Initial commit

.claude-plugin/plugin.json

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+{
+  "name": "metrics-aggregator",
+  "description": "Aggregate and centralize performance metrics",
+  "version": "1.0.0",
+  "author": {
+    "name": "Claude Code Plugins",
+    "email": "[email protected]"
+  },
+  "skills": [
+    "./skills"
+  ],
+  "commands": [
+    "./commands"
+  ]
+}

README.md

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+# metrics-aggregator
+
+Aggregate and centralize performance metrics

commands/aggregate-metrics.md

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+---
+description: Aggregate performance metrics centrally
+---
+
+# Metrics Aggregator
+
+Implement centralized metrics aggregation for comprehensive performance visibility.
+
+## Metric Categories
+
+1. **Application Metrics**: Custom business and performance metrics
+2. **System Metrics**: CPU, memory, disk, network
+3. **Database Metrics**: Query performance, connections
+4. **Cache Metrics**: Hit rates, memory usage
+5. **Queue Metrics**: Message rates, processing times
+6. **External Service Metrics**: Third-party API performance
+
+## Process
+
+1. Design metrics taxonomy and naming convention
+2. Choose metrics platform (Prometheus, StatsD, CloudWatch, etc.)
+3. Implement metric instrumentation
+4. Configure aggregation and retention
+5. Create visualization dashboards
+6. Set up alerting on key metrics
+
+## Output
+
+Provide:
+- Metrics instrumentation code
+- Metric naming and tagging standards
+- Collection configuration (Prometheus, etc.)
+- Aggregation rules and retention policies
+- Dashboard configurations (Grafana, etc.)
+- Alert definitions for critical metrics

plugin.lock.json

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+    "repoRoot": "/Users/zhongweili/projects/openmind/42plugin-data"
+  },
+  "manifest": {
+    "name": "metrics-aggregator",
+    "description": "Aggregate and centralize performance metrics",
+    "version": "1.0.0"
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+}

skills/metrics-aggregator/SKILL.md

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+---
+name: aggregating-performance-metrics
+description: |
+  This skill enables Claude to aggregate and centralize performance metrics from various sources. It is used when the user needs to consolidate metrics from applications, systems, databases, caches, queues, and external services into a central location for monitoring and analysis. The skill is triggered by requests to "aggregate metrics", "centralize performance metrics", or similar phrases related to metrics aggregation and monitoring. It facilitates designing a metrics taxonomy, choosing appropriate aggregation tools, and setting up dashboards and alerts.
+allowed-tools: Read, Write, Bash, Grep
+version: 1.0.0
+---
+
+## Overview
+
+This skill empowers Claude to streamline performance monitoring by aggregating metrics from diverse systems into a unified view. It simplifies the process of collecting, centralizing, and analyzing performance data, leading to improved insights and faster issue resolution.
+
+## How It Works
+
+1. **Metrics Taxonomy Design**: Claude assists in defining a clear and consistent naming convention for metrics across all systems.
+2. **Aggregation Tool Selection**: Claude helps select the appropriate metrics aggregation tool (e.g., Prometheus, StatsD, CloudWatch) based on the user's environment and requirements.
+3. **Configuration and Integration**: Claude guides the configuration of the chosen aggregation tool and its integration with various data sources.
+4. **Dashboard and Alert Setup**: Claude helps set up dashboards for visualizing metrics and defining alerts for critical performance indicators.
+
+## When to Use This Skill
+
+This skill activates when you need to:
+- Centralize performance metrics from multiple applications and systems.
+- Design a consistent metrics naming convention.
+- Choose the right metrics aggregation tool for your needs.
+- Set up dashboards and alerts for performance monitoring.
+
+## Examples
+
+### Example 1: Centralizing Application and System Metrics
+
+User request: "Aggregate application and system metrics into Prometheus."
+
+The skill will:
+1. Guide the user in defining metrics for applications (e.g., request latency, error rates) and systems (e.g., CPU usage, memory utilization).
+2. Help configure Prometheus to scrape metrics from the application and system endpoints.
+
+### Example 2: Setting Up Alerts for Database Performance
+
+User request: "Centralize database metrics and set up alerts for slow queries."
+
+The skill will:
+1. Help the user define metrics for database performance (e.g., query execution time, connection pool usage).
+2. Guide the user in configuring the aggregation tool to collect these metrics from the database.
+3. Assist in setting up alerts in the aggregation tool to notify the user when query execution time exceeds a defined threshold.
+
+## Best Practices
+
+- **Naming Conventions**: Use a consistent and well-defined naming convention for all metrics to ensure clarity and ease of analysis.
+- **Granularity**: Choose an appropriate level of granularity for metrics to balance detail and storage requirements.
+- **Retention Policies**: Define retention policies for metrics to manage storage space and ensure data is available for historical analysis.
+
+## Integration
+
+This skill integrates with other Claude Code plugins that manage infrastructure, deploy applications, and monitor system health. For example, it can be used in conjunction with a deployment plugin to automatically configure metrics collection after a new application deployment.

