OP

Analyze DeFi yield data across protocols to identify high-performing liquidity pools.

Install

mkdir -p .claude/skills/optimizing-defi-yields && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7951" && unzip -o skill.zip -d .claude/skills/optimizing-defi-yields && rm skill.zip

Installs to .claude/skills/optimizing-defi-yields

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Find and compare DeFi yield opportunities across protocols with APY
67 charsno explicit “when” trigger
Beginner

Key capabilities

  • Search for DeFi yield opportunities across chains
  • Filter yield opportunities by minimum TVL, asset type, or protocol
  • Apply risk filters to show audited protocols or specific risk levels
  • Analyze specific yield opportunities with detailed pool breakdowns
  • Compare APY rates across multiple DeFi protocols
  • Export yield results to JSON or CSV formats

How it works

This skill aggregates data from DeFiLlama and other sources to provide APY/APR comparisons and risk assessments. It allows filtering by various criteria and exporting results.

Inputs & outputs

You give it
Chain name, minimum TVL, asset type, protocol names, risk level, output format
You get back
Yield summary table, detailed pool breakdown, risk assessment, or JSON/CSV export

When to use optimizing-defi-yields

  • Search for yield opportunities by chain
  • Compare APY rates across protocols
  • Filter yield farming by minimum TVL
  • Analyze DeFi risk and performance

About this skill

Optimizing DeFi Yields

Overview

Find and compare DeFi yield opportunities across protocols. Aggregates data from DeFiLlama and other sources to provide APY/APR comparisons, risk assessments, and optimization recommendations for yield farming strategies.

Prerequisites

Before using this skill, ensure you have:

  • Python 3.8+ installed
  • Internet access for API queries
  • Understanding of DeFi concepts (APY, APR, TVL, impermanent loss)

Instructions

  1. Search for yield opportunities across all chains or filter by a specific chain:

    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --top 20
    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --chain ethereum --top 10
    
  2. Filter by criteria -- minimum TVL (for safety), asset type, or protocol:

    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --min-tvl 10000000 --top 15  # 10000000 = 10M limit
    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --asset USDC --chain ethereum
    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --protocol aave,compound,curve
    
  3. Apply risk filters -- show only audited protocols or filter by risk level (--risk low, --risk medium, --risk high):

    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --audited-only --min-tvl 1000000  # 1000000 = 1M limit
    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --risk low --min-apy 3
    
  4. Analyze specific opportunities -- get detailed pool breakdown or compare protocols:

    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --pool "aave-v3-usdc-ethereum" --detailed
    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --compare aave,compound,spark --asset USDC
    
  5. Export results to JSON or CSV for further analysis:

    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --top 50 --format json --output yields.json
    python ${CLAUDE_SKILL_DIR}/scripts/yield_optimizer.py --chain ethereum --format csv --output eth_yields.csv
    

Output

Yield Summary Table

==============================================================================
  DEFI YIELD OPTIMIZER                              2026-01-15 15:30 UTC  # 2026 year
==============================================================================

  TOP YIELD OPPORTUNITIES
------------------------------------------------------------------------------
  Protocol       Pool          Chain      TVL        APY    Risk    Score
  Convex        cvxCRV        Ethereum   $450M    12.5%    Low     9.2
  Aave v3       USDC          Ethereum   $2.1B     4.2%    Low     9.8
  Curve         3pool         Ethereum   $890M     3.8%    Low     9.5
  Compound v3   USDC          Ethereum   $1.5B     3.2%    Low     9.6
  Yearn         yvUSDC        Ethereum   $120M     5.1%    Medium  7.8
------------------------------------------------------------------------------

  APY BREAKDOWN (Top Result)
------------------------------------------------------------------------------
  Base APY:     4.5%
  Reward APY:   8.0% (CRV + CVX)
  Total APY:    12.5%
  IL Risk:      None (single-sided)
==============================================================================

Risk Assessment

  RISK ANALYSIS: Convex cvxCRV
------------------------------------------------------------------------------
  Audit Status:    ✓ Audited (Trail of Bits, OpenZeppelin)
  Protocol Age:    3+ years
  TVL:             $450M (stable)
  TVL Trend:       +5% (30d)
  Risk Score:      9.2/10 (Low Risk)

  Risk Factors:
  • Smart contract dependency on Curve
  • CRV/CVX reward token volatility
  • Vote-lock mechanics
==============================================================================

Error Handling

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

Common issues:

  • API timeout: Uses cached data with staleness warning
  • No pools found: Broaden search criteria
  • Invalid protocol: Check supported protocols list

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed usage examples.

Quick Examples

Find stablecoin yields:

python yield_optimizer.py --asset USDC,USDT,DAI --min-tvl 10000000  # 10000000 = 10M limit

Low-risk opportunities:

python yield_optimizer.py --risk low --audited-only --min-apy 2

Multi-chain search:

python yield_optimizer.py --chain ethereum,arbitrum,polygon --top 20

Export top yields:

python yield_optimizer.py --top 100 --format json --output all_yields.json

Configuration

Settings in ${CLAUDE_SKILL_DIR}/config/settings.yaml:

  • Default chain: Primary chain to search
  • Cache TTL: How long to cache API responses
  • Risk weights: Customize risk scoring factors
  • Min TVL default: Default minimum TVL filter

Resources

  • DeFiLlama: https://defillama.com/yields - Yield data source
  • DeFi Safety: - Protocol security scores
  • Impermanent Loss Calculator: Understand LP risks

When not to use it

  • When not interested in DeFi yield farming opportunities
  • When not needing to compare APY/APR rates across protocols
  • When not requiring risk assessments for DeFi investments

Prerequisites

Python 3.8+ installedInternet access for API queriesUnderstanding of DeFi concepts (APY, APR, TVL, impermanent loss)

Limitations

  • Relies on data aggregated from DeFiLlama and other sources
  • API timeout may result in cached data with a staleness warning
  • Requires valid protocol names for filtering

How it compares

This skill automates the process of finding and comparing DeFi yield opportunities, providing structured data and risk analysis that would otherwise require manual research across multiple platforms.

Compared to similar skills

optimizing-defi-yields side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
optimizing-defi-yields (this skill)027dReviewBeginner
quant-analyst1032moNo flagsAdvanced
stock-analyzer712moReviewBeginner
google-analytics436moReviewIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

More by jeremylongshore

View all by jeremylongshore

analyzing-logs

jeremylongshore

Analyze application logs to detect performance issues, identify error patterns, and improve stability by extracting key insights.

14123

ollama-setup

jeremylongshore

Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs. Trigger phrases: "install ollama", "local AI", "free LLM", "self-hosted AI", "replace OpenAI", "no API costs". Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

1167

backtesting-trading-strategies

jeremylongshore

Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".

1071

generating-database-seed-data

jeremylongshore

Process this skill enables AI assistant to generate realistic test data and database seed scripts for development and testing environments. it uses faker libraries to create realistic data, maintains relational integrity, and allows configurable data volumes. u... Use when working with databases or data models. Trigger with phrases like 'database', 'query', or 'schema'.

1033

cursor-codebase-indexing

jeremylongshore

Execute set up and optimize Cursor codebase indexing. Triggers on "cursor index setup", "codebase indexing", "index codebase", "cursor semantic search". Use when working with cursor codebase indexing functionality. Trigger with phrases like "cursor codebase indexing", "cursor indexing", "cursor".

885

testing-mobile-apps

jeremylongshore

Execute mobile app testing on iOS and Android devices/simulators. Use when performing specialized testing. Trigger with phrases like "test mobile app", "run iOS tests", or "validate Android functionality".

810

You might also like

quant-analyst

zenobi-us

Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.

103355

stock-analyzer

FrancyJGLisboa

Provides comprehensive technical analysis for stocks and ETFs using RSI, MACD, Bollinger Bands, and other indicators. Activates when user requests stock analysis, technical indicators, trading signals, or market data for specific ticker symbols.

71214

google-analytics

davila7

Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.

43193

data-engineering

pluginagentmarketplace

ETL pipelines, Apache Spark, data warehousing, and big data processing. Use for building data pipelines, processing large datasets, or data infrastructure.

13192

math-tools

ananddtyagi

Deterministic mathematical computation using SymPy. Use for ANY math operation requiring exact/verified results - basic arithmetic, algebra (simplify, expand, factor, solve equations), calculus (derivatives, integrals, limits, series), linear algebra (matrices, determinants, eigenvalues), trigonometry, number theory (primes, GCD/LCM, factorization), and statistics. Ensures mathematical accuracy by using symbolic computation rather than LLM estimation.

26134

crawl4ai

basher83

This skill should be used when users need to scrape websites, extract structured data, handle JavaScript-heavy pages, crawl multiple URLs, or build automated web data pipelines. Includes optimized extraction patterns with schema generation for efficient, LLM-free extraction.

21137

Search skills

Search the agent skills registry