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Found 6 Skills
Parse ebooks, extract concepts and entities with citation traceability, classify by type/layer, and synthesize across book collections.
Deep dive into a book. Collect information from six dimensions including chapter structure, background key points, problem impacts, solutions, term index, and further reading through parallel sub-agents, then output a Markdown deep learning note after cross-analysis. Trigger words: Analyze the book XX, Study XX, Reading notes for XX, book analysis.
Best practices for creating comprehensive Jupyter notebook data analyses with statistical rigor, outlier handling, and publication-quality visualizations
Read Hyperliquid (app.hyperliquid.xyz) perp + spot market data via opencli (read-only, public info API). Use whenever the user wants Hyperliquid perpetual or spot markets, mark/oracle/mid prices, 24h change, funding rates (hourly or annualized APR), open interest, volume, the L2 order book, OHLCV candles, historical funding, or a cross-venue funding comparison (Hyperliquid vs Binance vs Bybit) for funding arbitrage. Triggers: "Hyperliquid funding for BTC", "HL perp markets", "funding on BTC perp", "Hyperliquid order book", "HL open interest", "funding arb Hyperliquid vs Binance", "Hyperliquid candles for SOL", "Hyperliquid spot markets", "PURR price on Hyperliquid", "hyperliquid", "hyperliquid.xyz", "HL DEX". READ-ONLY market data — no account, order, or trade operations.
Use this skill whenever the user wants to find trading opportunities, detect arbitrage, analyze a market, perform edge detection, find mispricing, do probability analysis, evaluate orderbook depth, find momentum signals, or assess Polymarket market quality. Triggers: "find opportunities", "detect arbitrage", "analyze market", "edge detection", "mispricing", "probability analysis", "orderbook analysis", "momentum scanner", "market inefficiency", "price gap", "volume surge", "trading edge", "market analysis".
Find Cross-Exchange Market Making (XEMM) opportunities by comparing order book depth and liquidity across exchanges. Identifies pairs where one exchange has thin liquidity (ideal for quoting) and another has deep liquidity (ideal for hedging).