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/edgarnpx skills add eddmpython/dartlab --skill edgargit 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 | $0.03756 |
| Opus 5 | $0.00000 | $0.01878 |
| Sonnet 5 | $0.00000 | $0.00751 |
| Haiku 4.5 | $0.00000 | $0.00376 |
Grade A, and why
edgar 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
엔진 역할
edgar 는 별도 사용자 capability 가 아니라 — dartlab.Company facade 가 ticker / CIK 를 받았을 때 자동 활성화되는 provider 다. DartCompany ↔ EdgarCompany 는 public 메서드 양쪽 동등 SSOT — panel / select / trace / disclosure / liveFilings / readFiling / analysis / credit / quant 모두 같은 시그니처. 시장만 다름. (industry 는 KR 가치사슬 지도 전용 — US 동등 데이터 부재로 EXEMPT, 아래 EXEMPT 섹션 참조.)
EDGAR 재무는 두 소스로 분기한다 (DART 와 같은 메커니즘):
- 소문자 native (
c.panel("is"/"bs"/"cf"/"cis"/"sce"/"ratios")) — panel 단일 artifact 의 row payload 에 보존된 XBRL native 셀을 read-time 분해 (presentation role 앵커링). DARTc.panel("is")의 panel 셀 경로 미러.compare(codes, topic="is")finance 비교도 이 native 셀을 쓴다 (account=snakeId, USD 실값). - 대문자 companyfacts (
c.panel("IS"/"BS"/"CF"/"RATIOS")) — SEC companyfacts 벌크(edgar/finance) 위임. panel 과 무관, 항상 가용.
live filings 는 SEC EDGAR API. 거시는 FRED (gather("macro", "FEDFUNDS")).
⚠ native(소문자) 경로는 panel artifact 에 native payload 가 있어야 동작한다. 2026-06-06 이전 빌드된 panel 은 payload 가 없어
c.panel("is")·compare(topic=finance)가 빈 결과 —EDGAR_FULL_REBUILD=1(originalSync.yml dispatch) 전수 재빌드로 backfill. 대문자 companyfacts 경로는 영향 없음.
공개 호출 방식
import dartlab
# 1. Company facade — ticker 또는 CIK 인식 자동 EDGAR 라우팅
c = dartlab.Company("AAPL")
print(c.market) # "US"
print(c.panel.shape) # 항목 x 기간 격자 크기 (DART 와 동일 인터페이스)
# 2. DART 와 동등 인터페이스 (XBRL 자동 정규화)
bs = c.panel("BS", freq="Q")
is_y = c.panel("IS", freq="Y")
ratios = c.panel("ratios")
# 3. 공시
filings = c.filings() # 공시 문서 목록 + 링크
# 3.5. 공시 수평화 보드 — engines.panel 의 US 미러 (DART c.panel 과 동일 표면)
c.panel # item × 기간 wide (pl.DataFrame) — 잡는 순간 보드
c.panel("Risk") # 섹션 행 검색 (10-K item 본문)
c.panel("is", freq="year") # native 재무 (소문자 — panel payload 셀, role 앵커)
c.panel("ratios", freq="year") # native 재무비율 (core 공식, panel 자급)
c.panel("IS") # 대문자 — companyfacts 위임 (내부 finance, 항상 가용)
c.panel.search("supply chain") # 본문 전체검색
# 3.6. 회사 간 재무 비교 — 탑레벨 verb (DART 와 동일 호출계약, native 셀 기반)
import dartlab
dartlab.compare(["AAPL", "MSFT"], topic="is", freq="year") # account(snakeId)×회사, USD 실값
# 3.7. 전종목 횡단 scan — market="us" (companyfacts 기반 재무축)
dartlab.scan("profitability", market="us") # opMargin/netMargin/roe/roa + grade
dartlab.scan("account", "sales", market="us") # 단일 계정 전종목 시계열
# 4. 보조 엔진도 동일 (US 자동)
analysis = c.analysis("financial", "수익성")
credit = c.credit()
quant = c.quant("모멘텀")
# 5. 시장 데이터 (gather ticker 자동 판정)
price = dartlab.gather("price", "AAPL")
macro = dartlab.gather("macro", "FEDFUNDS")
news = dartlab.gather("news", "Apple", market="US")
# 6. 한글 별칭 자동 해결 — 영문 alias resolveEnglishAlias
result = dartlab.searchName("인텔") # → Intel (EDGAR 자동 재검색)
# 7. EDGAR 직접 client (저수준)
from dartlab import OpenEdgar
client = OpenEdgar()
filings = client.filings("AAPL", form="10-K", limit=5)
What ships with it
1 file 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 · 261 lines · 0 tokens per session scan A dc943c5ac693
edgar 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 3,756 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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