fleet-intelligence
Skill138 starsChanged 14 days agoMITView on GitHub
Monitor robot fleet telemetry via Azure IoT Operations, drift detection, Grafana dashboards, and Fabric analytics
In this repository10 other files it gives its agents.
- .github/copilot-instructions.mdCopilot instructions
- azureml-k3s-compute-target-setupSkill
- dataviewerSkill
- e2e-infrastructureSkill
- e2e-testsSkill
- environment-deploymentSkill
- fleet-deploymentSkill
- infrastructureSkill
- osmo-lerobot-trainingSkill
- synthetic-dataSkill
The file
Files
Fleet Intelligence
Monitor and analyze deployed robot fleet telemetry — IoT Operations data collection, Grafana dashboards, drift detection, and Fabric Real-Time Intelligence analytics.
Prerequisites
| Requirement | Purpose |
| Azure IoT Operations | Edge telemetry collection |
| Azure Event Hubs | Cloud telemetry ingestion |
| Grafana | Fleet dashboards and alerting |
| Microsoft Fabric | Real-Time Intelligence KQL analytics |
az CLI | Azure authentication |
kubectl | Cluster access for IoT Operations |
Setup Workflow
Deploy fleet intelligence components in order:
Step 1 — Deploy IoT Operations
fleet-intelligence/setup/deploy-iot-operations.shStep 2 — Deploy telemetry pipeline
fleet-intelligence/setup/deploy-telemetry-pipeline.shStep 3 — Deploy dashboards
fleet-intelligence/setup/deploy-dashboards.shStep 4 — Deploy Fabric Real-Time Intelligence
fleet-intelligence/setup/deploy-fabric-rti.shTelemetry Schemas
| Schema | Path | Description |
| Policy Execution | telemetry/schemas/policy-execution.schema.json | Inference metrics and action outputs |
| Robot Health | telemetry/schemas/robot-health.schema.json | Hardware status and connectivity |
Drift Detection
Drift detection monitors deployed policies for performance degradation. When drift exceeds configured thresholds, retraining triggers initiate automated training pipelines.
| Component | Path | Description |
| Detection | drift/detection/ | Statistical tests against training baselines |
| Alerting | drift/alerting/ | Threshold evaluation and notification routing |
| Triggers | drift/triggers/ | Automated retraining pipeline launching |
Key Files
| File | Description |
fleet-intelligence/README.md | Domain overview |
fleet-intelligence/telemetry/routing/edge-to-eventhub.yaml | Edge-to-cloud routing config |
fleet-intelligence/dashboards/grafana/fleet-overview.json | Grafana dashboard definition |
fleet-intelligence/dashboards/fabric/fleet-kql-queries.kql | Fabric KQL analytics queries |
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.