No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by kimlawtech · MCP Server · ★ 82
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is korean-jangbu-for safe to install? View the security audit →
korean-jangbu-for 한국 스타트업·1인 법인 대표·프리랜서·개인사업자를 위한 장부 자동 생성 Claude Code 스킬 패키지. 카드명세서 PDF·은행 CSV·영수증을 넣으면 표준 거래내역·계정과목 매핑·재무제표·세무사 전달 CSV가 자동 생성됩니다. 한국 법률 AI 허브 SpeciAI 에서 만들고 있어요. 계약·노동·투자·지재권·세무를 AI로 해결하는 창업자·변호사 커뮤니티에 초대합니다. → discord.gg/wQWpEpnBfE 라이선스: Apache-2.0 버전: 0.2.0 저자: @kimlawtech (SpeciAI) 설계 원칙 입력은 넓게 — 엑셀·은행 CSV·카드 내역·영수증 이미지·세금계산서 PDF 출력은 단계적 — 세무용(BS·PL) / 경영용(현금흐름·cash burn) 계정체계는 매핑 — 내부 계정(유연) ↔ 국세청 표준계정(고정) 보안 Level 2 — 민감정보 마스킹 + 룰 우선 분류 + LLM fallback 최소화 스킬 구성 진입점 1개 + 하위 스킬 6개 + MCP 서버 1개. 지원 파일 포맷 PNG · JPG · JPEG
| Stars | 82 |
| Forks | 17 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 63.9217335989456/100 |
| Last Updated | 2026-08-29 |
| Created | 2026-04-22 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~14k |
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korean-jangbu-for is 한국 스타트업, 1인 법인, 프리랜서, 개인 사업자를 위한 장부 자동 생성 Claude Code 스킬. 카드명세서 PDF·은행 CSV → 재무제표·세무사 전달 CSV 자동 생성. Level 2 민감정보 마스킹 적용.. It is categorized as a MCP Server with 82 GitHub stars.
korean-jangbu-for is primarily written in Python. It covers topics such as accounting, bookkeeping, ceo.
You can find installation instructions and usage details in the korean-jangbu-for GitHub repository at github.com/kimlawtech/korean-jangbu-for. The project has 82 stars and 17 forks, indicating an active community.
korean-jangbu-for is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to korean-jangbu-for on Agent Skills Hub include mcp-fiscal-brasil, pdf-mcp, lex. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: