数据分析、机器学习、LLM 应用、提示词设计（9032 个）
Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.
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Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.
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Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.
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Quantize diffusion transformers with NVIDIA ModelOpt, using FP8 or NVFP4 lower-precision numbers, and prepare the saved weights for SGLang Diffusion. Follow a workflow for loading, checking output quality, and comparing speed under matching conditions.
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Choose SGLang Diffusion settings for a specific model, GPU, and memory budget. Compare generation speed, memory use, and output quality to select command-line options, including CPU offloading, multiple GPUs, caching, and reduced precision.
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Retrieve cryptocurrency prices, candlesticks, order books, funding rates, and open interest from OKX’s public API. Use spot, derivatives, and index data for market research without registering an account or configuring authentication.
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Turn broker trade exports into a report on holding periods, trading frequency, win rate, and realized profit or loss. Diagnose patterns such as holding losers longer, overtrading, chasing rising prices, and fixation on particular price levels, with numerical evidence.
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Provides guidance for retrieving China A-share prices and trading volumes through the TDX protocol. The source is currently retired from automatic routing because servers stopped responding, with explicit selection retained for possible future recovery.
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Analyze crypto perpetual futures funding payments and spot–futures price differences to assess carry trades. Compare exchanges, identify crowded positions, and summarize potential returns alongside risks such as funding reversals and liquidation.
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Browse registered trading strategies and compare evidence from backtests, which simulate trades on historical data. Filter by market conditions, trade counts, risk-adjusted returns, and cost feasibility, inspect evidence age, and rebuild the evidence cache from local runs.
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Analyze price and volume data using trend indicators, overbought and oversold measures, and volume changes. Combine these three perspectives into a single long, short, or stand-aside signal, with adjustable calculation periods and thresholds.
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Generate trading signals from historical volatility, a measure of past price fluctuations. Rank its current level against recent history, signal long positions at low levels, and exit or short at high levels while retaining positions between thresholds.
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Compare Chinese A-share sectors using business conditions, price trends, valuation, and fund flows. The skill provides scoring and ranking frameworks to organize sector allocation proposals and trace how demand or cost changes affect connected industries.
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Backtest stock and cryptocurrency option strategies using theoretical prices derived from underlying prices. Simulate combinations of option positions, track profit and loss, and export Greeks, measures of sensitivity to price, volatility, and time.
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Generates buy, sell, or stand-aside signals from calendar patterns using price and volume data. Supports configurable months and weekdays, including combined conditions, for testing seasonal trading ideas against historical data.
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Retrieve U.S. company filings and financial series from SEC EDGAR. Resolve stock tickers to company identifiers, list recent reports with document links, and fetch reported metrics such as revenue or assets without an API key.
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Finds and retrieves paid financial datasets when free sources lack coverage or detail. Guides comparison of providers, success rates, prices, and required inputs, while keeping execution costs and remaining credits visible.
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Rank stocks using several indicators, such as recent returns and trading volume, combined on a common scale. Build equally weighted portfolios from the highest scores, or combine existing factors into strategies with buying and selling positions.
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Generate trading signals using Smart Money Concepts, a framework for interpreting institutional trading behavior. Analyze trend continuation, reversals, price gaps, and order concentration zones, then combine structural signals and gaps to determine a trading direction.
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Assess market sentiment using fear and greed measures, options activity, margin trading, foreign investment flows, and social media discussion. Combine indicators on a common scale to produce a dashboard and explain signs of excessive optimism or fear.
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Retrieve price and trading volume data for US, Hong Kong, and Canadian stocks, ETFs, and indices through Yahoo Finance. Also gather company profiles, financial statements, dividends, and ownership data for market research and historical strategy testing.
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Build a pair trading strategy for two related instruments using their price ratio. Measure how far the ratio moves from its recent average, generate opposite buying and selling signals, and close positions when the ratio returns near that average.
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Analyze investment returns and portfolio risk using loss thresholds, average losses beyond those thresholds, and peak-to-trough declines. Combine simulated price paths with historical and hypothetical market shocks to produce a risk report and control recommendations.
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Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.
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