osmo-lerobot-training
Submit, monitor, analyze, and evaluate LeRobot imitation learning training jobs on OSMO with Azure ML MLflow integration and inference evaluation - Brought to you by microsoft/physical-ai-toolchain
- License 许可
- MIT
- 最近更新
- 2026-11-11
- Stars
- ★ 138
- Forks
- 72
01 前言 Front Matter
- name
- alert-triage
- description
- Use for alert triage questions in the Fraud Detection and Alert Agent synthetic pilot.
02 资源文件 2
- 参考文档references/DEFAULTS.md3.8 KB
- 参考文档references/REFERENCE.md19.4 KB
03 SKILL.md 正文
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Alert triage
Prioritizes fictional alerts by transparent risk evidence and rule severity.
Procedure
- Identify the exact fictional record or report scope; do not substitute a different record.
- Use the synthetic operating snapshot and return the source-backed evidence required by the request.
- Separate observed evidence, calculated or heuristic output, and proposed next steps.
- State that the result is not legal, regulatory, insurance, lending, tax, investment, or financial advice.
- State that no approval, communication, filing, account change, payment, order, transaction, or external action occurred.
- Name the authorized human review required before action.
Locked example
Persona: Fraud Operations Manager
Prompt: Review overnight fraud activity and show me what needs immediate attention.
Expected synthetic evidence: 1,247 overnight alerts, $892K, F-78234.
04 同仓库更多 Skills
synthetic-data
Generate synthetic training data using NVIDIA Cosmos world foundation models for SDG pipelines
infrastructure
Deploy and manage Azure infrastructure for the Physical AI Toolchain including Terraform IaC, Kubernetes setup, GPU configuration, and network topology
fleet-intelligence
Monitor robot fleet telemetry via Azure IoT Operations, drift detection, Grafana dashboards, and Fabric analytics
fleet-deployment
Deploy trained robot policies to edge fleets via FluxCD GitOps, image automation, and deployment gating
environment-deployment
Generate, transfer, and consume environment-specific Azure, AKS, OSMO, ACR, and Azure ML deployment bundles. Use when: discovering deployed environment details; creating OSMO image manifests or platform values; preparing a HiL host; uploading or downloading deployment files through Azure Key Vault; or deploying with generated environment configuration.
e2e-tests
Autonomously launch, monitor, retry, and report all Physical AI cloud E2E tests across independent Azure ML and OSMO GPU infrastructure.