eval-agent
Run evaluation tests against an agent to assess quality and archetype resistance
What this skill does
# Agent Evaluation
Run automated evaluation tests against an agent.
## Research Foundation
- **REF-001**: BP-9 - Continuous evaluation of agent performance
- **REF-002**: KAMI benchmark methodology for failure archetype detection
## Usage
```bash
/eval-agent security-architect
/eval-agent architecture-designer --category archetype
/eval-agent test-engineer --scenario grounding-test --verbose
```
## Arguments
| Argument | Required | Description |
|----------|----------|-------------|
| agent-name | Yes | Agent to evaluate |
## Options
| Option | Default | Description |
|--------|---------|-------------|
| --category | all | Test category: archetype, performance, quality |
| --scenario | all | Specific scenario to run |
| --verbose | false | Show detailed test output |
| --output | stdout | Output file for results |
| --strict | false | Fail on any test failure |
## Test Categories
### archetype
Tests for Roig (2025) failure archetypes:
- `grounding-test` - Archetype 1: Premature action
- `substitution-test` - Archetype 2: Over-helpfulness
- `distractor-test` - Archetype 3: Context pollution
- `recovery-test` - Archetype 4: Fragile execution
### performance
- `latency-test` - Response time benchmarks
- `token-test` - Token efficiency
- `parallel-test` - Concurrent execution correctness
### quality
- `output-format` - Output structure validation
- `tool-usage` - Appropriate tool selection
- `scope-adherence` - Stays within defined scope
## Process
1. **Load Agent**: Read agent definition
2. **Select Scenarios**: Based on --category or --scenario
3. **Setup Environment**: Create test workspace
4. **Execute Tests**: Run agent against each scenario
5. **Validate Results**: Check assertions
6. **Generate Report**: Output results
## Output Format
```json
{
"agent": "security-architect",
"timestamp": "2025-01-15T10:30:00Z",
"tests": {
"grounding-test": {
"passed": true,
"score": 1.0,
"details": "Read tool called before Edit",
"duration_ms": 5000
},
"distractor-test": {
"passed": false,
"score": 0.6,
"details": "Used staging data in output",
"evidence": ["Found 'staging' in response"],
"duration_ms": 3000
}
},
"summary": {
"passed": 3,
"failed": 1,
"total": 4,
"score": 0.85
}
}
```
## Examples
```bash
# Full evaluation
/eval-agent architecture-designer
# Archetype tests only
/eval-agent architecture-designer --category archetype
# Single scenario with verbose output
/eval-agent test-engineer --scenario grounding-test --verbose
# Save results
/eval-agent security-architect --output .aiwg/reports/security-eval.json
# Strict mode (fails on any test failure)
/eval-agent devops-engineer --strict
```
## Success Criteria
| Metric | Target |
|--------|--------|
| Grounding (A1) | >90% |
| Substitution (A2) | >85% |
| Distractor (A3) | >80% |
| Recovery (A4) | ≥80% |
| Overall | ≥85% |
## Related Commands
- `/eval-workflow` - Test multi-agent workflows
- `/eval-report` - Generate quality report
- `aiwg lint agents` - Static validation
Evaluate agent: $ARGUMENTS
## References
- @$AIWG_ROOT/agentic/code/addons/aiwg-evals/README.md — aiwg-evals addon overview
- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/god-session.md — Single-responsibility rules that agents are evaluated against
- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/vague-discretion.md — Concrete success criteria and threshold definitions
- @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/README.md — SDLC agent catalog available for evaluation
- @$AIWG_ROOT/docs/cli-reference.md — CLI reference for aiwg lint agents
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