solution-comparator

Compare multiple solutions for correctness and performance

509 stars

Best use case

solution-comparator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Compare multiple solutions for correctness and performance

Teams using solution-comparator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/solution-comparator/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/algorithms-optimization/skills/solution-comparator/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/solution-comparator/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How solution-comparator Compares

Feature / Agent	solution-comparator	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Compare multiple solutions for correctness and performance

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Solution Comparator Skill

## Purpose

Compare multiple algorithm solutions against the same test cases to verify correctness and benchmark performance.

## Capabilities

- Run solutions against same test cases
- Performance benchmarking and comparison
- Output diff analysis
- Find minimal failing test case
- Memory usage comparison
- Time complexity validation

## Target Processes

- correctness-proof-testing
- complexity-optimization
- upsolving
- algorithm-implementation

## Comparison Modes

1. **Correctness**: Compare outputs against a known-correct solution
2. **Performance**: Benchmark execution time across solutions
3. **Stress Testing**: Run with random large inputs to find discrepancies
4. **Minimal Counter-example**: Binary search to find smallest failing case

## Input Schema

```json
{
  "type": "object",
  "properties": {
    "solutions": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "code": { "type": "string" },
          "language": { "type": "string" }
        }
      }
    },
    "testCases": { "type": "array" },
    "mode": {
      "type": "string",
      "enum": ["correctness", "performance", "stress", "minimal"]
    },
    "oracleSolution": { "type": "string" },
    "timeout": { "type": "integer", "default": 5000 }
  },
  "required": ["solutions", "mode"]
}
```

## Output Schema

```json
{
  "type": "object",
  "properties": {
    "success": { "type": "boolean" },
    "results": { "type": "array" },
    "discrepancies": { "type": "array" },
    "performance": { "type": "object" },
    "minimalFailingCase": { "type": "object" }
  },
  "required": ["success"]
}
```

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