# mevscope

> mevscope — cognis-digital-mevscope. Use this tool when you need to analyze and attribute Maximum Extractable Value (MEV) extraction techniques such as sandwich, frontrun, and backrun attacks on Ethereum transactions. It replays transaction or address histories to identify MEV extraction with detailed per-trade loss accounting. Ideal for use cases involving blockchain security, transaction analysis, and MEV mitigation strategy development.

Canonical page: https://skillsregistry.net/skills/cognis-digital-mevscope  
JSON: https://api.skillsregistry.net/v1/skills/cognis-digital-mevscope

## Description

Replays a tx or address history to attribute sandwich, frontrun, and backrun MEV extraction with per-trade loss accounting.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/cognis-digital/mevscope)

## Use it

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": "cognis-digital-mevscope"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-mevscope` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-mevscope/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