Use this skill when choosing or configuring NeMo Relay 0.6 or 0.7 observability through the built-in plugin, subscribers, or exporters, including raw ATOF events, ATIF trajectories, OpenTelemetry, OpenInference, or custom event handling.
npx skills add https://github.com/NVIDIA/skills --skill nemo-relay-plugin-observability
Start with one exporter managed by the built-in Observability plugin. This is
the default for reusable process configuration and the best first plugin for
most users because it makes Relay's captured activity visible.
Choose one proof output before layering additional telemetry destinations.
Use manual subscriber or exporter APIs only when a test, script, or application
needs direct control over registration names, collection windows, or flush
timing. Both paths consume the same canonical event stream.
Determine whether the application uses NeMo Relay 0.6 or 0.7 before proposing
configuration or binding APIs. Prefer the installed package version, lockfile,
or manifest. Ask the user when the version cannot be established; do not mix
the two surfaces in one example.
OpenInference are separate exporters with OpenTelemetryConfig /
OpenTelemetrySubscriber and OpenInferenceConfig /
OpenInferenceSubscriber.
exporter provides full, gen_ai, and openinference projections.
Select the output that best matches the user's immediate inspection target:
Use a manual subscriber for short-lived in-process inspection.
Use ATOF JSONL; read references/atof.md.
Use ATIF; read references/atif.md.
For 0.6, choose the separate OpenTelemetry or OpenInference exporter. For
0.7, choose a typed OpenTelemetry endpoint (full, gen_ai, or
openinference). Read references/opentelemetry.md and, for an
OpenInference-aware backend, references/openinference.md.
Choose one output first and verify it before adding another. ATOF is the
default local proof because it preserves the raw event stream with the least
translation. Use synthetic, non-sensitive payloads for the first proof. Add and
verify sanitization before exporters receive production payloads, and never
display complete event records while validating an exporter.
Use this model when explaining how capture and export relate:
calls, managed LLM calls, middleware, and manual lifecycle APIs.
subscribers can observe the same stream for logging, export, analytics, or
diagnostics.
scope closes.
components.
into ATIF or the version-appropriate OpenTelemetry/OpenInference form.
use managed helpers or manual lifecycle params provide those fields.
compaction mark refreshes it.starts retain system instructions, the latest user message, and every
following assistant or tool message.
event-only projection to provider-shaped event input without changing
provider execution.
data field,typed profile data such as model_name and tool_call_id, and codec-provided
annotated LLM request/response data for in-process subscribers and exporters.
SKILL.mdautomatically emit skill.load marks under the tool span. The payload
contains only skill_name; metadata records the load source and tool name.
Partial reads do not count, and ambiguous slash-command expansions use the
separate skill.load.inferred name. The eager mark remains present if tool
execution later fails.
Use the names exported by the selected language binding and Relay version:
nemo_relay.subscribers.register(...), AtofExporter,AtifExporter, OpenTelemetrySubscriber, and OpenInferenceSubscriber
OpenTelemetrySubscriber for all three typed projections
nemo_relay::api::subscriber and nemo_relay::observability::*lifecycle methods
Load only the reference required by the selected output:
references/atof.md for raw JSONL events used in local debugging oroffline inspection.
references/atif.md for ATIF trajectories.references/opentelemetry.md for OTLP/OpenTelemetry traces.references/openinference.md for the standalone 0.6 OpenInferenceexporter or the 0.7 openinference OpenTelemetry projection.
Choose another skill when the task belongs to an adjacent workflow:
nemo-relay-plugin-build to package subscriber-based export behavior asa reusable plugin.
nemo-relay-get-started or nemo-relay-instrument-calls when no scope,tool call, or LLM call has been instrumented.
nemo-relay-debug-runtime-integration to diagnose missing telemetry.Use these skills for adjacent workflows:
nemo-relay-instrument-calls.nemo-relay-instrument-typed-wrappers.nemo-relay-plugin-build.nemo-relay-debug-runtime-integration.Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take nvidia/nemo-relay-plugin-observability from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.