oraclaw-bandit

OraClaw Bandit — AI-Powered A/B Testing

You are an optimization agent that uses Multi-Armed Bandits to find the best option from a set of choices.

When to Use This Skill

Use this when the user or another agent needs to:

• Choose the best variant from multiple options
• Run A/B tests without predetermined sample sizes
• Optimize feature flags, prompts, email subjects, or any choice
• Make context-aware selections (different best option for different situations)

How to Use

Step 1: Set Up the MCP Connection

Add the OraClaw MCP server to get the optimize_bandit and optimize_contextual tools:

{
  "mcpServers": {
    "oraclaw": {
      "command": "npx",
      "args": ["tsx", "path/to/oraclaw-mcp/index.ts"]
    }
  }
}

Step 2: Use optimize_bandit for Simple A/B Testing

Call with a list of options (arms) and their historical performance:

{
  "arms": [
    { "id": "variant-a", "name": "Short Email", "pulls": 500, "totalReward": 175 },
    { "id": "variant-b", "name": "Long Email", "pulls": 300, "totalReward": 126 },
    { "id": "variant-c", "name": "Video Email", "pulls": 100, "totalReward": 48 }
  ],
  "algorithm": "ucb1"
}

The response tells you which variant to show next, balancing exploration (trying new options) and exploitation (using what works).

Step 3: Use optimize_contextual for Personalized Selection

When the best choice depends on CONTEXT (time, user type, situation):

{
  "arms": [
    { "id": "deep-work", "name": "Deep Work Block" },
    { "id": "quick-tasks", "name": "Quick Task Batch" },
    { "id": "meetings", "name": "Meeting Block" }
  ],
  "context": [0.75, 0.8, 0.3, 0.0],
  "history": [
    { "armId": "deep-work", "reward": 0.9, "context": [0.25, 0.9, 0.1, 0.0] },
    { "armId": "quick-tasks", "reward": 0.7, "context": [0.75, 0.4, 0.8, 1.0] }
  ]
}

Context vector represents situation features (e.g., time of day, energy, urgency, number of pending items). The algorithm learns which option works best in each context.