Data & Analytics

Analyze network structures, identify communities, measure centrality, and visualize relationships for social networks and organizational structures

Optimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt

Build and train machine learning models using scikit-learn, PyTorch, and TensorFlow for classification, regression, and clustering tasks

Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability

Discover patterns, distributions, and relationships in data through visualization, summary statistics, and hypothesis generation for exploratory data analysis, data profiling, and initial insights

Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency

Create effective visualizations using matplotlib and seaborn for exploratory analysis, presenting insights, and communicating findings with business stakeholders

Measure relationships between variables using correlation coefficients, correlation matrices, and association tests for correlation measurement, relationship analysis, and multicollinearity detection

Design and analyze A/B tests, calculate statistical significance, and determine sample sizes for conversion optimization and experiment validation

Use to attribute forecast vs actual deltas and recommend remediation

Use when designing, tuning, or auditing revenue forecast models.

Visualization toolkit for mapping partner landscape, coverage, and priorities.

Toolkit for building TAM/SAM/SOM models, sensitivity analyses, and narrative-ready

Use to design composite intent scoring models with decay, weighting,

Lifecycle frameworks for driving repeat purchase, loyalty, and subscription

Use to track enrichment provider success, cost, latency, and quality

Use when consolidating product usage, health, and sentiment signals for

Structure for capturing qualitative + quantitative win/loss insights

Use when establishing measurement frameworks, dashboards, and optimization rhythms for live campaigns.

Best-practice kit for BI dashboard layout, storytelling, and adoption.

Use when designing dashboards, reports, and narratives for GTM stakeholders.

Logs and scores skill usage quality, tracking output effectiveness, user satisfaction signals, and improvement opportunities. Expert in skill analytics, quality metrics, feedback loops, and continuous improvement. Activate on "skill logging", "skill quality", "skill analytics", "skill scoring", "skill performance", "skill metrics", "track skill usage", "skill improvement". NOT for creating skills (use agent-creator), skill documentation (use skill-coach), or runtime debugging (use debugger sk...

Interpret YouTube Analytics, TikTok Analytics, and video performance data. Identifies trends, explains metrics, and provides actionable recommendations for growth. Use when analyzing video performance, understanding metrics, or optimizing channel strategy.

Screen US stocks for high-quality dividend opportunities combining value characteristics (P/E ratio under 20, P/B ratio under 2), attractive yields (3% or higher), and consistent growth (dividend/revenue/EPS trending up over 3 years). Supports two-stage screening using FINVIZ Elite API for efficient pre-filtering followed by FMP API for detailed analysis. Use when user requests dividend stock screening, income portfolio ideas, or quality value stocks with strong fundamentals.