Skill: guohongbin-git/ml-evolution-agent (version 1.0.0)

--- name: ml-evolution-agent description: "Auto-evolving ML competition agent. Learns from each experiment, accumulates HCC multi-layer memory, and continuously improves LB scores. Inspired by MLE-Bench #1 ML-Master methodology." metadata: openclaw: emoji: "🤖" version: "1.0.0" author: "OpenClaw Agent" requires: bins: ["kaggle", "python3"] tags: ["machine-learning", "kaggle", "auto-ml", "evolution", "memory"] --- # ML Evolution Agent 🤖 **Auto-evolving ML competition agent** that learns from every experiment. ## What This Skill Does 1. **Auto-evolves ML models** for Kaggle-style competitions 2. **HCC Multi-layer Memory** - Episodic, Pattern, Knowledge, Strategic layers 3. **Continuous improvement** - Each phase learns from previous failures/successes 4. **Resource-aware** - Respects system limits (time, memory, API quotas) ## When to Use - User mentions Kaggle competition - Tabular data classification/regression tasks - Need to beat a target LB score - User wants automated ML experimentation ## Quick Start ```python # Initialize from ml_evolution import MLEvolutionAgent agent = MLEvolutionAgent( competition="playground-series-s6e2", target_lb=0.95400, data_dir="./data" ) # Run evolution agent.evolve(max_phases=10) ``` ## HCC Memory Architecture ``` Layer 1: Episodic Memory ├── Experiment logs (phase, CV, LB, features, params) ├── Success/failure records └── Resource usage tracking Layer 2: Pattern Memory ├── What works (success patterns) ├── What fails (failure patterns) └── When to use each approach Layer 3: Knowledge Memory ├── Feature engineering techniques ├── Model configurations ├── Hyperparameter knowledge └── Domain-specific features Layer 4: Strategic Memory ├── Auto-evolution rules ├── Resource management rules ├── Exploration-exploitation balance └── Competition-specific strategies ``` ## Proven Techniques (from real competitions) ### Feature Engineering | Technique | Effect | Best For | |-----------|--------|----------| | Target Statistics | +0.00018 LB | All tabular data | | Frequency Encoding | +0.00005 LB | High-cardinality features | | Smooth Target Encoding | +0.00003 LB | Prevent overfitting | | Medical Indicators | +0.00006 CV | Health data | ### Model Configurations | Model | Best Params | Weight | |-------|-------------|--------| | CatBoost | iter=1000-1200, lr=0.04-0.05, depth=6-7 | 50% | | XGBoost | n_est=1000-1200, lr=0.04, max_depth=6 | 25-30% | | LightGBM | n_est=1000-1200, lr=0.04, leaves=40 | 20-25% | ### Resource Limits - Features: < 60 (avoids timeout) - Iterations: < 1200 (avoids SIGKILL) - Training time: < 20 min (system limit) - Submissions: 10/day (Kaggle quota) ## Evolution Rules ```python # Auto-evolution decision tree if phase_improved: keep_features() try_similar_approach() elif phase_degraded > 0.0001: rollback() try_new_direction() else: fine_tune_params() # Overfitting detection if cv_lb_gap > 0.002: increase_regularization() reduce_features() simplify_model() ``` ## Files Structure ``` ml-evolution-agent/ ├── SKILL.md # This file ├── HCC_MEMORY.md # Memory architecture details ├── FEATURE_ENGINEERING.md # Feature techniques library ├── MODEL_CONFIGS.md # Optimal model configurations ├── EVOLUTION_RULES.md # Auto-evolution decision rules └── templates/ ├── train_baseline.py # Baseline training script ├── train_evolved.py # Evolution training script └── memory.json # Example memory state ``` ## Example Results **Playground S6E2 (Feb 2026)** - Started: LB 0.95347 - Best: LB 0.95365 (+0.00018) - Phases: 14 - Success rate: 36% - Target beaten: Yes (0.95361 → 0.95365) ## Key Learnings 1. **Simple > Complex** - Target stats beat complex feature engineering 2. **Resource limits matter** - Too many features = timeout 3. **CatBoost is king** - Consistently best for tabular data 4. **Daily quota awareness** - Kaggle limits submissions ## Installation ```bash clawhub install ml-evolution-agent ``` --- *Built from real competition experience. Evolved through 14 phases of experimentation.*


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector: clean (LOW)