Creating Apm Dashboards
by jeremylongshore
|
Skill Details
Repository Files
1 file in this skill directory
name: Creating APM Dashboards description: | This skill enables Claude to create Application Performance Monitoring (APM) dashboards. It is triggered when the user requests the creation of a new APM dashboard, monitoring dashboard, or a dashboard for application performance. The skill helps define key metrics and visualizations for monitoring application health, performance, and user experience across multiple platforms like Grafana and Datadog. Use this skill when the user needs assistance setting up a new monitoring solution or expanding an existing one. The plugin supports the creation of dashboards focusing on golden signals, request metrics, resource utilization, database metrics, cache metrics, business metrics, and error tracking.
Overview
This skill automates the creation of Application Performance Monitoring (APM) dashboards, providing a structured approach to visualizing critical application metrics. By defining key performance indicators and generating dashboard configurations, this skill simplifies the process of monitoring application health and performance.
How It Works
- Identify Requirements: Determine the specific metrics and visualizations needed for the APM dashboard based on the user's request.
- Define Dashboard Components: Select relevant components such as golden signals (latency, traffic, errors, saturation), request metrics, resource utilization, database metrics, cache metrics, business metrics, and error tracking.
- Generate Configuration: Create the dashboard configuration file based on the selected components and user preferences.
- Deploy Dashboard: Deploy the generated configuration to the target monitoring platform (e.g., Grafana, Datadog).
When to Use This Skill
This skill activates when you need to:
- Create a new APM dashboard for an application.
- Define key metrics and visualizations for monitoring application performance.
- Generate dashboard configurations for Grafana, Datadog, or other monitoring platforms.
Examples
Example 1: Creating a Grafana Dashboard
User request: "Create a Grafana dashboard for monitoring my web application's performance."
The skill will:
- Identify the need for a Grafana dashboard focused on web application performance.
- Define dashboard components including request rate, response times, error rates, and resource utilization (CPU, memory).
- Generate a Grafana dashboard configuration file with pre-defined visualizations for these metrics.
Example 2: Setting up a Datadog Dashboard
User request: "Set up a Datadog dashboard to track the golden signals for my microservice."
The skill will:
- Identify the need for a Datadog dashboard focused on golden signals.
- Define dashboard components including latency, traffic, errors, and saturation metrics.
- Generate a Datadog dashboard configuration file with pre-defined visualizations for these metrics.
Best Practices
- Specificity: Provide detailed information about the application and metrics to be monitored.
- Platform Selection: Clearly specify the target monitoring platform (Grafana, Datadog, etc.) to ensure compatibility.
- Iteration: Review and refine the generated dashboard configuration to meet specific monitoring needs.
Integration
This skill can be integrated with other plugins that manage infrastructure or application deployment to automatically create APM dashboards as part of the deployment process. It can also work with alerting plugins to define alert rules based on the metrics displayed in the generated dashboards.
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.
