시험 준비 종합 파이프라인. 출제경향분석→약점진단→맞춤학습설계→모의고사출제→오답분석을 에이전트 팀이 협업하여 수행한다. '시험 준비 도와줘', '모의고사 만들어줘', '기출 분석해줘', '약점 진단', '오답 분석', '수능 준비', '자격증 시험', '공무원 시험', '토익 준비', '학습 계획 세워줘' 등 시험 준비 전반에 이 스킬을 사용한다. 기존 성적표나 오답 데이터가 있으면 진단 단계에 활용한다. 단, 실제 시험 접수·결제, 학원 추천, 실시간 강의 제공은 이 스킬의 범위가 아니다.
학술 연구 방법론 설계의 상세 프레임워크를 제공하는 전문 스킬. methodology-expert 에이전트가 정량/정성/혼합 연구를 설계하고 표본, 도구, 분석 방법을 선정할 때 활용한다. '연구 방법론', '연구 설계', '표본 설계', '측정 도구', '타당도 신뢰도', '통계 분석 방법' 등의 맥락에서 자동 적용한다. 단, 통계 소프트웨어(SPSS, R) 직접 실행이나 IRB 승인 신청은 이 스킬의 범위가 아니다.
데이터 시각화 차트 선택 가이드, 정보 계층 설계 원칙, 숫자-차트 매핑 매트릭스를 제공하는 info-architect 확장 스킬. '차트 선택', '데이터 시각화', '그래프 종류', '정보 설계', '차트 컬러', '데이터 스토리텔링' 등 데이터를 시각적으로 표현할 때 사용한다. 단, 실제 차트 렌더링이나 BI 도구 연동은 이 스킬의 범위가 아니다.
Summarize a CSV, compute basic stats, and produce a markdown report + a plot image.
Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
通用数字永生框架：从聊天记录、社交媒体、文档等多平台数据中蒸馏任何人的数字分身——支持自己、同事、导师、亲人、伴侣/前任、朋友、公众人物 7 种角色模板，接入国内外 12+ 数据平台。
Parallel research using Fleet wave mechanics. Spawns multiple scout agents, each investigating a different angle of the same question. Findings are compressed between waves. Produces a unified research brief from multiple independent perspectives.
Performs arbitrary-precision arithmetic calculations including addition, subtraction, multiplication, division, and exponents. Use when the user asks to calculate, compute, or evaluate math expressions, or when precise decimal arithmetic is needed to avoid floating-point errors.
Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses.
Prepares statistical tables and reports for clinical trials following regulatory standards. Generates Table 1 baseline characteristics, defines analysis populations (ITT, per-protocol, safety), performs multiple imputation for missing data, and follows CONSORT and ICH E9 guidelines. Use when creating analysis reports, handling missing data, or preparing regulatory submissions from clinical trials.
Creates reproducible Jupyter notebooks for bioinformatics analysis with parameterization using papermill. Use when generating automated analysis reports, running notebook-based pipelines, or creating shareable computational notebooks.
Generates standardized quality control reports by aggregating metrics from FastQC, alignment, and other tools using MultiQC. Use when summarizing QC metrics across samples, creating shareable quality reports, or building automated QC pipelines.
Applies multiple testing correction methods including FDR, Bonferroni, and q-value for genomics data. Use when filtering differential expression results, setting significance thresholds, or choosing between correction methods for different study designs.
Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, and Cochran-Mantel-Haenszel tests. Computes effect sizes and post-hoc pairwise comparisons. Use when analyzing categorical outcomes or testing treatment-outcome independence in clinical trials.
Performs stratified and subgroup analyses for clinical trial data. Covers Mantel-Haenszel pooling, Breslow-Day homogeneity testing, interaction terms in regression, multiple comparisons correction, and forest plot visualization. Use when analyzing treatment effects across patient subgroups or controlling for stratification variables.
Create publication-quality scientific figures with ggplot2 including scatter plots, boxplots, heatmaps, and multi-panel layouts. Use when creating static figures for papers, presentations, or reports in R.
Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures.
Reusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots.
Compare two containers using native portainer tool data collection and render SVG/CSV outputs.
Problem-solving strategies for modular arithmetic in graph number theory
Problem-solving strategies for graph algorithms in graph number theory
SaaS analytics event taxonomy, metric formulas (MRR, churn, LTV), provider-agnostic tracking, funnel analysis, cohort setup, and privacy-respecting instrumentation.
Structured pipeline for statistical analysis deliverables — SPSS, R, Python. Covers reliability, chi-square, correlation, regression, assumption checking, and client-ready reporting.
