Agent Evaluation
- 620 installs
- 29.9k repo stars
- Updated July 27, 2026
- davila7/claude-code-templates
agent-evaluation is an AI quality skill that systematically tests, benchmarks, and monitors LLM agent reliability through behavioral testing, capability assessment, and production readiness metrics before deployment.
About
agent-evaluation is a Claude Code skill from davila7/claude-code-templates, sourced from vibeship-spawner-skills under Apache 2.0, for testing and benchmarking LLM agents where even top agents achieve less than 50% on real-world benchmarks. The skill treats agent evaluation as fundamentally different from traditional software testing because identical inputs can yield different outputs and correctness often has no single answer. It covers behavioral testing, capability assessment, reliability metrics, and production monitoring workflows. Developers reach for agent-evaluation when shipping coding agents or autonomous workflows and need structured evaluation before trusting benchmark scores alone.
- Behavioral contract testing that defines and verifies agent invariants
- Statistical test evaluation that runs multiple executions and analyzes result distributions
- Adversarial testing that actively attempts to break agent behavior
- Capability assessment combined with reliability metrics
- Regression testing framework designed specifically for non-deterministic LLM outputs
Agent Evaluation by the numbers
- 620 all-time installs (skills.sh)
- +22 installs in the week ending Jul 3, 2026 (Skillselion tracking)
- Ranked #1,533 of 16,659 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
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| Installs | 620 |
|---|---|
| repo stars | ★ 29.9k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 27, 2026 |
| Repository | davila7/claude-code-templates ↗ |
How do you test LLM agent reliability before production?
Systematically test, benchmark, and monitor the reliability of LLM agents before they reach production.
Who is it for?
AI engineers shipping LLM agents who need behavioral testing and reliability benchmarks beyond standard unit tests.
Skip if: Traditional CRUD applications without LLM agents or teams satisfied with manual spot-checking without structured evaluation.
When should I use this skill?
User mentions agent testing, agent evaluation, benchmark agents, agent reliability, or pre-production agent quality gates.
What you get
Agent benchmark results, behavioral test suites, reliability metrics, and production monitoring plans for LLM agents.
- Agent benchmark reports
- behavioral test suites
- reliability metric dashboards
By the numbers
- Cites that top agents achieve less than 50% on real-world benchmarks
Files
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
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Use agent-evaluation instead of generic test runners when validating non-deterministic LLM agent behavior rather than deterministic function outputs.
FAQ
Why is agent-evaluation different from normal software testing?
agent-evaluation accounts for non-deterministic LLM outputs where the same input can produce different results and correctness may lack a single answer. Behavioral and capability benchmarks replace simple pass-fail assertions.
What benchmark context does agent-evaluation cite?
agent-evaluation notes that even top-performing agents achieve less than 50% on real-world benchmarks, motivating structured behavioral testing and reliability metrics before production deployment.
Is Agent Evaluation safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.