<objective> Prompt Testing runs A/B comparisons between prompt variants through a 5-step workflow: define the objective and metrics, prepare variants A/B and a test dataset, execute on the dataset, analyze and compare results, then decide. Metrics span three categories -- quality (accuracy, compliance, consistency, relevance), efficiency (input/output tokens, latency, cost), and robustness (edge-case handling, jailbreak resistance, error recovery) -- plus a UX category detailed in metrics.md.

The adoption decision is rule-based: adopt B if its accuracy is at least equal with acceptable token cost, consider B as a trade-off if accuracy improves >10% despite <20% token regression, otherwise keep A or iterate. Requires a minimum of 20 test cases with 15-20% edge cases for statistical significance. </objective>

Prompt Testing

Skill for testing, comparing, and measuring prompt performance.

References

• [metrics.md](references/metrics.md) - Load when: defining or scoring Quality/Efficiency/Robustness/UX metrics with thresholds and calculation formulas
• [methodology.md](references/methodology.md) - Load when: running a full A/B test (hypothesis, dataset sizing, statistical significance, common pitfalls)
• [templates.md](references/templates.md) - Load when: writing a test dataset JSON or an A/B test report

Testing Workflow

1. DEFINE
   └── Test objective
   └── Metrics to measure
   └── Success criteria

2. PREPARE
   └── Variants A and B
   └── Test dataset
   └── Baseline (if existing)

3. EXECUTE
   └── Run on dataset
   └── Collect results
   └── Document observations

4. ANALYZE
   └── Calculate metrics
   └── Compare variants
   └── Identify patterns

5. DECIDE
   └── Recommendation
   └── Statistical confidence
   └── Next iterations

Performance Metrics

Quality

Efficiency

Robustness

For full definitions, thresholds, and the UX metrics category, see [metrics.md](references/metrics.md). For the test dataset and report formats, see [templates.md](references/templates.md).

Commands

# Create a test
/prompt test create --name "Test v1" --dataset tests.json

# Run an A/B test
/prompt test run --a prompt_a.md --b prompt_b.md --dataset tests.json

# View results
/prompt test results --id test_001

# Compare two tests
/prompt test compare --tests test_001,test_002

Decision Criteria

When to adopt variant B?

IF:
  - Accuracy B >= Accuracy A
  AND (Tokens B <= Tokens A * 1.1 OR accuracy improvement > 5%)
  AND no regression on edge cases
THEN:
  → Adopt B

ELSE IF:
  - Accuracy improvement > 10%
  AND token regression < 20%
THEN:
  → Consider B (acceptable trade-off)

ELSE:
  → Keep A or iterate

Best Practices

1. Minimum 20 test cases for significance
2. Include edge cases (15-20% of dataset)
3. Test multiple runs for consistency
4. Document hypotheses before testing
5. Version the prompts being tested