Claude-code-plugins-plus-skills simulating-flash-loans

install
source · Clone the upstream repo
git clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/jeremylongshore/claude-code-plugins-plus-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/crypto/flash-loan-simulator/skills/simulating-flash-loans" ~/.claude/skills/jeremylongshore-claude-code-plugins-plus-skills-simulating-flash-loans && rm -rf "$T"
manifest: plugins/crypto/flash-loan-simulator/skills/simulating-flash-loans/SKILL.md
source content

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:
    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:
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py arbitrage ETH USDC 100 --compare-providers
    
  3. Analyze liquidation profitability on lending protocols:
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py liquidation \
      --protocol aave --health-factor 0.95
    
  4. Simulate triangular arbitrage with multi-hop circular paths:
    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:
    python ${CLAUDE_SKILL_DIR}/scripts/flash_simulator.py arbitrage ETH USDC 100 --risk-analysis
    
  6. Run full analysis combining all features:
    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

ErrorCauseSolution
RPC Rate LimitToo many requestsSwitch to backup endpoint or wait
Stale PricesData older than 30sAuto-refreshes with warning
No Profitable RouteAll routes lose after costsTry different pairs or amounts
Insufficient LiquidityTrade exceeds pool depthReduce amount or split across pools

See

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

Examples

Basic arbitrage simulation:

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

Find cheapest provider:

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

Liquidation opportunity scan:

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