agent-evaluation
Evaluate and Test LLM Agent Performance
LLM agents often fail in production despite passing benchmarks. This skill provides behavioral testing, capability assessments, and reliability metrics to catch issues before deployment.
Die Skill-ZIP herunterladen
In Claude hochladen
Gehe zu Einstellungen → Fähigkeiten → Skills → Skill hochladen
Einschalten und loslegen
Teste es
Verwendung von "agent-evaluation". Run behavioral contract test on customer support agent
Erwartetes Ergebnis:
Test Results: 5/5 invariants passed across 20 test runs. Consistency score: 94%. Minor variance detected in response tone under high-load scenarios.
Verwendung von "agent-evaluation". Adversarial testing for code generation agent
Erwartetes Ergebnis:
Identified 3 failure modes: (1) Silent failure on malformed syntax, (2) Over-confident incorrect answers on ambiguous specs, (3) Resource exhaustion on recursive tasks.
Sicherheitsaudit
SicherAll static analysis findings determined to be false positives. The external_commands pattern matches markdown backtick formatting for inline code references, not shell execution. The unicode escape sequence is a standard em-dash character in the description. No weak cryptography exists—this is a documentation file with no executable code. The skill describes LLM agent evaluation methodologies and contains no security risks.
Qualitätsbewertung
Was du bauen kannst
Pre-Production Agent Validation
Run comprehensive behavioral tests on agents before deploying to production environments to catch regressions and capability gaps.
Agent Comparison and Selection
Evaluate multiple agent configurations or models against standardized benchmarks to select the best performer for specific tasks.
Continuous Agent Monitoring
Implement ongoing reliability metrics and regression tests to detect performance degradation in deployed agents.
Probiere diese Prompts
Test this agent on a simple task and verify the output matches expected behavior. Run the test 3 times and report any inconsistencies.
Define behavioral invariants that this agent must maintain across all inputs. Create test cases that verify each invariant holds true.
Design edge cases and adversarial inputs that could break this agent. Include malformed inputs, ambiguous requests, and conflicting constraints.
Run this agent on the same task 10 times. Analyze the distribution of outputs, calculate consistency metrics, and identify failure patterns.
Bewährte Verfahren
- Run tests multiple times and analyze statistical distributions rather than single outcomes
- Focus on behavioral invariants rather than exact output string matching
- Include adversarial inputs that actively try to break the agent
Vermeiden
- Testing agents with single runs—LLM outputs vary and require statistical analysis
- Only testing happy paths—edge cases reveal critical failure modes
- Optimizing agents for specific metrics rather than actual task performance