The Best Finance & Trading Skills for Claude Code (Crypto, DeFi, Market Data)
Finance and trading skills have the highest security stakes of any category — these skills touch your keys, your positions, and your money. The evaluation weighted the security dimension accordingly. Ten skills made the cut.
The finance category is crypto-heavy, and that's not an accident: crypto is where the APIs are public, the data is free, and the workflows are code-native. Traditional finance hides behind terminals and data subscriptions; DeFi is just JSON endpoints and programmatic execution.
That also means the stakes are real. The security dimension matters more here than anywhere else in the directory — and every skill below passed a scan of what it reads, where it sends data, and what it can touch.
1. dex-execution — 9.3/10
Solana DEX swap execution via the Jupiter aggregator — quoting, routing, execution.
Why: The engineering is careful with exactly the right things: it separates quoting from execution, confirms before signing, and never holds your keys. You see the route and the price impact before anything moves.
The one thing: It executes; it doesn't decide. Whether the trade is a good idea remains your problem.
2. coingecko-api — 9.1/10
Broad crypto market data from CoinGecko — 13,000+ tokens, prices, volumes, market caps.
Why: The best data foundation in the category. Free, no-auth, and comprehensive — the skill wraps it with clean query patterns so your agent stops guessing at endpoint parameters.
The one thing: Free-tier rate limits are real. High-frequency polling will get throttled.
3. jito-bundles — 9.1/10
Jito bundle submission for MEV protection on Solana — bundle building, submission, confirmation tracking.
Why: MEV protection is the difference between trading at the price you saw and trading at the price someone extracted from you. The skill handles the bundle mechanics so you don't handcraft transactions that sandwich bots eat alive.
The one thing: Bundle submission has its own failure modes (tips too low, bundle expired). It reports them honestly instead of pretending every submission lands.
4. kalshi-api — 9.1/10
Kalshi exchange mechanics — RSA-PSS authentication, order schema, YES/NO orderbooks.
Why: Kalshi's API is idiosyncratic (that auth scheme isn't standard), and this skill encodes the quirks so your agent doesn't learn them by failing. Prediction markets are growing; this is the on-ramp.
The one thing: Regulatory status varies by jurisdiction. The skill won't stop you where you shouldn't trade.
5. defillama-api — 9.0/10
Free DeFi analytics across all chains — TVL, token prices, DEX volumes, protocol metrics.
Why: DeFi Llama is the reference data source for on-chain activity, and this skill makes it queryable without memorizing the schema. Protocol comparisons come out structured instead of scraped.
The one thing: TVL is a self-reported metric in some corners. The data layer is accurate; what protocols claim isn't always.
6. dexscreener-api — 9.0/10
Multi-chain DEX pair data — prices, volume, liquidity, transactions.
Why: The real-time layer that complements CoinGecko's aggregate view. Pair-level granularity for when you need to know what's happening on THIS pool, right now.
The one thing: New pairs are where rugs live. The data is accurate; the tokens the data describes may not be.
7. exit-strategies — 9.0/10
Systematic exit rules, stop-loss methods, take-profit strategies, and position sizing on the way out.
Why: The methodology entry in the category. Exits are where retail traders lose the most — this skill converts "I'll know when to sell" into rules you can actually follow and backtest.
The one thing: Rules only work if you let them fire. Override the exit rule enough times and you don't have a system.
8. feature-engineering — 9.0/10
Feature construction from market data for ML trading models — returns, volatility, microstructure signals.
Why: Clean, correct feature construction with lookahead-bias checks. Lookahead bias is the bug that makes every backtest look brilliant and every live deployment look tragic — and this skill actively guards against it.
The one thing: Features are model food, not signals. You still need the model, the validation, and the humility.
9. impermanent-loss — 8.9/10
Impermanent loss calculation, modeling, and breakeven analysis for AMM liquidity provision.
Why: IL is the cost LPs pretend doesn't exist. This skill models it properly — against price trajectories, not just static snapshots — so the yield you're earning gets measured against the loss you're absorbing.
The one thing: Models assume clean fee tiers. Novel AMM designs (dynamic fees, concentrated liquidity variants) need manual validation.
10. dex-pool-analysis — 8.8/10
AMM pool mechanics comparison across Raydium, Orca, and Meteora — fees, depth, slippage behavior.
Why: The Solana AMM landscape is three-plus serious DEXs with different mechanics. This skill compares them on the dimensions that matter for a given trade size, instead of defaulting to wherever you usually swap.
The one thing: Pool conditions change by the block. Use it for structure understanding, re-quote at execution time.
The pattern
The strongest finance skills share a property the category demands: they separate information from execution and never blur decision from action. Data skills just fetch. Execution skills confirm before they move anything. The methodology skills (exits, features) make your thinking explicit without pretending to trade for you. In a category where a bad skill can cost real money, that separation is the whole game.
Full scorecards — including the security scan of every network call — are on the finance & trading collection page.