A sophisticated diagnostic skill for executing fleet-wide health audits and deep-dive telemetry analysis of Azure PostgreSQL Flexible Servers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install amg-check-pg-flex
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install amg-check-pg-flex using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AMG PostgreSQL Flexible Server Health Check?
The AMG PostgreSQL Flexible Server Health Check skill provides a comprehensive monitoring and diagnostic framework for Azure-based database clusters. By integrating directly with Azure Managed Grafana and Azure Monitor, it enables developers to perform automated, multi-tier investigations into server performance. It excels at identifying resource bottlenecks, connection issues, and abnormal log patterns across an entire subscription or specific resource group.
This tool is designed to move from high-level fleet triage to granular root-cause analysis. It automatically handles data discovery, metric correlation, and persistent reporting, ensuring that transient issues are captured and documented. Built for the Openclaw Skills ecosystem, it leverages MCP tools to provide a professional-grade observability experience directly within an AI agent workflow.
AMG PostgreSQL Flexible Server Health Check Use Cases
- Routine fleet-wide health audits to identify servers nearing resource limits before they fail.
- Troubleshooting performance degradation or 'Ready' state issues in specific PostgreSQL instances.
- Correlating resource spikes with activity logs to identify problematic SQL queries or connection storms.
- Maintaining a persistent record of known issues and bug statuses across multiple monitoring sessions.
How AMG PostgreSQL Flexible Server Health Check Works
- Validates the Azure Monitor datasource UID through the amgmcp_datasource_list tool to ensure connectivity.
- Discovers all PostgreSQL Flexible Servers within the targeted subscription using Azure Resource Graph queries.
- Conducts a Tier 1 Pulse Check to scan CPU, memory, storage, and I/O metrics against defined severity thresholds.
- Initiates a Tier 2 Deep Dive for any identified abnormal servers, pulling granular metrics and KQL-based resource logs.
- Performs correlation analysis to identify root causes like replication delays or long-running transactions.
- Generates a formatted report and updates a persistent tracking file to document resolved and active issues.
AMG PostgreSQL Flexible Server Health Check Setup
Ensure you have an MCP server registered under the name amg in your workspace. Use the following command to configure the connection to your Azure Managed Grafana instance:
openclaw mcp set amg '{"url":"https://<your-grafana-instance>/api/azure-mcp","transport":"streamable-http","headers":{"Authorization":"Bearer <your-token>"}}'
After registration, verify the setup with:
openclaw mcp list
On the first run, the skill will automatically prompt for your Subscription ID and discover your Datasource UID to populate its configuration.
AMG PostgreSQL Flexible Server Health Check Data Schema & Taxonomy
The skill manages its state and configuration through a dedicated directory structure in the workspace memory:
| File | Purpose |
|---|---|
config.md |
Stores the Azure Monitor Datasource UID and the target Subscription ID. |
report.md |
A persistent ledger of discovered bugs, resource inventory, and historical telemetry findings. |
reference/ |
Contains KQL templates, analysis patterns, and output formatting rules used during the audit. |
Metrics are categorized by severity: CRITICAL (e.g., CPU >90%, Storage >85%) and WARNING (e.g., Connection failures, deadlocks, or replication delays).
AMG PostgreSQL Flexible Server Health Check Advanced Features
- Persistent Bug Tracking: Maintains a historical report to track if issues are improving, worsening, or resolved over time.
- Intelligent Batching: Implements a parallelism cap of 30 concurrent calls to prevent Azure Monitor rate-limiting.
- Automated Deep-Dive: Automatically triggers secondary metric queries and KQL log analysis only for servers flagged during the initial pulse check.
- Large Result Handling: Utilizes local execution (Node.js or Python) to process large JSON payloads outside the LLM context window.
SKILL.md
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