agentclash-scorecard-reader

Name: agentclash-scorecard-reader
Availability: InStock
Author: agentclash

Interpret AgentClash run JSON to identify winners, regressions, and failure root causes.

25|2|Updated Mar 4, 2026

Search Tags:#debugging #evaluation #regression #ai-agents #scorecard #agentclash

Ready to run

npx skills add https://github.com/agentclash/agentclash --skill agentclash-scorecard-reader

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentclash-scorecard-reader
Source: https://github.com/agentclash/agentclash/tree/main/web/content/agent-skills/agentclash-scorecard-reader
Command: npx skills add https://github.com/agentclash/agentclash --skill agentclash-scorecard-reader

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the challenge of interpreting complex, multi-dimensional AI agent evaluation data by transforming raw JSON run evidence into clear, evidence-backed engineering findings.

Core Features & Use Cases

Evidence-First Analysis: Synthesizes rankings, scorecards, and failure-review items into human-readable summaries.
Regression Identification: Helps identify why an agent passed or failed by correlating scorecard dimensions with replay steps and artifact evidence.
Use Case: When an agent regresses in a production eval, use this skill to pinpoint the specific failure cluster, review the replay sequence, and determine if the failure is a policy violation or a tool-use error.

Quick Start

Use the agentclash-scorecard-reader skill to analyze the latest run scorecard and identify the primary cause of failure for the specified agent.

Frequently Asked Questions about agentclash-scorecard-reader

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

FAQPage Schema

How do I analyze AI agent evaluation scorecards to find regression root causes?▼

To analyze AI agent evaluation scorecards for regression root causes, synthesize rankings, replay timelines, and failure clusters to pinpoint whether a production eval failure stems from a policy violation or a tool-use error.

What is the best way to interpret multi-dimensional AI agent evaluation data from JSON run evidence?▼

Interpreting multi-dimensional AI agent evaluation data involves transforming raw JSON run evidence into evidence-backed engineering findings by correlating scorecard dimensions with replay steps and artifact evidence to identify winners and regressions.

How do I identify failure clusters from agent evaluation replay timelines?▼

Identifying failure clusters from replay timelines requires correlating scorecard dimensions with replay steps and artifact evidence to determine if an agent passed or failed due to specific tool-use errors or policy violations.

Do I need the AgentClash CLI authenticated to access run-level JSON payloads for debugging?▼

Yes, accessing run-level JSON payloads for debugging requires the AgentClash CLI to be authenticated and linked to a valid workspace to retrieve the evaluation run evidence needed for scorecard analysis.

Can I use scorecard analysis to determine if an agent regression is a policy violation?▼

Yes, scorecard analysis can determine if an agent regression is a policy violation by correlating scorecard dimensions with replay sequences and artifact evidence to review the specific failure cluster and root cause.