skills/metrics-aggregator/assets/README.md

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+# Assets
+
+Bundled resources for metrics-aggregator skill
+
+- [ ] grafana_dashboard_template.json: A template for creating Grafana dashboards with pre-configured panels and visualizations for common metrics.
+- [ ] datadog_dashboard_template.json: A template for creating Datadog dashboards with pre-configured panels and visualizations for common metrics.
+- [ ] splunk_dashboard_template.xml: A template for creating Splunk dashboards with pre-configured panels and visualizations for common metrics.
+- [ ] example_metrics_data.json: Example JSON data representing collected metrics from various sources.
+- [ ] metrics_schema.json: A JSON schema defining the structure and data types of collected metrics.

skills/metrics-aggregator/references/README.md

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+# References
+
+Bundled resources for metrics-aggregator skill
+
+- [ ] metrics_naming_standards.md: Defines the naming conventions for metrics to ensure consistency and clarity across different systems and applications.
+- [ ] metrics_aggregation_configuration.md: Provides detailed configuration options for metrics aggregation, including time intervals, dimensions, and aggregation functions.
+- [ ] prometheus_integration.md: Documents the steps to integrate with Prometheus, including configuring exporters and setting up data sources.
+- [ ] statsd_integration.md: Documents the steps to integrate with StatsD, including configuring clients and setting up data sources.
+- [ ] cloudwatch_integration.md: Documents the steps to integrate with CloudWatch, including configuring agents and setting up data sources.
+- [ ] grafana_dashboard_setup.md: Provides instructions on setting up Grafana dashboards to visualize aggregated metrics.
+- [ ] datadog_dashboard_setup.md: Provides instructions on setting up Datadog dashboards to visualize aggregated metrics.
+- [ ] splunk_dashboard_setup.md: Provides instructions on setting up Splunk dashboards to visualize aggregated metrics.
+- [ ] alert_definitions.md: Defines the process for creating and managing alerts based on aggregated metrics, including thresholds, notification channels, and escalation policies.

skills/metrics-aggregator/scripts/README.md

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+# Scripts
+
+Bundled resources for metrics-aggregator skill
+
+- [ ] metrics_collection.py: Automates the collection of metrics from various sources (Prometheus, StatsD, CloudWatch) using their respective APIs or SDKs.
+- [ ] metrics_validation.py: Validates collected metrics against predefined schemas or thresholds to ensure data quality and consistency.
+- [ ] metrics_aggregation.py: Aggregates metrics based on specified time intervals (e.g., hourly, daily, weekly) and dimensions (e.g., application, system, region).
+- [ ] metrics_export.py: Exports aggregated metrics to various monitoring and analysis platforms (e.g., Grafana, Datadog, Splunk) using their respective APIs or SDKs.