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InnerScopeHearing
InnerScopeHearingOFFICIAL
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agent-evals

Automate AI agent response evaluation against predefined rubrics and generate scorecards.

Updated May 30, 2026
Search Tags:#quality control#AI agent#AI monitoring#agent evaluation#performance assessment
One-click install
npx skills add https://github.com/InnerScopeHearing/otchealth-claude-tools --skill agent-evals-innerscopehearing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-evals
Source: https://github.com/InnerScopeHearing/otchealth-claude-tools/tree/main/skills/agent-evals
Command: npx skills add https://github.com/InnerScopeHearing/otchealth-claude-tools --skill agent-evals-innerscopehearing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires node, json, azure-openai, anthropic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the evaluation and quality control of agent responses, ensuring consistent performance and identifying potential issues early.

Core Features & Use Cases

  • Golden Task Evaluation: Automatically evaluates agent responses against predefined rubrics.
  • Scorecard Generation: Generates detailed scorecards for each evaluation.
  • Quality Control: Identifies and reports on quality issues in agent responses.
  • Use Case: Use this Skill to evaluate the responses of your AI agents against a set of predefined criteria, ensuring they meet the required quality standards.

Quick Start

Run the agent-evals skill with the following command: node run-evals.mjs --agent cto --task cto-diagnose-failing-job --emit

Frequently Asked Questions about agent-evals

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

FAQPage Schema
How do I automate AI agent evaluation against predefined rubrics?▼

Automate AI agent evaluation by running the node command with specific agent and task parameters to evaluate responses against predefined rubrics. This generates detailed scorecards to ensure your agents meet required quality standards.

What is automated quality control for AI agent responses?▼

Automated quality control for AI agent responses is the process of identifying performance issues early by applying predefined rubrics. It ensures consistent performance across your fleet by automatically generating evaluation scorecards.

Do I need Azure OpenAI or Anthropic API keys to generate agent scorecards?▼

Yes, you need Azure OpenAI or Anthropic API keys to generate agent scorecards. These model-based evaluation APIs are required dependencies for assessing agent responses and producing quality control reports.

Can I use Node and JSON to run performance assessments on my AI agent fleet?▼

Yes, you can use Node to run performance assessments on your AI agent fleet. The process requires Node to execute evaluation scripts and Python libraries for parsing JSON data to generate the scorecards.

What's the best way to monitor AI agent quality across multiple tasks?▼

The best way to monitor AI agent quality is to run automated golden task evaluations against predefined rubrics for each task. This identifies quality issues early and generates detailed scorecards for consistent performance monitoring.

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