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-dashgit 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-dash)<a href="https://agentmods.dev/skills/kimlawtech/korean-jangbu-for/jangbu-dash"><img src="https://agentmods.dev/badge/skills/kimlawtech/korean-jangbu-for/jangbu-dash/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-dash"><img src="https://agentmods.dev/badge/skills/kimlawtech/korean-jangbu-for/jangbu-dash.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 | $0.00079 | $0.01186 |
| Opus 5 | $0.00039 | $0.00593 |
| Sonnet 5 | $0.00016 | $0.00237 |
| Haiku 4.5 | $0.00008 | $0.00119 |
Grade A, and why
jangbu-dash 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 11d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
jangbu-dash
경영 의사결정용 리포트 생성 스킬.
생성 가능한 리포트
| 리포트 | 도구 | 용도 |
|---|---|---|
| 월별 손익 | export_report(report_type="monthly_pl") |
연간 매출·비용 추이 |
| 현금흐름 | export_report(report_type="cash_flow") |
기간 inflow/outflow, 일별 net |
| Cash burn rate | export_report(report_type="burn_rate") |
최근 N개월 평균 월별 순지출 |
인터뷰 플로우
Step 1. 리포트 유형 선택
어떤 경영 리포트를 생성하시겠습니까?
[1] 월별 손익 추이 (연간)
[2] 현금흐름 (기간 지정)
[3] Cash burn rate (최근 6개월 기본)
[4] 비용 구조 분석
[A] 전체 (1~4 일괄)
번호를 입력하세요.
Step 2. 파라미터 입력
[1] 월별 손익:
연도를 입력하세요. 기본값: 2026.
[2] 현금흐름:
기간을 지정하세요.
- 직전 월 / 올해 누적 / 직접 입력
[3] Cash burn:
분석 기간(개월)을 입력하세요. 기본 6.
[4] 비용 구조:
월별 비용을 계정별로 stacked 집계.
(내부적으로는 monthly_pl + 계정별 breakdown 사용)
Step 3. 리포트 생성
# 월별 손익
export_report(report_type="monthly_pl", year=2026)
# 현금흐름
export_report(
report_type="cash_flow",
period_start="2026-01-01",
period_end="2026-04-30"
)
# Cash burn
export_report(report_type="burn_rate", months=6)
Step 4. 결과 요약 표시
월별 손익 예시:
2026년 월별 손익
매출 비용 순이익
2026-01 12,000,000 8,500,000 3,500,000
2026-02 14,500,000 9,100,000 5,400,000
2026-03 11,800,000 9,800,000 2,000,000
2026-04 15,200,000 10,300,000 4,900,000
출력 파일: ~/.jangbu/reports/monthly_pl_2026-04-22.json
Cash burn 예시:
최근 6개월 Cash burn rate
평균 월 burn: -3,200,000원 (순지출)
→ 현재 예금 8천만원 기준 약 25개월 런웨이
출력 파일: ~/.jangbu/reports/burn_rate_2026-04-22.json
Step 5. 추가 질문·해석
생성 후 사용자가 자연어로 후속 질문 가능:
- "광고비 비중이 큰 달은?" → monthly_pl 파일 열어 비교
- "어느 계정이 제일 많이 늘었어?" → 직전 기간 대비 증감 계산
주의: 후속 분석도 list_transactions 마스킹 뷰 또는 리포트 파일에서만 수행. 원본 거래 전체를 LLM이 직접 집계하지 않음.
Cash burn 해석
- 양수 (+): 순수익 상태 (수입 > 지출)
- 음수 (-): burn 상태 (지출 > 수입)
- 런웨이 계산: 현재 현금 / |평균 월 burn|
사용자가 현재 예금 금액을 알려주면 런웨이를 추가 계산해 제시.
공식 재무제표와의 차이
| 항목 | jangbu-tax | jangbu-dash |
|---|---|---|
| 기준 | 일반기업회계기준 | 현금 기준 |
| 용도 | 세무 신고·감사 | 의사결정 |
| 계정 | 국세청 표준 | 내부 유연 |
| 감가상각 | (한계 존재) | 미반영 |
| 부가세 | (간이) | 무시 (총액 기준) |
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.
- 11d ago First seen · 137 lines · 79 tokens per session scan A f3a291dbd935
jangbu-dash is a skill published in the GitHub repository kimlawtech/korean-jangbu-for (83 stars, last pushed 12d ago), licensed Apache-2.0. It adds 79 tokens to every session and 1,186 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.
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