🔬Experiment Analysis Skills
Browse the best Experiment Analysis skills for Claude Code, Codex, Cursor and other AI agents.
443 skills · Page 2 of 19
hypothesis-tree
★ 3Structure complex questions into testable hypotheses. Use when validating product ideas, debugging problems, planning experiments, or breaking down ambiguous challenges into actionable research.
marketing-experimentation
★ 1Systematic marketing experimentation process - discover concepts, generate hypotheses, coordinate multiple experiments, synthesize results, generate next-iteration ideas through rigorous validation cycles
tuning-hyperparameters
★ 1Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. Finds best parameter configurations to maximize performance. Use when asked to "tune hyperparameters" or "optimize model".
advanced-analytics
★ 1Advanced analytics including machine learning, predictive modeling, and big data techniques
ab-test-analysis
Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
fyp-jupyter
Complete data science research workflow for Jupyter notebooks covering CRISP-DM methodology from data loading through model validation, with MLflow experiment tracking integration, phase-based workflow guidance (Exploration, Systematic Experimentation, Analysis, Documentation), and skill integration points. Use when working on FYP data science projects requiring systematic data preprocessing, EDA, feature engineering, modeling, statistical validation, experiment tracking, or needing guidance on what to work on at each project phase. Includes MLflow setup for tracking 30+ experiment runs, weekly work planning for 10-week FYP timeline, and clear decision framework for when to use which skill (fyp-jupyter, crossvit-covid19-fyp, fyp-statistical-validator, tar-umt-fyp-rds, tar-umt-academic-writing).
fyp-statistical-validator
Automate statistical validation, hypothesis testing, and confidence interval calculations required by TAR UMT thesis standards and top ML research conferences. Use when you need to (1) calculate 95% confidence intervals for model performance metrics, (2) perform hypothesis testing to compare models (McNemar's test, paired t-test, Bonferroni correction), (3) generate APA-formatted results tables for thesis chapters, (4) create reproducibility statements for experimental setup, (5) validate statistical significance of model improvements, or (6) format results according to academic publishing standards. Essential for FYP Chapter 4 (experimental setup documentation) and Chapter 5 (results with statistical validation).
mechinterp-investigator
Orchestrate a systematic research program to investigate and meaningfully label SAE features
ds-star
Multi-agent data science framework using DS-STAR (Data Science - Structured Thought and Action) architecture. Automates data analysis through collaborative AI agents with multi-model support (Haiku, Sonnet, Opus). Use for exploratory data analysis, automated insights, and iterative data science workflows.
scvitools-complete
scvi-tools 单细胞深度学习框架 - 100%覆盖文档（完整API+教程+模型训练+多模态整合）
moai-domain-ml
Machine learning model training, evaluation, deployment, and MLOps workflows.
machine-learning
Supervised & unsupervised learning, Scikit-learn, model evaluation, feature engineering
ds-star
Multi-agent data science framework using DS-STAR (Data Science - Structured Thought and Action) architecture. Automates data analysis through collaborative AI agents with multi-model support (Haiku, Sonnet, Opus). Use for exploratory data analysis, automated insights, and iterative data science workflows.
training-insights-analyzer
Analyze training patterns, performance, and physiological data using advanced computational models.AUTO-ACTIVATES when user asks about:- Training status/progress ("How am I doing?", "How's my training?")- Load/overtraining ("Am I overtraining?", "Is my load too high?")- Performance questions ("Why did I fade?", "What's limiting me?")- Specific run analysis ("Why was my HR high?", "What happened yesterday?")- Race readiness ("Am I ready?", "Will I make my goal?")- Physiological zones ("What are my zones?", "What's my threshold?")- Recovery/freshness ("Should I run today?", "Am I recovered?")- Patterns/causation ("What's causing...", "What predicts my best runs?")PROACTIVELY WARNS when detecting:- ACWR > 1.3 (load spike)- TSB < -25 (fatigue accumulation)- Correlation shifts (relationship changes)- Form breakpoints earlier than usual- Recovery decline between intervals
ff-statistical-methods
Expert guidance on statistical analysis methodologies and Monte Carlo simulation for fantasy football. Use this skill when selecting regression approaches, designing simulations, performing variance analysis, or conducting hypothesis tests. Covers regression types (OLS, Ridge, Lasso, GAMs), Monte Carlo frameworks, regression-to-mean analysis, and statistical best practices for player performance modeling.
spata2-docs-local
SPATA2 空间转录组学分析工具包 - 100%覆盖414个核心文件（388个API参考+25个教程+1个主页）
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）
hierarchical-models
Patterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.
sfh-sim
Run acoustic simulations on horn geometries using BEM analysis. Use when evaluatinghorn performance, analyzing impedance curves, calculating polar patterns, or scoringgeometry variations. Returns acoustic metrics and visualization data.
mlflow
★ 15.4kTrack ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
pyvene-interventions
★ 15.4kProvides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
weights-and-biases
★ 15.4kTrack ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
umap-learn
★ 14.5kUMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
statsmodels
★ 14.5kStatistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.