# com.predictionmarketspicks/quant

> com.predictionmarketspicks/quant — com-predictionmarketspicks-quant. Use this tool when you need to analyze and optimize prediction market strategies, as it provides core functionality for calculating expected value, Kelly criterion, and arbitrage opportunities, with inputs including market data and outputs including optimal bet sizing, and is particularly useful in the context of Kalshi and Polymarket platforms. It solves problems related to informed decision-making and risk management in prediction markets. The tool is ideal for AI agents seeking to gain an edge in these markets.

Canonical page: https://skillsregistry.net/skills/com-predictionmarketspicks-quant  
JSON: https://api.skillsregistry.net/v1/skills/com-predictionmarketspicks-quant

## Description

Quant tools + an NFL fantasy draft assistant for AI agents — Kalshi & Polymarket EV, edge, ADP.

## Trust

- **Trust score (0–1):** 0.93
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/com.predictionmarketspicks%2Fquant)
- **Repository:** <https://github.com/predictionmarketspicks/mcp>

## Use it

MCP endpoint published by the skill: `https://predictionmarketspicks.com/api/mcp/mcp`

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "com-predictionmarketspicks-quant"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/com-predictionmarketspicks-quant` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/com-predictionmarketspicks-quant/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt