Discover and use skill skills to extend Claude's capabilities
869 Skill Skills Available
Analyze a StarRocks query profile JSON to discover available metrics, identify bottlenecks, and suggest which metrics would be valuable to display.
Universal multi-perspective analyzer for any topic, file, idea, or decision. Extract key points, find gaps/risks, identify improvements with actionable plans.
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Generates professional infographics with 20 layout types and 17 visual styles. Analyzes content, recommends layout×style combinations, and generates publication-ready infographics. Use when user asks to create "infographic", "信息图", "visual summary", or "可视化".
Run Ralph calibration checks to analyze intention drift, technical quality, and self-improvement opportunities. Use when user asks to "ralph calibrate", "check drift", "analyze sessions", or needs to verify work alignment.
Senior Progress Analyst & Conductor Strategist. Expert in Predictive Project Tracking and Agentic Milestone Management for 2026.
Perform GO and KEGG functional enrichment using HOMER from genomic regions (BED/narrowPeak/broadPeak) or gene lists, and produce R-based barplot/dotplot visualizations. Use this skill when you want to perform GO and KEGG functional enrichment using HOMER from genomic regions or just want to link genomic region to genes.
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Analyze, filter, transform, and convert log files using Kelora. Use for parsing logs, extracting patterns, investigating incidents, calculating metrics, or converting formats.
Identify and validate profitable business opportunities by analyzing market size (TAM/SAM/SOM), unit economics, competitive landscape, and PMF indicators. Generates comprehensive HTML reports with opportunity scorecards.
Automatically fix SQL performance issues with step-by-step measurement. Rewrites problematic SQL patterns (functions on columns, implicit conversions), creates indexes, measures their impact, and rolls back ineffective indexes. Reports improvements at each step with cost reduction percentages.
Aggregate weekly summaries into a monthly overview. Use when asked to "monthly review", "review the month", "summarize this month", or "month summary".
Understand user experience with FullStory's digital experience intelligence platform.
Generate daily operation reports automatically. Use when creating EOD reports or summarizing work.
Track user behavior automatically with Heap's auto-capture analytics platform.
Generate Excalidraw diagrams. Use when the user asks to create a diagram, visualize a concept, or illustrate technical architectures.
Track product analytics and user behavior with Mixpanel's event-based platform.
Analyze product analytics with Amplitude's digital analytics platform.
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Create beautiful infographics based on the given text content. Use this when users request creating infographics.
Analyze causal impact of events on time series forecasts using TimeGPT. Use when quantifying promotion or disaster effects. Trigger with 'event impact analysis' or 'causal analysis'.
Query Prometheus and Loki billing metrics from Grafana. Use when discussing observability costs, active series, ingestion rates, storage usage, or cardinality analysis.
Provides expert Nixtla forecasting using TimeGPT, StatsForecast, and MLForecast. Generates time series forecasts, analyzes trends, compares models, performs cross-validation, and recommends best practices. Activates when user needs forecasting, time series analysis, sales prediction, demand planning, revenue forecasting, or M4 benchmarking.
PolicyEngine aggregation patterns - using adds attribute and add() function for summing variables across entities
Conduct quantitative synthesis through meta-analysis. Use when: (1) Combining effect sizes across studies, (2) Systematic review synthesis, (3) Calculating summary effects, (4) Assessing heterogeneity.
Interpret statistical results correctly and comprehensively. Use when: (1) Writing results sections, (2) Discussing findings, (3) Avoiding common misinterpretations, (4) Reporting effect sizes and confidence intervals.
Calculate and interpret effect sizes for statistical analyses. Use when: (1) Reporting research results to show practical significance, (2) Meta-analysis to combine study results, (3) Grant writing to justify expected effects, (4) Interpreting published studies beyond p-values, (5) Sample size planning for power analysis.
