Skills Overview¶

The OpenTelemetry Observability Agent Skills package includes 5 specialized skills that cover all aspects of OpenTelemetry implementation.

Available Skills¶

Instrumentation

SDK setup, spans, metrics, logs, and comprehensive PII protection patterns

Covers: Auto-instrumentation, custom spans, metrics, error handling, security
Collector

Pipeline configuration, processors, deployment, and production optimization

Covers: Configuration, processors, deployment, performance, monitoring
OTTL

Complete OpenTelemetry Transformation Language reference and patterns

Covers: Data transformation, filtering, enrichment, security, performance
Semantic Conventions

Attribute standards, naming patterns, and consistency guidelines

Covers: Attributes, naming, versioning, migration, compliance
Dynatrace

DQL queries, dashboard creation, SLOs, workflows, and automation

Covers: DQL, dashboards, SLOs, workflows, dtctl CLI, automation

How Skills Work¶

Each skill provides:

📋 Rule-Based Guidance¶

Structured rules covering specific aspects of OpenTelemetry implementation, with clear examples and anti-patterns.

🔒 Security-First Approach¶

Every skill includes security considerations, PII protection patterns, and compliance guidelines.

🚀 Production-Ready Patterns¶

All patterns are tested in production environments and optimized for scale and reliability.

🎯 Framework-Specific Guidance¶

Tailored advice for popular frameworks and languages, with technology-specific best practices.

Skill Integration¶

Natural Language Prompts¶

Skills work best with natural language requests:

"Add OpenTelemetry tracing to my Express.js API with proper error handling and PII protection"

"Create an OpenTelemetry collector config for Kubernetes that exports to Dynatrace"

"Write OTTL rules to redact sensitive data from HTTP headers"

AI Assistant Compatibility¶

Skills are designed to work with any AI assistant that supports:

Context injection for background knowledge
Rule-based guidance for structured advice
Example patterns for code generation
Best practices for quality assurance

Technology Coverage¶

Technology	Instrumentation	Collector	OTTL	Semantic Conventions	Dynatrace
Node.js	✅	✅	✅	✅	✅
Python	✅	✅	✅	✅	✅
Go	✅	✅	✅	✅	✅
Java	✅	✅	✅	✅	✅
.NET	✅	✅	✅	✅	✅
Kubernetes	✅	✅	✅	✅	✅
Docker	✅	✅	✅	✅	✅

Using Skills Effectively¶

1. Be Specific¶

Instead of "add observability," request specific implementations:

"Add OpenTelemetry instrumentation to this Express.js endpoint with span attributes following semantic conventions and PII protection for user data"

2. Include Context¶

Provide information about your environment:

"For a Python Django application deployed on Kubernetes with Dynatrace backend..."

3. Reference Skills Directly¶

Many assistants support skill references:

"@otel-instrumentation help me add tracing"
"Using the otel-collector skill, generate a config for..."

4. Security First¶

Always include security considerations:

"Ensure PII protection and follow security best practices"

Skill Combinations¶

Skills work together for comprehensive solutions:

Full Stack Implementation¶

1. Use otel-instrumentation for application tracing
2. Use otel-collector for data pipeline
3. Use otel-ottl for data transformation
4. Use otel-dynatrace for monitoring setup

Security-Focused Implementation¶

1. otel-instrumentation: Secure SDK setup
2. otel-ottl: PII redaction rules
3. otel-semantic-conventions: Safe attribute patterns

Performance-Optimized Implementation¶

1. otel-collector: Optimized pipeline config
2. otel-ottl: Efficient transformations
3. otel-dynatrace: Performance monitoring

Next Steps¶

📖 Read individual skill documentation for detailed guidance
🧪 Try the examples to see skills in action
🎯 Choose your AI assistant setup for optimal integration
🚀 Start building with natural language prompts

Each skill page includes comprehensive documentation with examples, best practices, and integration guidance specific to that area of OpenTelemetry.