Ai Evals

Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.

Full Sentry SDK setup for Flutter and Dart. Use when asked to "add Sentry to Flutter", "install sentry_flutter", "setup Sentry in Dart", or configure error monitoring, tracing, profiling, session replay, or logging for Flutter applications. Supports Android, iOS, macOS, Linux, Windows, and Web.

Reference for netlify.toml configuration and site environment variables. Use when configuring build settings, redirects, rewrites, headers, deploy contexts, the `[dev]` block that controls `netlify dev` (command, port, targetPort, framework), or any site-level configuration — and when managing environment variables or secrets with the Netlify CLI, including scoping values to specific deploy contexts. Covers the complete netlify.toml syntax including redirects with splats/conditions, headers, deploy contexts, functions config, edge functions config, and the `[dev]` block (including when `framework` must be `"#custom"` — required when both a custom `command` and `targetPort` are set).

Full Sentry SDK setup for Svelte and SvelteKit. Use when asked to "add Sentry to Svelte", "add Sentry to SvelteKit", "install @sentry/sveltekit", or configure error monitoring, tracing, session replay, or logging for Svelte or SvelteKit applications.

Core DQL syntax, pitfalls, query patterns, and query optimization. Load to write, build, fix, or OPTIMIZE a DQL query — prevents syntax errors and makes queries faster, more efficient, and cheaper (less data scanned = lower query consumption/cost per run). Covers fetch commands, data models, field namespaces, time alignment, entity/smartscape patterns, metric discovery, and performance/cost optimization (filter early, bucket filters, short time ranges, field selection, sampling, cardinality). Trigger: "write/build/fix a DQL query", "DQL syntax", "query logs/spans/metrics", "create a timeseries", "optimize my DQL", "make my query faster/cheaper", "reduce DQL cost/consumption/scanned data", "keep DQL cost under control". Do NOT use to explain an existing query or answer product questions. For MONITORING a tenant's ACTUAL query consumption/billing (how much queries cost, who scanned most, cost trends) use dt-platform-costs — this tunes the query text, not billing data.

Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.