relevance-analytics

Query Relevance AI agent analytics by project, agent, or date range.

1|Updated Mar 12, 2026
One-click install
npx skills add https://github.com/RelevanceAI/agent-skills --skill relevance-analytics
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: relevance-analytics
Source: https://github.com/RelevanceAI/agent-skills/tree/main/reference/relevance-analytics
Command: npx skills add https://github.com/RelevanceAI/agent-skills --skill relevance-analytics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Retrieves usage analytics for Relevance AI agents and projects, enabling teams to understand adoption, performance, and workload distribution.

Core Features & Use Cases

  • Retrieve agent analytics, including most active agents and execution counts.
  • Analyze usage trends over time and identify idle or underutilized agents.
  • API-driven approach to fetch analytics for both agents and projects for planning and optimization.

Quick Start

Ask the AI to run a query for /agents/analytics with filters to return recent agent activity, then review the resulting timeseries and totals.

Frequently Asked Questions about relevance-analytics

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

FAQPage Schema
How do I retrieve agent usage analytics for my Relevance AI projects?

You retrieve agent usage analytics by querying the /agents/analytics endpoint with project, agent, or date range filters. The API returns a structured payload containing timeseries, total_change, and last_updated_at results for your agents.

What metrics are included in agent analytics timeseries data?

Agent analytics timeseries data includes usage patterns, activity metrics, execution counts, and total_change values. The structured payload also provides last_updated_at timestamps to help track agent adoption and workload distribution over time.

Can I filter agent analytics by specific projects or date ranges?

You can filter agent analytics by project, agent, or date range when querying the /agents/analytics endpoint. This allows you to isolate specific usage trends, identify idle agents, and monitor execution counts across targeted time periods.

How do I identify underutilized or idle agents using analytics?

You identify underutilized agents by analyzing usage trends and execution counts from the analytics timeseries data. Querying /agents/analytics with date range filters reveals activity metrics and total_change values to pinpoint agents with low or no recent activity.

What is the best way to monitor agent execution counts over time?

The best way to monitor agent execution counts is by querying the /agents/analytics endpoint to retrieve timeseries data. This API-driven approach returns structured activity metrics and total_change values for tracking agent usage trends and planning optimization.

Does this analytics approach require any external dependencies?

This analytics approach requires no external dependencies. It uses the relevance_api_request to call POST /agents/analytics directly, returning structured timeseries and total_change data without needing additional setup or external libraries.