Agent skill · research science · aiskillstore
agent-evaluation
Testing and benchmarking LLM agents including behavioral testing,
Why this skill is useful
Provides domain-specific methodologies and statistical analysis techniques for evaluating LLM agent performance that the AI wouldn't reliably generate on its own.
What it needs
Requires agentbench, langsmith, tau-bench, tool-emu installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Agent Evaluation 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 Capabilities agent-testing benchmark-design capability-assessment reliability-metrics regression-testing Prerequisites Knowledge: Testing methodologies, Statistical analysis basics, LLM behavior patterns Skillsrecommended: autonomous-agents, multi-agent-orchestration Required skills: testing-fundamentals, llm-fundamentals Scope Doesnotcover: Model training evaluation (loss, perplexity), Fairness and bias testing, User experience testing Boundaries: Focus is agent capability and reliability, Covers functional and behavioral testing Ecosystem Primarytools AgentBench - Multi-environment benchmark for LLM agents (ICLR 2024) τ-bench (Tau-bench) - Sierra's real-world agent benchmark ToolEmu - Risky behavior detection for agent tool use Langsmith - LLM tracing and evaluation platform Alternatives Braintrust - When: Need production monitoring integration LLM evaluation and monitoring PromptFoo - When: Focus on prompt-level evaluation Prompt testing framework Deprecated Manual testing only Patterns Statistical Test Evaluation Run tests multiple times and analyze result distributions When to use: Evaluating stochastic agent behavior interface TestResult { testId: string; runId: string; passed: boolean; score: number; // 0-1 for partial credit latencyMs: number; tokensUsed: number; output: string; expectedBehaviors: string[]; actualBehaviors: string[]; } interface StatisticalAnalysis { passRate: number; confidence95: [number, number]; meanScore: number; stdDevScore: number; meanLatency: number; p95Latency: number; behaviorConsistency: number; } class StatisticalEvaluator { private readonly minRuns = 10; private readonly confidenceLevel = 0.95; async evaluateAgent( agent: Agent, testSuite: TestCase[] ): Promise<EvaluationReport> { const results: TestResult[] = …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/agent-evaluation