# mev-analysis

Evaluates MEV exposure and sandwich attack risks for Solana DEX trading. Recommends slippage limits and Jito bundle protections.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 459 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/mev-analysis/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/mev-analysis

## What it does

The skill inspects MEV vulnerability on Solana decentralized exchanges. Solana lacks a public mempool, but searchers intercept transactions en route to slot leaders and assemble Jito bundles. The documentation examines sandwich attacks and cross-DEX arbitrage.

Risk calculation relies on the `estimate_mev_risk` model. It checks trade size against pool liquidity, evaluates slippage headroom, and subtracts validator tips to determine attacker profitability. Trades on concentrated liquidity pools face less exposure than standard constant-product AMMs.

High slippage on illiquid pools leaves swaps vulnerable to front-running. The model suggests defensive steps like splitting large orders and routing swaps through private endpoints or Jito bundles.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill mev-analysis -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/mev-analysis/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/mev-analysis/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill mev-analysis -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/mev-analysis/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill mev-analysis -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/mev-analysis/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills mev-analysis
```

2. **Manually: put the files in place**
- File: `.github/skills/mev-analysis/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill mev-analysis -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/mev-analysis
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/mev-analysis/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill mev-analysis -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/mev-analysis/SKILL.md

## When to use it
- Detect sandwich attack risks before submitting a DEX swap
- Calculate trade price impact against pool liquidity
- Configure Jito bundle tips and private RPC routing
- Adjust slippage tolerance to eliminate attacker profit margins

## Example prompts
- Run mev-analysis to check sandwich attack risk for my pending DEX trade
- Use mev-analysis to evaluate price impact and slippage headroom on this pool
- Ask mev-analysis how to protect a swap using Jito bundles

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE afa9deaa9c375049
````text
---
name: mev-analysis
description: MEV exposure assessment, sandwich attack detection, and protection strategies for Solana DEX trading
---

# MEV Analysis for Solana DEX Trading

Maximal Extractable Value (MEV) is the profit that validators and searchers can extract by reordering, inserting, or censoring transactions within a block. On Solana DEXes, MEV primarily manifests as sandwich attacks against swaps, cross-DEX arbitrage, and liquidation extraction. This skill covers detection, estimation, and protection strategies.

## What Is MEV on Solana?

MEV occurs when someone with transaction ordering power profits at other traders' expense. On Solana, the MEV supply chain works as follows:

1. **You submit a swap** through an RPC endpoint
2. **Searchers observe** your transaction (via RPC forwarding, block engine access, or leader TPU sniffing)
3. **Searcher constructs a profitable bundle** (e.g., sandwich your swap)
4. **Bundle submitted to Jito block engine** with a tip to the validator
5. **Validator includes the bundle** in the block, earning the tip
6. **You receive worse execution**; the searcher profits the difference

### How Solana MEV Differs from Ethereum

| Aspect | Ethereum | Solana |
|--------|----------|--------|
| Block time | 12 seconds | ~400ms slots |
| Mempool | Public mempool | No mempool (but tx visible in transit) |
| Ordering | Proposer-builder separation (PBS) | Jito block engine (~85%+ validators) |
| Bundle system | Flashbots bundles | Jito bundles with tips |
| MEV cost | Gas priority fees | Jito tips (SOL) |
| Latency pressure | Moderate | Extreme (sub-100ms decisions) |

Key Solana-specific factors:
- **No public mempool**: Transactions flow RPC → TPU → Leader, but searchers tap into this flow via Jito's block engine and modified validators
- **Known leader schedule**: The leader (block producer) schedule is known ~2 epochs ahead, letting searchers target specific leaders
- **Jito dominance**: ~85%+ of validators run the Jito-modified client, making Jito bundles the primary MEV vector
- **Speed**: 400ms slots mean MEV bots must operate in microseconds, favoring co-located infrastructure

## MEV Types on Solana

### 1. Sandwich Attacks

The most common MEV attack against retail traders.

**Mechanics:**
```
1. Attacker sees your pending swap: Buy 10 SOL worth of TOKEN_X
2. Front-run:  Attacker buys TOKEN_X first  → price rises
3. Your swap:  You buy TOKEN_X at higher price → worse execution
4. Back-run:   Attacker sells TOKEN_X         → profits the difference
```

**Your loss** = price impact from front-run + attacker's profit margin
**Attacker profit** = your_loss - jito_tip - transaction_fees

**Risk factors:**
- Trade size: Larger trades = more profitable to sandwich
- Token liquidity: Illiquid tokens = easier price manipulation
- Slippage setting: Wide slippage = more room for the attacker
- Pool type: CPMM pools more vulnerable than CLMM pools at concentrated ranges
````
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