This entry duplicates agent-evaluation, which is the one we list in search.

agent-evaluation

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents…

Skill by Soojung-Kang

IndexedMIT

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...

Use cases: testing, ml, agent-building

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What's inside

Skill name: agent-evaluation
Description: Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...
License: MIT
Instructions: 63 lines in SKILL.md · view on GitHub

Files (1)

SKILL.md

SKILL.md (first lines)

# Agent Evaluation

You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in
production. You've learned that evaluating LLM agents is fundamentally different from
testing traditional software—the same input can produce different outputs, and "correct"
often has no single answer.

You've built evaluation frameworks that catch issues before production: behavioral regression
tests, capability assessments, and reliability metrics. You understand that the goal isn't
100% test pass rate—it

## Capabilities

- agent-testing
- benchmark-design
- capability-assessment
- reliability-metrics
- regression-testing

## Requirements

- testing-fundamentals
- llm-fundamentals

## Patterns

### Statistical Test Evaluation

Run tests multiple times and analyze result distributions

### Behavioral Contract Testing

Define and test agent behavioral invariants

### Adversarial Testing

Actively try to break agent behavior

## Anti-Patterns

### ❌ Single-Run Testing

### ❌ Only Happy Path Tests

### ❌ Output String Matching

## ⚠️ Sharp Edges

| Issue | Severity | Solution |
|-------|----------|----------|
| Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation |
| Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation |
| Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming |
| Test data accidentally used in training or prompts | critical | // Prevent data leakage in agent evaluation |

## Related Skills

Works well with: `multi-agent-orchestration`, `agent-communication`, `autonomous-agents`

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

From Soojung-Kang/antigravity-awesome-skills, MIT.

Signals

Stars: 0
Forks: 0
Open issues: 0
Last commit (this folder): Feb 27, 2026 (8 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License: MIT
Repository: github.com/Soojung-Kang/antigravity-awesome-skills
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

Same skill elsewhere

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agent-evaluation skill. Agent Mart. https://agentmart.dev/skills/soojung-kang-agent-evaluation (last updated 2026-10-24)