unsloth-mcp-server: Skill for Claude Code

Adaptive Workflows

Build a self-learning system that gets better with every experiment you run.

Overview

Learn from experience:

• Experiment tracking - Record all experiments and results
• Pattern recognition - Identify what works best for your use cases
• Smart recommendations - Get suggestions based on past success
• Workflow templates - Create reusable templates from successful experiments
• A/B testing - Compare approaches systematically
• Knowledge base - Build your personal best practices library
• Continuous improvement - Workflows get better over time

Quick Start

Initialize Workflow Tracker

import json
from datetime import datetime
from pathlib import Path

class WorkflowTracker:
    def __init__(self, storage_path="./workflows.json"):
        self.storage_path = Path(storage_path)
        self.experiments = self.load_experiments()

    def load_experiments(self):
        """Load previous experiments"""
        if self.storage_path.exists():
            with open(self.storage_path, 'r') as f:
                return json.load(f)
        return []

    def save_experiments(self):
        """Save experiments to disk"""
        with open(self.storage_path, 'w') as f:
            json.dump(self.experiments, f, indent=2)

    def record_experiment(self, experiment: dict):
        """Record a new experiment"""
        experiment['timestamp'] = datetime.now().isoformat()
        experiment['id'] = len(self.experiments)
        self.experiments.append(experiment)
        self.save_experiments()
        return experiment['id']

# Initialize
tracker = WorkflowTracker()

Record Your First Experiment

# After training
experiment = {
    'task': 'medical_qa_finetuning',
    'model': 'Llama-3.2-7B',
    'dataset_size': 1000,
    'hyperparameters': {
        'learning_rate': 2e-4,
        'lora_rank': 16,
        'batch_size': 8,
        'epochs': 3
    },
    'results': {
        'final_loss': 0.42,
        'training_time_hours': 2.5,
        'cost_usd': 5.20,
        'eval_accuracy': 0.89
    },
    'notes': 'Worked well, converged smoothly'
}

tracker.record_experiment(experiment)