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Manage isolated working environments in OpenShell using its command-line tool. Register gateways, create and connect to sandboxes, configure credential providers, and reuse workload templates with shared images, environment settings, and computing resources.

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• Create and connect to a sandbox
• Configure and refresh provider credentials
• Inspect and import provider profiles

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

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• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Helps adapt machine learning training to multiple GPUs and machines with Ray Train. Covers resource allocation, recovery from saved training states, and Ray Tune searches that compare settings such as learning rate and batch size.

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• Adapt existing PyTorch training to multiple GPUs
• Run HuggingFace training across multiple machines
• Compare learning rates and batch sizes with Ray Tune

Define reliability targets for production services and calculate error budgets, the allowed amount of failure. Create monitoring rules, incident response runbooks, capacity plans, and automation scripts, with failure experiments to check recovery behavior.

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• Define service reliability targets and error budget policies
• Design monitoring dashboards and actionable alerts
• Prepare incident response runbooks and postmortems

Design systems made of independently deployable services, or plan how to split an existing application. The skill produces service boundary diagrams and recommendations for communication, data ownership, failure handling, monitoring, and deployment.

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• Planning how to split a monolithic application
• Choosing REST, gRPC, or event-based communication
• Defining data ownership and cross-service consistency

Create Kubernetes deployment files and Helm charts, reusable application packages. Configure permissions, network isolation, and persistent storage, then investigate pod failures and resource usage to adjust workloads and verify deployment health.

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• Create deployment YAML and Helm charts
• Configure access permissions and network isolation
• Set up persistent storage and sensitive configuration

Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.

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• Automate model training and retraining workflows
• Record and compare machine learning experiments
• Build a feature store for model input data

Design controlled failure experiments for distributed systems to examine how services recover. Produce failure injection scripts, monitoring plans, rollback procedures, and exercise guides, then turn findings into documented improvements.

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• Planning controlled failure experiments
• Testing recovery after Kubernetes Pod failures
• Checking service behavior under network delays

Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.

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• Setting up automated build and deployment pipelines
• Packaging applications with Docker
• Managing Kubernetes and cloud infrastructure as code

Analyze Axiom data ingestion and query usage to identify unused columns and field values. Build cost dashboards and spike monitors, then summarize recommendations for dropping or sampling data with estimated savings.

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• Find unused data that contributes to Axiom costs
• Track ingestion trends and projected spending
• Set up alerts for ingestion and query cost spikes

Design and build Axiom dashboards from logs or metrics, the numerical measurements of system behavior. Create charts, filters, and layouts for monitoring and reporting, or migrate existing Splunk and Grafana panels while validating queries against available data.

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• Build a service health monitoring dashboard
• Create interactive filters for investigating logs
• Migrate dashboards from Splunk or Grafana

Investigate production incidents and bugs using logs, metrics, and code evidence. The skill guides hypothesis testing, root cause analysis, fix validation, and reusable investigation notes, with access to configured observability tools such as Axiom.

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• Investigate production outages and rising error rates
• Trace causes using logs, metrics, and code
• Reproduce bugs and validate minimal fixes

Understand how OpenRig organizes a team of coding agents. This skill explains the main terms, shows how to identify an agent’s role and peers, and clarifies where skills and working context come from.

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• Getting oriented in a new OpenRig environment
• Understanding rig, pod, and seat terminology
• Checking where skills and context come from

Look up exact syntax, options, JSON output, defaults, and error meanings for a known OpenRig command. Use the reference to check command behavior and diagnose runtime differences against the installed help and running daemon.

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• Checking required flags for a known rig command
• Looking up queue commands and handoff syntax
• Investigating command errors and daemon compatibility

Assess Kubernetes security from an existing pod or API access point. Covers permission checks, exposed node services, secrets, host access, and cloud metadata to explain how configuration weaknesses can expand access across a cluster.

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• Review service account permissions and RBAC settings
• Assess host access and secrets exposure from pods
• Trace access paths from a cluster to cloud infrastructure

Assess server-side template injection, where user input is interpreted as template code. Identify template engines, evaluate information exposure and code execution risks, and organize evidence alongside detection and prevention measures.

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• Assessing template rendering endpoints
• Identifying the template engine in use
• Evaluating information exposure and code execution risks

Investigate insecure deserialization, where restoring objects from stored or transmitted data can execute unwanted code. Covers multiple languages, input format recognition, vulnerable processing points, verification methods, and documentation of affected libraries and entry points.

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• Reviewing code that restores objects from external data
• Identifying serialized data in cookies and request bodies
• Assessing message consumers and admission webhooks

Assess AWS, Azure, and GCP environments by identifying identities, permissions, and paths to broader access or data exposure. Covers cloud storage, secrets, cross-account trust, and CI/CD, the automated systems used to build and deploy software.

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• Planning an AWS, Azure, or GCP security assessment
• Tracing privilege escalation from current permissions
• Reviewing CI/CD credentials and OIDC trust settings

Assess web applications and APIs for SQL injection, where input changes database queries. The skill covers finding vulnerable inputs, testing different database systems, automating checks, and documenting findings with remediation suggestions.

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• Assessing web application inputs for SQL injection
• Reviewing GraphQL and WebSocket injection risks
• Combining SQLmap automation with manual verification

Investigate TOCTOU flaws: race conditions where a checked property changes before an action uses it. The skill covers filesystem, kernel, web, and container testing, helping identify vulnerable sequences, reproduce timing issues, and document observed impact.

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• Reviewing file checks before privileged operations
• Testing access revocation during long-running requests
• Investigating duplicate payments from concurrent requests

Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.

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• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

Configure how Dynamo routes requests to workers, then check model discovery and a chat response. Supports local and Kubernetes setups, including routing based on cached model data, with clear commands, results, and limitations.

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• Start a local Dynamo frontend
• Change routing settings on Kubernetes
• Check model discovery and a chat response

Set up and operate NVIDIA AI-Q Blueprint locally or on your own infrastructure. Choose a deployment mode, configure credentials, check service health, troubleshoot failures, and provide a verified server address for aiq-research.

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• Starting a backend for aiq-research
• Running AI-Q through a terminal or browser
• Deploying AI-Q to a Kubernetes cluster

Check whether a Dynamo deployment is ready for disaggregated serving, where input processing and token generation run separately. Read-only probes inspect transport settings, GPU networking capabilities, and NIXL transfer-test readiness, then report gaps and next steps.

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• Check communication readiness after deployment
• Inspect transport setup before benchmarking
• Investigate suspected communication bottlenecks