Agent skill

simulating-flash-loans

Simulate flash loan strategies with profitability calculations and risk assessment across Aave, dYdX, and Balancer. Use when simulating flash loans, analyzing arbitrage profitability, evaluating liquidation opportunities, or comparing flash loan providers. Trigger with phrases like "simulate flash loan", "flash loan arbitrage", "liquidation profit", "compare Aave dYdX", "flash loan strategy", or "DeFi arbitrage simulation".

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/crypto/flash-loan-simulator/skills/simulating-flash-loans

SKILL.md

Simulating Flash Loans

Contents

Overview | Prerequisites | Instructions | Output | Error Handling | Examples | Resources

Overview

Simulate flash loan strategies across Aave V3, dYdX, and Balancer with profitability calculations, gas cost estimation, and risk assessment. Evaluate flash loan opportunities without executing real transactions.

Prerequisites

  1. Install Python 3.9+ with web3, httpx, and rich packages
  2. Configure RPC endpoint access (free public RPCs via https://chainlist.org work fine)
  3. Optionally add Etherscan API key for better gas estimates
  4. Set RPC in ${CLAUDE_SKILL_DIR}/config/settings.yaml or use ETH_RPC_URL env var

Instructions

  1. Simulate a two-DEX arbitrage with automatic fee and gas calculation:
    bash
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py arbitrage ETH USDC 100 \
  --dex-buy uniswap --dex-sell sushiswap
  2. Compare flash loan providers to find the cheapest for your strategy:
    bash
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py arbitrage ETH USDC 100 --compare-providers
  3. Analyze liquidation profitability on lending protocols:
    bash
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py liquidation \
  --protocol aave --health-factor 0.95
  4. Simulate triangular arbitrage with multi-hop circular paths:
    bash
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py triangular \
  ETH USDC WBTC ETH --amount 50
  5. Add risk assessment (MEV competition, execution, protocol, liquidity) to any simulation:
    bash
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py arbitrage ETH USDC 100 --risk-analysis
  6. Run full analysis combining all features:
    bash
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py arbitrage ETH USDC 100 \
  --full --output json > simulation.json

Output

  • Quick Mode: Net profit/loss, provider recommendation, Go/No-Go verdict
  • Breakdown Mode: Step-by-step transaction flow with individual cost components
  • Comparison Mode: All providers ranked by net profit with fee differences
  • Risk Analysis: Competition, execution, protocol, and liquidity scores (0-100) with viability grade (A-F)

See ${CLAUDE_SKILL_DIR}/references/implementation.md for detailed output examples and risk scoring methodology.

Error Handling

Error Cause Solution
RPC Rate Limit Too many requests Switch to backup endpoint or wait
Stale Prices Data older than 30s Auto-refreshes with warning
No Profitable Route All routes lose after costs Try different pairs or amounts
Insufficient Liquidity Trade exceeds pool depth Reduce amount or split across pools

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.

Examples

Basic arbitrage simulation:

bash
python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py arbitrage ETH USDC 100 \
  --dex-buy uniswap --dex-sell sushiswap

Find cheapest provider:

bash
python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py arbitrage ETH USDC 100 --compare-providers

Liquidation opportunity scan:

bash
python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py liquidation --protocol aave --health-factor 0.95

See ${CLAUDE_SKILL_DIR}/references/examples.md for multi-provider comparison and backtesting examples.

Resources

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