사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill extract-empirical-regularity시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
extract-empirical-regularity
Purpose
Extract a repeatable empirical pattern from records while preserving conditions, exceptions, and uncertainty.
Input contract
yaml
required: [observations, variable_schema, condition_schema]
optional: [replication_records, measurement_uncertainty]
constraints: [regularity claims require multiple comparable observations or an explicit single-case limitation]Procedure
- Normalize observations, units, and conditions.
- Identify repeated associations, trends, or invariants.
- Test exceptions, alternative explanations, and measurement artifacts.
- State the regularity with scope and uncertainty boundaries.
If the regularity is stable enough to express through measurable factors and outcomes, consider identify-variables as the next tactic.
Output contract
yaml
produces: [regularity_statement, supporting_records, exception_set, scope_conditions]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]Quality gates
- Conditions and exceptions accompany every regularity.
- Correlation is not labeled mechanism without supporting evidence.
Failure and counterexamples
Do not generalize a pattern across changed populations or protocols without a comparability check.
Provenance map
resolved: extract-empirical-regularity
