agent-evaluation
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents…
Skill by AndrewHaward2310
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Install
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.
npx agentmarket install skill/andrewhaward2310-agent-evaluation
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
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 real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
- License
- MIT
- Instructions
- 60 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`
From AndrewHaward2310/.agents, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Feb 10, 2026 (8 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/AndrewHaward2310/.agents
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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agent-evaluation skill. Agent Mart. https://agentmart.dev/skills/andrewhaward2310-agent-evaluation (last updated 2026-10-24)