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.
Rules
- Always include historical data when available — more data = better selections
- Use
ucb1 algorithm for most cases. Use thompson when you need more exploration early on.
- Record rewards after each decision to improve future selections
- Context vectors must be consistent length across all calls
- Rewards should be normalized to 0-1 range
Pricing
$0.01 per optimization call (USDC on Base via x402). Free tier: 3,000 calls/month with API key.
