Parallel Test Bench

Parallel Test Bench

You are an expert at designing and running parallel agent experiments with automatic metric collection and benchmark aggregation.

When to Use This Skill

Use this skill whenever you need to:

• Compare multiple approaches or implementations side-by-side
• A/B test different prompts, tools, or configurations
• Benchmark performance improvements (before/after comparisons)
• Run experiments with different agent settings
• Collect timing and token usage metrics systematically
• Generate comparison reports with statistical analysis

Core Workflow

The parallel testing workflow has 5 phases:

1. Setup - Define test configurations and create workspace structure
2. Spawn - Launch all agents in parallel (one message, multiple Agent tool calls)
3. Collect - Capture timing/token metrics from task notifications
4. Grade - Evaluate outputs against assertions programmatically
5. Aggregate - Generate benchmark reports with comparisons

Phase 1: Setup

Define Test Configurations

Before spawning agents, clarify with the user:

Test name: What are you testing? (e.g., "emoji-removal", "refactoring-approach")

Configurations: What variations to test?

• Baseline: The control group (e.g., no skill, old version, simple approach)
• Treatment: The experimental group (e.g., with skill, new version, optimized approach)
• Can have 2+ configurations (e.g., baseline vs approach-A vs approach-B)

Test cases: What specific tasks to run?

• Each test case needs: prompt, expected output description, optional input files
• Aim for 2-3 test cases minimum
• Each case tests a different aspect or edge case

Success criteria: How to measure success?

• Quantitative: assertions that can be checked programmatically
• Qualitative: aspects requiring human judgment

Create Workspace Structure

<test-name>-workspace/
└── iteration-1/
    ├── test-case-1/
    │   ├── baseline/
    │   │   ├── outputs/
    │   │   ├── timing.json
    │   │   └── grading.json
    │   └── treatment/
    │       ├── outputs/
    │       ├── timing.json
    │       └── grading.json
    ├── test-case-2/
    │   └── ...
    ├── benchmark.json
    └── benchmark.md

Don't create all directories upfront - create them as you go when saving outputs.

Phase 2: Spawn Agents in Parallel

CRITICAL: Spawn ALL agents in a SINGLE message using multiple Agent tool calls. This ensures they run truly in parallel.

For each test case, spawn one agent per configuration:

Test Case: <descriptive-name>

Baseline agent:
- Configuration: <baseline description>
- Task: <test prompt>
- Save outputs to: workspace/iteration-1/<test-case>/baseline/outputs/

Treatment agent:
- Configuration: <treatment description>
- Task: <same test prompt>
- Save outputs to: workspace/iteration-1/<test-case>/treatment/outputs/

Example agent spawning (all in one message):

I'm spawning 4 agents in parallel (2 test cases × 2 configurations):

Then use 4 Agent tool calls in the same response.

Agent Prompt Template

Each agent should receive clear instructions:

Execute this task:
- Configuration: <config description, e.g., "without emoji-remover skill" or "with skill at path/to/skill">
- Task: <user's test prompt>
- Input files: <paths to input files, or "none">
- Save all outputs to: <workspace-path>/outputs/
- Outputs to save: <what matters - e.g., "the cleaned markdown file", "the refactored code">

Phase 3: Collect Metrics

When each agent completes, you receive a task notification with:

• total_tokens: Token count
• duration_ms: Execution time in milliseconds

Save this data IMMEDIATELY - it's only available in the notification:

{
  "total_tokens": 18534,
  "duration_ms": 16408,
  "total_duration_seconds": 16.4
}

Save to <test-case>/<config>/timing.json.

Tracking Completions

As agents complete, keep a mental checklist:

• test-case-1/baseline - tokens: X, time: Ys
• test-case-1/treatment - tokens: X, time: Ys
• test-case-2/baseline - tokens: X, time: Ys
• test-case-2/treatment - tokens: X, time: Ys

Once all complete, proceed to grading.

Phase 4: Grade Outputs

For each configuration output, evaluate against assertions.

Programmatic Grading

Use the bundled grading script for objective checks:

python scripts/grade_outputs.py \
  --test-case <path-to-test-case-dir> \
  --config baseline \
  --assertions <assertions-json>

Or write custom grading logic inline:

# Example: Check no emojis remaining
import re
EMOJI_PATTERN = re.compile(r'[\U0001F300-\U0001F9FF]')
with open(output_file) as f:
    content = f.read()
has_emojis = bool(EMOJI_PATTERN.search(content))

grading = {
  "expectations": [
    {
      "text": "No emojis in output",
      "passed": not has_emojis,
      "evidence": f"Found {len(EMOJI_PATTERN.findall(content))} emojis" if has_emojis else "Clean"
    }
  ]
}

Save to <test-case>/<config>/grading.json.

Grading Schema

{
  "expectations": [
    {
      "text": "Assertion description",
      "passed": true,
      "evidence": "Supporting details"
    }
  ]
}

Field requirements:

• text: Human-readable assertion name
• passed: Boolean result
• evidence: Why it passed/failed

Phase 5: Aggregate Results

Use the bundled aggregation script:

python scripts/aggregate_benchmark.py \
  --workspace workspace/iteration-1 \
  --configs baseline treatment \
  --test-name "emoji-removal"

This generates:

• benchmark.json - Machine-readable metrics
• benchmark.md - Human-readable report

Benchmark Contents

Pass Rate: Percentage of assertions passed per configuration Token Usage: Mean ± stddev across test cases Execution Time: Mean ± stddev across test cases Delta: Difference between configurations (treatment - baseline)

Manual Aggregation

If you need to aggregate manually:

import json, statistics

configs = {}
for config in ['baseline', 'treatment']:
    grades, tokens, times = [], [], []

    for test_case in test_cases:
        # Load grading
        with open(f"{test_case}/{config}/grading.json") as f:
            g = json.load(f)
            passed = sum(1 for e in g['expectations'] if e['passed'])
            total = len(g['expectations'])
            grades.append(passed / total)

        # Load timing
        with open(f"{test_case}/{config}/timing.json") as f:
            t = json.load(f)
            tokens.append(t['total_tokens'])
            times.append(t['total_duration_seconds'])

    configs[config] = {
        'pass_rate': {'mean': statistics.mean(grades), 'values': grades},
        'tokens': {'mean': statistics.mean(tokens), 'stddev': statistics.stdev(tokens) if len(tokens) > 1 else 0},
        'time': {'mean': statistics.mean(times), 'stddev': statistics.stdev(times) if len(times) > 1 else 0}
    }

# Calculate deltas
delta = {
    'pass_rate': configs['treatment']['pass_rate']['mean'] - configs['baseline']['pass_rate']['mean'],
    'tokens': configs['treatment']['tokens']['mean'] - configs['baseline']['tokens']['mean'],
    'time': configs['treatment']['time']['mean'] - configs['baseline']['time']['mean']
}

Reporting Results

After aggregation, present findings to the user:

Summary Format

# <Test Name> - Benchmark Results

## Summary
- Test Cases: N
- Configurations: baseline vs treatment

## Results

### Pass Rate
- Baseline: X.X%
- Treatment: Y.Y%
- Delta: +Z.Z% (better/worse/same)

### Token Usage
- Baseline: X,XXX ± YYY tokens
- Treatment: X,XXX ± YYY tokens
- Delta: +Z,ZZZ tokens (X% increase/decrease)

### Execution Time
- Baseline: XX.X ± Y.Y seconds
- Treatment: XX.X ± Y.Y seconds
- Delta: +Z.Z seconds (X% faster/slower)

## Analysis

<Your interpretation of the results>

## Recommendation

<What the data suggests - e.g., "Treatment approach is 15% faster with same quality" or "Baseline is simpler and equally effective">

Advanced: Multi-Configuration Testing

For 3+ configurations (e.g., baseline vs approach-A vs approach-B vs approach-C):

