스킬 denario
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가설 생성, 방법론 개발, 계산 실험, LaTeX 논문 작성을 자동화하여 데이터셋을 출판-ready 연구로 변환합니다. Multiagent AI가 종단 간 연구 파이프라인을 위한 전문 에이전트를 조정합니다.
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"denario" 사용 중입니다. 기후 데이터에 대한 연구 생성
예상 결과:
- Research hypothesis: Quantify global warming acceleration using linear regression
- Methodology: Time-series decomposition, trend analysis, statistical significance testing
- Results: 0.18°C per decade warming trend, p < 0.001
- Paper: Complete LaTeX document with figures and bibliography
"denario" 사용 중입니다. 고객 이탈 데이터셋 분석
예상 결과:
- Research idea: Ensemble model combining XGBoost and Random Forest with SMOTE
- Methodology: Train/test split, cross-validation, hyperparameter tuning, SHAP analysis
- Results: 85% AUC-ROC, key churn factors identified
- Paper: Journal-formatted manuscript with performance visualizations
보안 감사
낮은 위험v4 • 1/17/2026
All 369 static findings are FALSE POSITIVES. The skill is documentation-only with bash command examples, API key configuration patterns, and file operations for research project management - all legitimate documented functionality for a scientific research automation tool.
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출판 파이프라인 가속화
원시 데이터셋에서 형식화된 저널 제출까지의 여정을 자동화하여 방법론 설계 및 논문 작성에 소요되는 시간 단축
구조화된 탐색 워크플로우
탐색적 데이터 분석을 생성된 가설, 방법, 문서화와 함께 재현 가능한 연구 프로젝트로 변환
연구 모범 사례 학습
구조화된 계산 연구 및 과학적 글쓰기 접근 방식을 이해하기 위해 AI가 생성한 방법론 따르기
이 프롬프트를 사용해 보세요
기후 분석
Analyze global temperature anomaly data from 1880-2023 using pandas, scipy, and matplotlib. Generate research questions about long-term warming trends, develop a methodology with linear regression, and create publication-ready paper.
유전자 발현 연구
Process gene expression microarray data from 500 breast cancer patients with 20000 features. Generate hypotheses about treatment response predictors, create methodology with machine learning classification, and produce LaTeX paper.
금융 예측
Analyze monthly unemployment rates from 1950-2023 with GDP and inflation indicators. Generate SARIMAX forecasting methodology, execute analysis with confidence intervals, and write research paper.
전체 연구 파이프라인
Use denario to run complete research pipeline: set data description for my IoT sensor dataset, generate research idea for anomaly detection, develop methodology with sklearn, execute analysis, and generate APS-formatted LaTeX paper.
모범 사례
- 데이터 형식, 크기, 알려진 도전 과제, 사용 가능한 도구를 포함한 자세한 데이터 설명을 제공하여 AI 생성 출력 개선
- 계산적으로 비용이 높은 분석 단계 실행 전에 생성된 아이디어 및 방법론 검토하고 정제
- 버전 관리를 위해 각 파이프라인 단계 후 커밋하여 연구 진화 추적 및 재현 가능성 보장
피하기
- 모호한 데이터 설명 제공은 일반적이고 사용 불가능한 연구 아이디어 및 방법론으로 이어짐
- 실행 전 방법론 검토를 건너뛰면 데이터에 부적합한 통계적 접근 방식이 발생할 수 있음
- 중간 검증 없이 전체 파이프라인 실행은 결함이 있는 연구 방향에 컴퓨팅 자원 낭비
자주 묻는 질문
denario가 지원하는 LLM 공급자는 무엇입니까?
OpenAI GPT-4/3.5, Google Vertex AI Gemini/PaLM, AG2 또는 LangGraph 프레임워크와 호환되는 모든 공급자 지원.
이 스킬에 LaTeX가 필요합니까?
LaTeX는 PDF 논문 생성에만 필요합니다. 전체 워크플로우는 LaTeX 없이 사용하고 LaTeX 소스 파일을 얻을 수 있습니다.
자체 연구 방법론을 사용할 수 있습니까?
네, set_method()를 사용하여 마크다운 방법론 또는 파일 경로를 제공하여 자동 생성을 건너뛸 수 있습니다.
필요한 Python 버전은 무엇입니까?
Python 3.12 이상이 이 스킬이 제대로 작동하는 데 필요합니다.
사전 계산된 결과를 제공할 수 있습니까?
네, set_results()를 사용하여 기존 분석 결과를 제공하고 계산 실행 단계를 건너뛸 수 있습니다.
논문 형식을 지원하는 저널은 무엇입니까?
American Physical Society(APS) 형식이 포함되어 있습니다. 추가 저널 형식은 문서를 확인하세요.