Analyze Nixtla baseline forecasting results (sMAPE/MASE on M4 or other benchmark datasets). Use when the user asks about baseline performance, model comparisons, or metric interpretation for Nixtla time-series experiments. Trigger with "baseline review", "interpret sMAPE/MASE", or "compare AutoETS vs AutoTheta".
Provide expert guidance on skills architecture, YAML frontmatter, tool permissions, and debugging. Use when creating, troubleshooting, or validating skills. Trigger with "skill not loading", "frontmatter", or "allowed-tools".
Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Trigger with "detect anomalies", "find outliers", "anomaly detection".
Performs rigorous time series cross-validation using expanding and sliding windows. Use when needing to evaluate the performance of time series models on unseen data. Trigger with "cross validate time series", "evaluate forecasting model", "time series backtesting".
Assists users in migrating their codebase and data pipelines from TimeGPT-1 to TimeGPT-2. Use when upgrading to the latest version of TimeGPT, ensuring compatibility, and optimizing performance. Trigger with "migrate to TimeGPT-2", "upgrade TimeGPT", "TimeGPT compatibility".
Quantifies prediction uncertainty using conformal prediction. Use when risk assessment, scenario planning, or decision-making under uncertainty is required. Trigger with "quantify uncertainty", "generate prediction intervals", "confidence bands".
Fine-tunes TimeGPT on custom datasets to improve forecasting accuracy. Use when TimeGPT's zero-shot performance is insufficient or domain-specific accuracy is needed. Trigger with "finetune TimeGPT", "train TimeGPT", "adapt TimeGPT".
Detects arbitrage opportunities between Polymarket and Kalshi prediction markets. Use when a user wants to find price discrepancies for the same event on different platforms. Trigger with "find arbitrage", "detect market inefficiencies", "compare Polymarket and Kalshi prices".
Forecasts multiple time series in parallel batches using TimeGPT API. Optimizes throughput with rate limiting and supports portfolio aggregation. Use when processing 10-100+ contracts or needing efficient multi-series forecasting. Trigger with "batch forecast", "portfolio forecast", "parallel forecasting".
Transforms prediction market data to Nixtla format (uniqueid, ds, y). Maps arbitrary column names to required schema. Validates date and numeric types. Use when preparing prediction market datasets for Nixtla forecasting tools. Trigger with "convert to Nixtla format", "schema mapping", "transform data".
Analyzes multi-contract correlations and generates hedge recommendations. Use when managing a portfolio of correlated assets and needing to mitigate risk. Trigger with "analyze correlations", "suggest hedge", "portfolio risk assessment".
Quantifies the impact of exogenous events on contract prices using TimeGPT and CausalImpact. Triggers on "event impact analysis", "model event effects", "quantify event impact", or "causal analysis".
Validates time series forecast quality metrics by comparing current performance against historical benchmarks. Detects degradation in MASE and sMAPE metrics. Activates when user mentions "validate forecast", "check forecast quality", or "assess forecast metrics".
Forecasts orderbook depth and spreads to optimize trade execution timing. Use when needing to estimate market liquidity for large orders. Trigger with "forecast liquidity", "predict orderbook", "estimate depth".
Analyzes market risk by calculating VaR, volatility, drawdown, and position sizing. Use when assessing investment risk, managing portfolios, or determining position sizes. Trigger with "analyze market risk", "calculate portfolio risk", "determine position size".
Automatically selects the best forecasting model between StatsForecast and TimeGPT based on time series data characteristics. Use when unsure which model performs best. Trigger with "auto-select model", "choose best model", "model selection".
Validate skills and plugins with deterministic evidence bundles and strict schema gates. Use when auditing changes or enforcing compliance. Trigger with 'run validation' or 'audit validators'.
Guides TimeGPT lab environment setup including Python dependencies, API key configuration, smoke testing, experiment workflows, and optional CI/CD integration. Inspects environment docs and scripts to provide step-by-step setup instructions, troubleshooting guidance, and onboarding for new developers. Use when setting…
Scaffolds production-ready forecasting experiments with Nixtla libraries. Creates configuration files, experiment harnesses, multi-model comparisons, and cross-validation workflows for StatsForecast, MLForecast, and TimeGPT. Activates when user needs experiment setup, forecasting pipeline creation, model benchmarking…
Transforms forecasting experiments into production-ready inference pipelines with Airflow, Prefect, or cron orchestration. Generates ETL tasks, monitoring, error handling, and deployment configs. Activates when user needs to deploy forecasts to production, schedule batch inference, operationalize models, or create…
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