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 eddmpython/dartlab --skill simulategit clone --depth 1 https://github.com/eddmpython/dartlabWrote 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/eddmpython/dartlab/simulate)<a href="https://agentmods.dev/skills/eddmpython/dartlab/simulate"><img src="https://agentmods.dev/badge/skills/eddmpython/dartlab/simulate/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/eddmpython/dartlab/simulate"><img src="https://agentmods.dev/badge/skills/eddmpython/dartlab/simulate.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 | $0.00000 | $0.03955 |
| Opus 5.5 | $0.00000 | $0.01582 |
| Sonnet 5.5 | $0.00000 | $0.00791 |
| Haiku 4.5 | $0.00000 | $0.00396 |
Grade A, and why
simulate 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 15d 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
엔진 역할
simulate는 회사 원자료와 매크로 시나리오를 결정론 드라이버 시트로 연결하는 L3 엔진이다. 난수를 쓰지 않으며 같은 target, scenario, asOf는 같은 노드별 inputsHash를 만든다. 결과는 미래 확정치가 아니라 입력 가정에 조건부인 변환 결과다.
드라이버 시트는 매크로 변수와 전달 채널마다 노드 하나를 둔다. 노드 감사에서 어느 입력이 어느 숫자를 움직였는지 그대로 읽힌다.
| 노드 | 의존 | 역할 |
|---|---|---|
macro.path, macro.rate, macro.fx |
없음 | 프리셋 GDP, 기준금리, 원달러 경로 |
rev.path |
GDP, 환율 | 매출 채널 |
margin.path |
GDP, 금리 | 영업이익률 채널 (금리는 금융업 NIM만) |
wacc.path |
금리 | 할인율 채널 (금리 변화의 절반) |
proforma |
매출, 마진 | 성장 경로와 영업이익률 충격으로 3표 추정 |
dcf |
proforma, WACC | 연도별 시나리오 WACC로 FCFF 할인 |
금리 충격은 WACC 경로를 통해 주당 DCF를 움직인다. 마진 경로는 proforma 영업이익에 충격으로 반영되어 FCF를 움직인다.
사용자 가정: 시나리오 경로와 드라이버 override
프리셋 다섯 개 밖의 가정은 사용자가 직접 넣는다. scenario, strategies 두 축 모두 경로와 드라이버 변경을 받으며, 각 축에서 실제 계산에 쓰는 값만 허용한다.
- 사용자 시나리오:
scenario에 dict를 넘긴다. 키는name,base,gdp,rate,fx다.base프리셋(기본baseline)의 경로 중 준 변수만 바꾼다. GDP 성장률과 기준금리는 %, 환율은 원달러 수준이다. 세 경로를 모두 주면 프리셋 길이 3년을 넘어 10년까지 펼칠 수 있다. - 드라이버 override:
overrides에 dict를 넘긴다.baseWacc,terminalGrowth,baseMargin은 %,revenueToGdp,revenueToFx,marginToGdp,nimToRate는 업종 탄성 단위다. 정한 값이 대신한 기본값 가정은assumptions에서 빠진다. - 검증: 모르는 키, 숫자가 아닌 값, 범위 밖 값(금리 0
30%, GDP -3030%, 환율 100~10,000 등), horizon보다 짧은 경로, 프리셋과 같은name은 실행 전에AssumptionInputError로 실패한다. 조용히 자르거나 보정하지 않는다. - 추적: 결과의
scenarioKind는user,scenarioBase는 바꾸지 않은 경로의 출처 프리셋이다.macroPaths는 실제로 쓴 세 경로다. 바꾼 root node는 provenanceuser:{name}, refuser:scenario/{name}#{variable}을 남기고,assumptionLedger에source="user"행이appliedToDriverSheet=True로 남는다. 바꾸지 않은 root는 같은 프리셋 node와 inputsHash가 같다.
shock = dartlab.simulate("005930", scenario={"name": "rateShock", "rate": [5.0, 5.5, 5.5]})
shock.scenarioKind, shock.macroPaths["rate"], shock.waccPath, shock.dcfPerShare
slow = dartlab.simulate(
"005930",
scenario={"name": "slowDecade", "gdp": [1.0] * 5, "rate": [3.0] * 5, "fx": [1450.0] * 5},
horizon=5,
overrides={"baseWacc": 12.0, "terminalGrowth": 2.0},
)
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.
- 15d ago Changed · +64 lines 3b90e4f6c949
- 16d ago Changed · +34 lines b37189db2b62
- 1mo ago First seen · 184 lines · 0 tokens per session scan A 9b1c7b8fd100
simulate is a skill published in the GitHub repository eddmpython/dartlab (225 stars, last pushed 3d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 3,955 tokens. 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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