🔬Experiment Analysis Skills
Browse the best Experiment Analysis skills for Claude Code, Codex, Cursor and other AI agents.
443 skills · Page 1 of 19
scikit-learn
★ 22ML toolkit. Classification, regression, clustering, PCA, preprocessing, pipelines, GridSearch, cross-validation, RandomForest, SVM, for general machine learning workflows.
scanpy-complete
Scanpy 单细胞分析工具包 - 100%覆盖文档（API+教程+预处理+分析+可视化）
scikit-learn
★ 14.5kMachine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
senior-data-scientist
★ 14.5kWorld-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
tensorboard
★ 481Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
moai-domain-data-science
Data analysis, visualization, statistical modeling, and reproducible research workflows.
senior-data-scientist
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
tensorboard
★ 15.4kVisualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
statistical-analysis
★ 14.5kStatistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
pymc-bayesian-modeling
★ 3.0kBayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
statistical-analysis
★ 3.0kStatistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
exploratory-data-analysis
★ 3.0kPerform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
weights-and-biases
★ 481Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
data-ml
★ 82Competence in data analytics and machine learning, enabling developers to build data-driven features and integrate AI/ML capabilities.
retention-analysis
★ 32Analyze user retention and churn using survival analysis, cohort analysis, and machine learning. Calculate retention rates, build survival curves, predict churn risk, and generate retention optimization strategies. Use when working with user subscription data, membership information, or when user mentions retention, churn, survival analysis, or customer lifetime value.
growth-model-analyzer
★ 32增长模型分析技能 - 提供全面的增长黑客分析工具，包括裂变策略评估、用户细分、Uplift建模、ROI优化等。支持多种增长场景的机器学习建模和智能决策建议。适用于用户增长、营销优化、产品迭代等增长分析场景。
attribution-analysis-modeling
★ 32Perform multi-touch attribution analysis using Markov chains, Shapley values, and custom attribution models. Use when you need to analyze marketing channel effectiveness, calculate conversion attribution, optimize marketing budgets, or understand customer journey paths. Supports channel transition analysis, ROI calculation, and marketing optimization insights with Chinese language support.
pyopenms
★ 22Mass spectrometry toolkit (OpenMS Python). Process mzML/mzXML, peak picking, feature detection, peptide ID, proteomics/metabolomics workflows, for LC-MS/MS analysis.
umap-learn
★ 22UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
aeon
★ 22Time series machine learning toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use this skill when working with temporal data, performing time series analysis, building predictive models on sequential data, or implementing workflows that involve distance metrics (DTW), transformations (ROCKET, Catch22), or deep learning for time series. Applicable for tasks like ECG classification, stock price forecasting, sensor anomaly detection, or activity recognition from wearable devices.
time-series-forecaster
★ 17Time series forecasting with ARIMA, Prophet, LSTM, and statistical methods. Activates for "time series", "forecasting", "predict future", "trend analysis", "seasonality", "ARIMA", "Prophet", "sales forecast", "demand prediction", "stock prediction". Handles trend decomposition, seasonality detection, multivariate forecasting, and confidence intervals with SpecWeave increment integration.
reasoning-abductive
★ 12Generate and evaluate explanatory hypotheses from incomplete observations. Use when diagnosing anomalies, explaining unexpected outcomes, or inferring causes from effects. Produces ranked hypotheses with evidence and confidence scores.
ml-model-training
★ 9Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
growth-marketing
★ 5Эксперт по growth-маркетингу. Используй для growth hacking, A/B тестов, воронок, viral loops и экспериментов роста.