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 agentmods add skills/eddmpython/dartlab/viznpx skills add eddmpython/dartlab --skill vizgit clone --depth 1 https://github.com/eddmpython/dartlabWhat 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 | $0.00000 | $0.03449 |
| Opus 5 | $0.00000 | $0.01724 |
| Sonnet 5 | $0.00000 | $0.00690 |
| Haiku 4.5 | $0.00000 | $0.00345 |
Grade A, and why
viz 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 2d 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
엔진 역할
viz 는 분석 결과를 차트 spec 으로 변환한다. 직접 차트 이미지를 그리지 않고 — spec dict 를 만들어 (a) emit_chart(spec) 로 stdout 마커 출력 → AI tool result 로 인라인 렌더 또는 (b) landing/static/charts/{code}/manifest.json 정적 빌드 → svelte ChartRenderer 가 등록 chartType 단일 분기 렌더.
evidence 회로 강제 — 모든 ChartSpec 은 evidenceBinding 또는 evidenceIds 가 채워져야 emit. drill-back 으로 차트 점 클릭 시 source 데이터 패널 진입.
공개 호출 방식
# RunPython 안에서
from dartlab.viz import emit_chart, emit_diagram
import dartlab
c = dartlab.Company("005930")
ratios = c.panel("ratios", freq="Q")
# 1. 시계열 line
emit_chart({
"chartType": "line",
"title": "영업이익률 추이",
"data": [{"period": p, "value": v}
for p, v in zip(ratios["period"], ratios["operatingMargin"])],
"xAxis": "period",
"yAxis": "value",
"unit": "%",
"evidenceBinding": {
"tableRef": "table:005930:ratios:Q",
"source": "dart",
"stockCode": "005930",
"topic": "ratios",
},
})
# 2. peer 비교 bar
emit_chart({
"chartType": "bar",
"title": "peer ROE",
"data": peer_rows,
"xAxis": "stockCode",
"yAxis": "roe",
"evidenceIds": ["scan:profitability:2025Q3"],
})
# 3. 다이어그램 (mermaid)
emit_diagram("mermaid", "graph LR\n A-->B\n B-->C")
# 4. CompileVisual tool (AI 도구 경로 — auto evidence)
# LLM 이 자율 호출
# 회사 페이지 정적 차트 빌드
uv run python -X utf8 landing/_scripts/buildCompanyCharts.py --code 005930
# → landing/static/charts/005930/manifest.json + section JSON
강행 호출 룰 (agent 답변 품질 회귀 차단)
차트 시각화에서 다음 4 룰 강행:
- 차트 생성은
CompileVisualtool 1 회 — chartType + data + 인자. RunPython 직접 matplotlib/plotly 호출 금지 (visualRef 미발급 → UI 렌더 실패). - 모든 차트의
evidenceBinding필수 — 차트 안 모든 값에 ref 박힌 source 명시. evidenceBinding 누락 시 거부 (해결책 포함 경고). - 데이터 부족 시 차트 만들지 마라 — 표 + coverage note 로 낮춘다. 환각 차트 (X 값 없는 그래프, peer 4 개 미만 분포 등) 금지.
- 본문 안 차트 인용에
[visualRef:...]표기 — UI 가 inline 렌더링하므로 ref id 필수.
호출 동작
emit_chart(spec) — ChartSpec dict 를 stdout 에 [VIZ_SPEC_START]...[VIZ_SPEC_END] 마커로 출력. agent 가 extract_viz_specs(stdout) 로 추출 → view_spec TraceEvent → 클라이언트 ChartRenderer 가 인라인 렌더. evidenceBinding 또는 evidenceIds 누락 시 거부 (해결책 포함 경고).
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 297 lines · 0 tokens per session scan A 345e328a56e2
viz is a skill published in the GitHub repository eddmpython/dartlab (209 stars, last pushed 10d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 3,449 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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