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
Profile →npx skills add sickn33/agentic-awesome-skills --skill agent-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Tests and benchmarks LLM agents across behavioral testing, capability assessment, reliability metrics, and production monitoring, including regression testing and statistical analysis of results.
How it works
- Defines capabilities such as agent-testing, benchmark-design, capability-assessment, reliability-metrics, and regression-testing.
- Provides prerequisites: knowledge of testing methodologies, statistical analysis basics, and LLM behavior patterns; skills like autonomous-agents and multi-agent-orchestration; required skills including testing-fundamentals and llm-fundamentals.
- Outlines patterns for Statistical Test Evaluation, including running tests multiple times, aggregating results by test, and producing overall analyses with pass rates, confidence intervals, mean scores, latency metrics, and behavior consistency.
- Includes Behavioral Contract Testing: defines a Customer Service contract with must-behaviors and must-not-behaviors, plus contextual behaviors conditioned on inputs (e.g., refund scenarios). Provides a testContract method to run inputs through an agent, collect violations, and report pass status.
- Details Adversarial Testing to actively probe agent resilience, with categories like prompt_injection, role_confusion, boundary_testing, resource_exhaustion, and output_manipulation. Provides generation of adversarial tests (injection tests, role confusion tests, boundary tests, output manipulation, and tool abuse if applicable) and a runAdversarialSuite method.
When to use it
- When evaluating stochastic agent behavior to understand variability and reliability.
- When enforcing behavioral invariants via Behavioral Contract Testing.
- When seeking to identify edge cases and failure modes through Adversarial Testing.
What it can touch
- Tools listed: AgentBench, τ-bench (Tau-bench), ToolEmu, Langsmith.
- Interactions include creating test results, analyses, contracts, and adversarial test suites; agent.process is invoked for inputs during contract testing and adversarial scenarios.
Caveats
- Risk level declared as safe.
- License: MIT.
- Focus is on agent capability and reliability, excluding model training evaluation and bias testing.
# 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 - Skills_recommended: autonomous-agents, multi-agent-orchestration - Required skills: testing-fundamentals, llm-fundamentals ## Scope - Does_not_cover: 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 ### Primary_tools - 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 prom
- Capabilities
- Prerequisites
- Scope
- Ecosystem
- Primarytools
- Alternatives
- Deprecated
- Patterns
- Statistical Test Evaluation
- Behavioral Contract Testing
- Adversarial Testing
- Regression Testing Pipeline
- Sharp Edges
- Agent scores well on benchmarks but fails in production
What does the agent-evaluation skill do?
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
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill agent-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.