1. Spawn N×M agents (N test cases × M configurations)
2. Collect metrics for all
3. Grade all outputs
4. Aggregate with pairwise comparisons:
   • Compare each treatment to baseline
   • Optionally compare treatments to each other
5. Report best performer with statistical significance

Best Practices

Choosing Test Cases

Good test cases:

• Representative of real-world usage
• Test different aspects (edge cases, common cases, stress tests)
• Have clear success criteria

Poor test cases:

• Too similar to each other (test the same thing)
• Trivial (any approach works)
• Ambiguous success criteria

Fair Comparisons

Ensure apples-to-apples:

• Same prompt for all configurations
• Same input files
• Same model (unless testing model differences)
• Run truly in parallel (not sequentially)

Statistical Rigor

For small sample sizes (2-3 test cases):

• Report individual results, not just averages
• Be cautious about generalizing
• Consider variance (stddev) when interpreting means

For larger sample sizes (10+ test cases):

• Use statistical tests (t-tests, confidence intervals)
• Report effect sizes, not just p-values
• Consider practical significance vs statistical significance

Common Use Cases

Use Case 1: Skill Effectiveness Testing

Question: Does this skill improve results?

Setup:

• Baseline: Without skill
• Treatment: With skill
• Test cases: 3-5 representative tasks

Metrics:

• Quality: Pass rate on assertions
• Efficiency: Token usage, execution time
• Consistency: Stddev across test cases

Use Case 2: Prompt Optimization

Question: Which prompt variant works best?

Setup:

• Baseline: Original prompt
• Treatment-A: Variant with examples
• Treatment-B: Variant with reasoning steps
• Treatment-C: Variant with constraints

Metrics:

• Correctness: Did it produce the right output?
• Completeness: Did it address all requirements?
• Clarity: Is the output easy to understand?

Use Case 3: Refactoring Validation

Question: Did refactoring break anything?

Setup:

• Baseline: Old implementation
• Treatment: Refactored implementation
• Test cases: Comprehensive test suite

Metrics:

• Correctness: All tests pass
• Performance: Execution time
• Code quality: Complexity, readability (manual assessment)

Use Case 4: Model Comparison

Question: Which model is best for this task?

Setup:

• Baseline: Sonnet
• Treatment-A: Opus
• Treatment-B: Haiku

Metrics:

• Quality: Task-specific assertions
• Cost: Token usage × model pricing
• Speed: Execution time

Bundled Scripts

The skill includes helper scripts in scripts/:

grade_outputs.py

Programmatic grading helper:

python scripts/grade_outputs.py \
  --output-dir <path-to-outputs> \
  --assertions <assertions-json-file> \
  --save-to <grading-json-path>

aggregate_benchmark.py

Benchmark aggregation:

python scripts/aggregate_benchmark.py \
  --workspace <iteration-dir> \
  --configs baseline treatment \
  --test-name "my-test"

compare_outputs.py

Detailed diff generation for outputs:

python scripts/compare_outputs.py \
  --baseline <baseline-output> \
  --treatment <treatment-output> \
  --format <text|json|md>

Workflow Checklist

Use this checklist for each test run:

Setup Phase:

• Define test name and configurations
• Create workspace structure
• Define test cases with clear prompts
• Define assertions for grading

Execution Phase:

• Spawn all agents in parallel (single message, multiple Agent calls)
• Monitor agent completions
• Save timing.json for each completion immediately

Analysis Phase:

• Grade all outputs against assertions
• Save grading.json for each configuration
• Aggregate results into benchmark.json
• Generate benchmark.md report

Reporting Phase:

• Present summary to user
• Explain key findings
• Make recommendation based on data

Remember

The goal is to provide objective, data-driven comparisons that help the user make informed decisions. Focus on:

1. Fair tests - Same conditions for all configurations
2. Meaningful metrics - Measure what actually matters
3. Clear reporting - Present results understandably
4. Actionable insights - What should the user do with this data?

Avoid:

• Spawning agents sequentially (defeats the purpose of parallel testing)
• Forgetting to capture timing data (only available in task notification)
• Making claims without statistical backing
• Testing configurations that differ in multiple ways (can't isolate cause)