Engines observed

Analysis - 지배구조

analysis engine application skill for the 지배구조 axis: 이 회사의 주인은 누구이며 감시는 작동하는가.

engines.analysis.governance GitHub 원본

출력

기대 결과

  • public call
  • representative return shape
  • verification result

엔진 역할

analysis engine application skill for the 지배구조 axis: 이 회사의 주인은 누구이며 감시는 작동하는가.

공개 호출 방식

import dartlab
c = dartlab.Company("005930")
result = c.analysis("지배구조", "지배구조")

호출 동작

Reads the Company financial/disclosure/market snapshot required for the 지배구조 axis. The guide reports 8 calculation items. Missing values are represented through flags, assumptions, dataAsOf, nulls, or empty history; they are not zero-filled.

대표 반환 형태

Returns a dict. Check items, history, displayHints, turningPoints, dataAsOf, assumptions, flags, _summary, tableRef, valueRef, dateRef, and executionRef-style evidence fields.

기본 실행 순서

  1. target, period, and source data are fixed first.
  2. Run the public call exactly as documented.
  3. Check latestAsOf/date, missing values, flags, and assumptions.
  4. Bind numeric claims to tableRef/valueRef/dateRef/executionRef.
  5. Hand off multi-axis narrative composition to story or the parent report skill.

기본 검증

This skill is a public execution document. If this axis call, representative return keys, error behavior, or runtime limits change, update this file in the same change.

런타임

실행 환경별 호환성

환경상태비고 / 제한
Local Python supported
Server supported
MCP supported
Web AI supported
Pyodide limited

실패 회피

흔한 실패 · 절대 금지

절대 금지
  • Do not fabricate numbers.
  • Do not zero-fill missing values.
  • Do not treat one axis result as a final investment conclusion.