What does Agent Evaluation Reporting do?

It converts raw agent evaluation runs into clear, decision-ready reports that explicitly document outcome populations, denominators, latency distributions, and experiment conditions, ensuring every headline number can be reproduced without hiding failures or overstating capability.

When should I use Agent Evaluation Reporting?

Use it when you need to communicate agent performance results to stakeholders who require visibility into success rates, failure modes, and experimental conditions, especially when reproducibility matters and teams need to verify claims or make go/no-go decisions based on evaluation data.

When should I NOT use this skill?

Do not use it when you need real-time monitoring dashboards or when stakeholders prefer high-level summaries without underlying population data.

What inputs does this skill require?

You provide raw agent evaluation run data including test outcomes, performance metrics, latency measurements, and the conditions under which experiments were conducted.

Who is this skill designed for?

It serves AI engineering teams, product managers, and technical decision-makers who evaluate agent systems and need to communicate results while maintaining auditability of every reported number.

Skill

Generate decision-ready reports from agent evaluation runs

Name: Agent Evaluation Reporting
Availability: OnlineOnly
Author: Whxuan0701

Transforms raw agent evaluation runs into decision-ready reports with explicit outcome populations, denominators, latency data, and experiment conditions

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Spark score

out of 100

Updated 3 days ago

Source checked Sep 17, 2026

Version 17.4.0

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Why it matters

Transform raw agent evaluation data into transparent, reproducible reports that surface both successes and failures with explicit outcome populations, denominators, latency metrics, and experiment conditions so stakeholders can validate every claim.

Outcomes

What it gets done

Extract outcome populations and denominators from evaluation runs

Calculate and report latency distributions across test populations

Document experiment conditions and parameters for reproducibility

Surface failures and capability limits without overstating performance

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-agent-evaluation-reporting | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

Reports

Agent outcome reports

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Overview

Agent Evaluation Reporting

Agent Evaluation Reporting transforms raw agent evaluation runs into decision-ready reports. It keeps outcome populations, denominators, latency populations, and experiment conditions explicit, ensuring every headline number can be independently reproduced and verified without overstating capability. Use this when you need to create reports from agent evaluation runs where every headline number must include explicit outcome populations, denominators, latency populations, and experiment conditions for reproducibility.

What it does

Agent Evaluation Reporting converts raw agent evaluation runs into decision-ready reports. It ensures every headline number includes explicit outcome populations, denominators, latency populations, and experiment conditions so readers can independently reproduce and verify every claim without overstating capability.

When to use - and when NOT to

Use this skill when you need to create reports from agent evaluation runs that keep outcome populations, denominators, latency populations, and experiment conditions explicit. It's essential when reproducibility of headline numbers matters.

Do not use this when you need real-time evaluation dashboards or when explicit populations and denominators are not required.

Inputs and outputs

You provide raw agent evaluation run data including test outcomes, timing information, and experimental parameters. You receive a structured report that explicitly documents outcome populations, denominators, latency populations, and the exact experiment conditions under which tests were conducted. The report format ensures no capabilities are overstated.

Who it's for

This skill serves those who need to create reports from agent evaluation runs where every headline number must be reproducible through explicit outcome populations, denominators, latency populations, and experiment conditions.

Source README

Turn raw agent evaluation runs into a decision-ready report without hiding failures or overstating capability. Keep outcome populations, denominators, latency populations, and experiment conditions explicit so readers can reproduce every headline number.

FAQ

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