Evolve Skill

Evolutionary algorithm discovery for Claude Code. Evolves novel solutions to hard problems through parallel mutation and selection, with Rust benchmarks for precise performance measurement.

Usage

Invoke with /evolve followed by what you want to optimize:

/evolve "Optimize the string search in src/search.rs"
/evolve "Find a faster sorting algorithm for nearly-sorted arrays"
/evolve "Evolve a more efficient pathfinding heuristic"

How It Works

┌─────────────────────────────────────────────────────────────┐
│                        /evolve                               │
└─────────────────────────────┬───────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                 Claude Code Task Agents                      │
│            (8 parallel mutation strategies)                  │
│                                                              │
│  tweak | unroll | specialize | vectorize                    │
│  memoize | restructure | hybrid | alien                     │
└─────────────────────────────┬───────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                   Rust Evaluator                             │
│  • Compile with optimizations (LTO, release mode)           │
│  • Run comprehensive benchmarks                              │
│  • Compare against baselines                                 │
│  • Return fitness score                                      │
└─────────────────────────────────────────────────────────────┘

Requirements

The skill automatically checks for and helps install:

• Rust toolchain - offers to install via rustup if missing
• Python 3.10+ - guides you to install if needed

Just run /evolve and follow the prompts! No external dependencies required.

Fitness Function

score = correctness_gate * (performance_score + baseline_bonus)

Where:

• correctness_gate: 0 if any test fails, 1 otherwise
• performance_score: log(ops_per_second) normalized
• baseline_bonus: 50% bonus for each 100% improvement over best baseline

Output

Evolution artifacts in .evolve/<problem>/:

.evolve/<problem>/
├── rust/
│   └── src/
│       ├── lib.rs        # Trait definition
│       ├── baselines.rs  # Algorithms to beat
│       ├── evolved.rs    # Champion code
│       └── benchmark.rs  # Performance measurement
├── evaluator.py          # Fitness evaluation
└── mutations/            # All tested variants

Example Results

Tips

• Start simple: Begin with a basic implementation, let evolution discover complexity
• Good tests: Ensure your test suite covers edge cases; correctness is non-negotiable
• Diverse corpus: Use varied benchmark inputs to avoid overfitting
• Patience: Significant improvements often emerge after multiple generations