Cursor Usage Analytics
by jeremylongshore
|
Skill Details
Repository Files
11 files in this skill directory
name: "cursor-usage-analytics" description: | Track and analyze Cursor usage metrics. Triggers on "cursor analytics", "cursor usage", "cursor metrics", "cursor reporting", "cursor dashboard". Use when working with cursor usage analytics functionality. Trigger with phrases like "cursor usage analytics", "cursor analytics", "cursor". allowed-tools: "Read, Write, Edit, Bash(cmd:*)" version: 1.0.0 license: MIT author: "Jeremy Longshore jeremy@intentsolutions.io"
Cursor Usage Analytics
Overview
This skill helps you track and analyze Cursor usage metrics. It covers available analytics for Business/Enterprise plans, dashboard views, custom reports, API access, and strategies for optimizing team productivity based on usage data.
Prerequisites
- Cursor Business or Enterprise subscription
- Admin access to organization dashboard
- Understanding of key metrics
- Access to team for feedback
Instructions
- Access Admin Dashboard > Analytics
- Review key metrics (users, completions, costs)
- Identify trends and anomalies
- Create custom reports for stakeholders
- Set up scheduled report delivery
- Plan actions based on insights
Output
- Usage metrics and trends
- Team productivity insights
- Cost analysis and optimization
- Custom reports for stakeholders
- Data-driven improvement plans
Error Handling
See {baseDir}/references/errors.md for comprehensive error handling.
Examples
See {baseDir}/references/examples.md for detailed examples.
Resources
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
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
Pydeseq2
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.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
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.
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.
