Agent Skills

otel-semantic-conventions

OpenTelemetry Semantic Conventions expert. Use when selecting, applying, or reviewing telemetry attributes. Triggers on tasks involving attribute selection, semantic convention compliance, attribute migration, or custom attribute decisions. Covers the attribute registry, naming patterns, attribute placement, and versioning. For span names, span kinds, and span status codes, see the otel-instrumentation skill.

Install

npx skills add https://github.com/dash0hq/agent-skills --skill otel-semantic-conventions
SKILL.md

OpenTelemetry Semantic Conventions

This skill governs correct selection, placement, and validation of telemetry attributes and metric instruments according to the OpenTelemetry Semantic Conventions specification. For span naming, span kinds, and span status codes, see the otel-instrumentation skill.

The Attribute Registry is the single source of truth for all defined attributes.

Rules

Rule Description Use Case
attributes Attribute registry, selection, placement, common attributes by domain Choosing or reviewing attributes; HTTP/DB/messaging/RPC attributes; attribute placement (resource vs span)
versioning Semconv versioning, stability, migration Semconv version migration
dash0 Dash0 derived attributes and feature dependencies Dash0 derived attributes

Official documentation

How to select the right attribute

  1. Search the registry first — Look up the concept in the Attribute Registry. Use the standard name if it exists (e.g., prefer http.request.method over a custom custom.http.verb). Custom names fragment querying and break tooling — only create a custom attribute when no registry entry covers the concept.
  2. Check stability — Prefer stable attributes; note any experimental attributes that may change. See versioning.
  3. Place at the correct level — Resource attributes describe the entity producing telemetry; span/log attributes describe the individual operation. Do not duplicate across levels. Once an attribute is at a given level, keep it there consistently across all services.
  4. Verify cardinality — Metric attribute values must be low-cardinality (bounded set). Variable data (user IDs, request paths with parameters) belongs in span attributes, not metric attributes.
  5. Custom attribute as last resort — Only create a custom attribute if no registry entry covers the concept. Document the decision and follow the org.namespace.attribute_name naming pattern.

Example: correct vs incorrect attribute selection

# Correct — uses registry attribute for HTTP method
span.set_attribute("http.request.method", "GET")

# Incorrect — invents a custom attribute for a concept already in the registry
span.set_attribute("custom.http.verb", "GET")

Example: resource vs span attribute placement

# Correct — service identity is a resource attribute
resource = Resource({"service.name": "checkout-service", "service.version": "2.1.0"})

# Correct — operation-specific data is a span attribute
span.set_attribute("http.request.method", "POST")
span.set_attribute("http.response.status_code", 201)

# Incorrect — placing a resource-level attribute on every span
span.set_attribute("service.name", "checkout-service")  # belongs on the resource

Example: cardinality violation in metric attributes

# Correct — metric attribute uses a bounded, low-cardinality value
histogram.record(duration_ms, {"http.request.method": "GET", "http.response.status_code": 200})

# Incorrect — unbounded values as metric attributes explode storage and query cost
histogram.record(duration_ms, {"user.id": "u-839201", "url.path": "/orders/839201"})
# Fix: move high-cardinality values to span attributes instead
span.set_attribute("user.id", "u-839201")
span.set_attribute("url.path", "/orders/839201")

Related skills

azure-diagnosticsmicrosoft608KDebug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, troubleshoot functions, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, aazure-preparemicrosoft608KPrepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azazure-aimicrosoft608KUse for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.azure-deploymicrosoft607KExecute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: \"run azd up\", \"run azd deploy\", \"execute deployment\",

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers