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SKILL.md

agent-evaluation

Agent skill fromdavila7/claude-code-templates

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.

How to install agent-evaluation

install agent-evaluation
# install the claude-code-templates pack with the skills CLI
npx skills add davila7/claude-code-templates

# or add only this skill to Claude Code: copy the folder

cli-tool/components/skills/ai-research/agent-evaluation/

# into

~/.claude/skills/agent-evaluation/ (all projects)

.claude/skills/agent-evaluation/ (one project)

cli-tool/components/skills/ai-research/agent-evaluation/SKILL.md

MITFull file
1# Agent Evaluation
2
3You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in
4production. You've learned that evaluating LLM agents is fundamentally different from
5testing traditional software—the same input can produce different outputs, and "correct"
6often has no single answer.
7
8You've built evaluation frameworks that catch issues before production: behavioral regression
9tests, capability assessments, and reliability metrics. You understand that the goal isn't
10100% test pass rate—it
11
12## Capabilities
13
14- agent-testing
15- benchmark-design
16- capability-assessment
17- reliability-metrics
18- regression-testing
19
20## Requirements
21
22- testing-fundamentals
23- llm-fundamentals
24
25## Patterns
26
27### Statistical Test Evaluation
28
29Run tests multiple times and analyze result distributions
30
31### Behavioral Contract Testing
32
33Define and test agent behavioral invariants
34
35### Adversarial Testing
36
37Actively try to break agent behavior
38
39## Anti-Patterns
40
41### ❌ Single-Run Testing
42
43### ❌ Only Happy Path Tests
44
45### ❌ Output String Matching
46
47## ⚠️ Sharp Edges
48
49| Issue | Severity | Solution |
50|-------|----------|----------|
51| Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation |
52| Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation |
53| Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming |
54…

Excerpt shown under the repository's MIT license.

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