Router Analytics
by 0xrdan
Generate HTML analytics dashboard for routing statistics
Skill Details
Repository Files
1 file in this skill directory
name: router-analytics description: Generate HTML analytics dashboard for routing statistics context: fork agent: general-purpose allowed-tools: Read, Write, Bash
Router Analytics Skill
Generate a visual HTML analytics dashboard from your routing statistics.
What This Does
Reads your routing statistics from ~/.claude/router-stats.json and generates an interactive HTML dashboard with:
- Route distribution pie chart
- Daily/weekly trends line chart
- Cost savings over time
- Session comparison metrics
Usage
/router-analytics
/router-analytics --output ~/Desktop/router-report.html
Generated Dashboard Includes
Summary Cards
- Total queries processed
- Total estimated savings vs always-Opus
- Route distribution (fast/standard/deep/orchestrated)
- Average confidence scores
Charts
- Pie Chart: Route distribution breakdown
- Line Chart: Daily query trends over last 30 days
- Bar Chart: Savings per session
Tables
- Recent sessions with per-session metrics
- Exception tracking (router_meta queries, slash commands)
Output
By default, generates router-analytics.html in the current directory.
Use --output <path> to specify a custom output location.
Implementation
When this skill runs:
- Read
~/.claude/router-stats.json - Parse and aggregate statistics
- Generate HTML with embedded Chart.js visualizations
- Write to output file
- Report summary to user
Requirements
The stats file must exist (run some queries through the router first).
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.