configure-trace-metrics

Name: configure-trace-metrics
Availability: InStock
Author: Coval

Configure trace-based metrics and LLM judges from OpenTelemetry spans.

2|Updated Feb 17, 2026

Search Tags:#metrics #observability #tracing #opentelemetry #llm-evaluation #coval

Ready to run

npx skills add https://github.com/coval-ai/coval-external-skills --skill configure-trace-metrics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: configure-trace-metrics
Source: https://github.com/coval-ai/coval-external-skills/tree/main/skills/traces/configure-trace-metrics
Command: npx skills add https://github.com/coval-ai/coval-external-skills --skill configure-trace-metrics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the challenge of turning raw, noisy trace data into meaningful business and operational insights, preventing the common issue of having observability data that provides no clear decision-making value.

Core Features & Use Cases

Custom Trace Metrics: Create numeric metrics from span attributes to track latency, token usage, and business-specific workflow signals.
Trace-Aware LLM Judges: Deploy LLM-based evaluation metrics that utilize both transcript and OTel trace context to verify tool grounding and recovery behavior.
Operational Monitoring: Automatically pair new instrumentation with high-signal metrics like P90 latency, tool failure rates, and workflow completion rates.

Quick Start

Use the configure-trace-metrics skill to inventory existing spans and create custom P90 latency and LLM judge metrics for the current agent.

Frequently Asked Questions about configure-trace-metrics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I turn OpenTelemetry spans into custom metrics for monitoring agent performance?▼

You can turn OpenTelemetry spans into custom trace metrics by configuring numeric metrics from span attributes to track latency, token usage, and workflow signals. This skill sets up those metric definitions for production environments.

Can I use LLM evaluation judges with OpenTelemetry trace context?▼

Yes, you can deploy trace-aware LLM judges that utilize both transcript and OpenTelemetry trace context. These LLM evaluation metrics verify tool grounding and recovery behavior within agent workflows.

What high-signal metrics can I create from trace data for operational monitoring?▼

You can create high-signal operational metrics like P90 latency, tool failure rates, and workflow completion rates from trace data. This automatically pairs new instrumentation with actionable performance monitoring.

Do I need Coval CLI to configure trace metrics from OpenTelemetry data?▼

Yes, you need Coval CLI access and existing OpenTelemetry trace data to validate metric definitions and aggregation methods. This environment is required to properly configure and test the trace-based metrics.

What is the best way to evaluate tool usage and latency in production agents?▼

The best way to evaluate tool usage and latency is by configuring custom trace-based metrics from OpenTelemetry spans. This approach transforms raw, noisy trace data into meaningful business and operational insights.

Why should I not rely on raw trace data for evaluating workflow health?▼

Relying on raw trace data often provides no clear decision-making value because the data is noisy. Configuring trace-aware metrics and LLM judges is needed to extract meaningful operational and business signals from spans.