to classify transactions, map internal categories to Korean tax-office standard categories, and prepare categorized data for tax reports.
Why use it?
It reduces manual transaction sorting while keeping sensitive transaction details masked and allowing a user to review uncertain results.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to classify transactions, map internal categories to Korean tax-office standard categories, and prepare categorized data for tax reports.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kimlawtech/korean-jangbu-for/jangbu-tag
Install

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.

Any agent
npx skills add kimlawtech/korean-jangbu-for --skill jangbu-tag
Clone the repo
git clone --depth 1 https://github.com/kimlawtech/korean-jangbu-for

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for jangbu-tag

README.md
[![agentmods](https://agentmods.dev/badge/skills/kimlawtech/korean-jangbu-for/jangbu-tag/github.svg)](https://agentmods.dev/skills/kimlawtech/korean-jangbu-for/jangbu-tag)
Your own site
<a href="https://agentmods.dev/skills/kimlawtech/korean-jangbu-for/jangbu-tag"><img src="https://agentmods.dev/badge/skills/kimlawtech/korean-jangbu-for/jangbu-tag/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.

agentmods 80×15 button for jangbu-tag

Your own site · 80×15
<a href="https://agentmods.dev/skills/kimlawtech/korean-jangbu-for/jangbu-tag"><img src="https://agentmods.dev/badge/skills/kimlawtech/korean-jangbu-for/jangbu-tag.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,645 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00080 $0.01645
Opus 5 $0.00040 $0.00822
Sonnet 5 $0.00016 $0.00329
Haiku 4.5 $0.00008 $0.00164

Measured 12d ago against content hash 545a18fda02e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

jangbu-tag 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.

skills/jangbu-tag/SKILL.md · 166 lines

How it starts

The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.

jangbu-tag

거래내역 → 계정과목 매핑 스킬.

분류 전략

  1. 룰 기반 (LLM 미경유)classify_with_rules 호출로 일괄 처리
  2. LLM fallback — 룰 실패 건에 한해 마스킹된 뷰만 LLM에 전달
  3. 사용자 확인 — 낮은 confidence 건은 사용자 검증 후 apply_classification(source="user")

계정 구조 (이중)

  • 내부 계정: 유연. 예) 광고선전비_구글, 광고선전비_메타, SaaS_노션
  • 국세청 계정: 고정. accounts/nts_standard.json 매핑 테이블 경유
  • 세무 리포트 생성 시 내부 → 국세청 자동 변환

인터뷰 플로우

Step 1. 대상 기간 확인

분류할 거래 기간을 선택하세요.

[1] 이번 달
[2] 전월
[3] 올해 전체
[4] 특정 기간 입력

미분류 거래만 처리합니다.

Step 2. 룰 기반 일괄 분류

classify_with_rules(start_date="2026-04-01", end_date="2026-04-30")

결과 예시:

룰 분류 완료: 87건
분류 실패(LLM fallback 필요): 13건

Step 3. LLM fallback (분류 실패 건)

핵심: list_transactions(unclassified_only=True) 응답은 이미 마스킹된 뷰.

LLM 분류 프롬프트 템플릿
다음 거래를 한국 일반기업회계기준 계정과목으로 분류하세요.
거래처명·적요에 토큰(TK_*)이 있으면 그대로 두고 문맥으로만 판단.

거래 목록:
{
  "tx_abc": {"date": "2025-09-07", "amount": 142025, "direction": "outflow", 
             "counterparty": "CLAUDE.AI SUBSCRIPTION", "description": "..."},
  "tx_def": {...}
}

사용 가능 계정과목 (이 목록 외 사용 금지):
- 복리후생비: 직원 복리·회식·식대·간식·건강검진
- 여비교통비: 택시·KTX·주유·출장
- 접대비: 고객 접대 (5만원 이상 식사가 유력 신호)
- 통신비: 휴대폰·인터넷·유선전화
- 수도광열비/전력비: 수도·전기·가스
- 임차료: 월세·사무실 임대
- 지급수수료: SaaS 구독·은행 수수료·법무·회계·세무 용역비
- 광고선전비: 페이스북·구글·네이버 광고
- 도서인쇄비: 책·출판물·인쇄
- 소모품비: 사무용품·다이소·IKEA
- 세금과공과: 4대보험·세금·지방세
- 수선비·보험료·차량유지비·운반비·교육훈련비·회의비
- 급여·상여금·퇴직급여·잡급
- 매출액/이자수익 (inflow일 때)

각 거래에 대해 JSON으로 반환:
{
  "tx_abc": {"internal_account": "지급수수료", "confidence": 0.92, 
             "reason": "CLAUDE.AI는 AI SaaS 구독"},
  ...
}

confidence < 0.7인 건은 "사용자 확인 필요"로 표시.
적용 절차
  1. list_transactions(unclassified_only=True, limit=50) — 50건씩 배치
  2. 위 프롬프트로 LLM 호출 → JSON 결과
  3. confidence ≥ 0.7: apply_classification(source="llm") 자동 저장
  4. confidence < 0.7: Step 4로 사용자 확인

LLM 분류 시 반드시 한국 일반기업회계기준의 표준 계정 사용.

Step 4. 낮은 confidence 건 사용자 확인

다음 거래의 계정을 확인해주세요.

거래: TK_ACCOUNT_ID_xxxx | 2026-04-15 | 500,000원 | outflow
적요: (마스킹된 내용)
추정 계정: 지급수수료 (confidence=0.65)

[1] 지급수수료 유지
[2] 다른 계정 지정
[3] 스킵 (미분류 유지)

Read the full file on GitHub · 166 lines

Changes

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.

  1. 12d ago First seen · 166 lines · 80 tokens per session scan A 545a18fda02e

Subscribe to this mod's changes

jangbu-tag is a skill published in the GitHub repository kimlawtech/korean-jangbu-for (83 stars, last pushed 13d ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,645 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

bookkeeping

Use when a small business needs audit-ready books — a chart of accounts, posting a transaction to the right account and side, clearing an uncategorized bank feed, cash vs accrual, or a ledger that won't tie to the bank. NOT interpreting the numbers — runway, burn, P&L cadence (that is finance-ops), NOT issuing…

ericrisco/rsc-harness · 84 tokens

countbean-accounting

Rules for writing correct double-entry Beancount transactions when managing a Countbean cloud book — account naming, how to balance postings, categorisation, and safe ingestion. Use whenever recording, importing, or correcting financial data in a Countbean book.

CPUtester5465/countbean-plugin · 54 tokens

ai-anomaly-detection

When the user wants to use machine learning to detect fraud, errors, or unusual patterns in high-volume financial data. Also use when the user mentions "ML fraud detection," "unsupervised learning for audit," "isolation forest," "autoencoders for finance," "unusual transaction clusters," or "automated expense…

GAJETOso/financeskills · 73 tokens

bukio-cli

Drive bukio-cli — agent-first double-entry bookkeeping for SMEs across eleven jurisdictions. Book invoices, archive the originals, keep the books balanced.

erikvankempen/bukio-cli · 32 tokens

QuickBooks Automation

Automate QuickBooks accounting workflows including invoicing, expenses, reporting, and bank reconciliation.

claude-office-skills/skills · 21 tokens

ledger

Use for ANY monetary, accounting, ledger, journal, balance, P&L, fee, FX, invoice, tax, cost-basis, or valuation work. Exact Money.from (never floats or parseFloat), double-entry via JournalEntry + validateEntry + Ledger.apply, MCP moneycompute / ledgerpost, canon citations, Zero-Skip + Canonical Financial Artifact.…

eternal-roman/ledger · 97 tokens