plugins-nixtla

Generate and configure Nixtla Skills using the CLI for forecasting workflows. Use when installing or updating skills. Trigger with 'install nixtla skills' or 'update nixtla'.

Generate benchmarking pipelines to compare forecasting models and summarize accuracy/speed trade-offs. Use when evaluating TimeGPT vs StatsForecast/MLForecast/NeuralForecast on a dataset. Trigger with "benchmark models", "compare TimeGPT vs StatsForecast", or "model selection".

Research and summarize Nixtla ecosystem updates and time-series forecasting content from the web and GitHub. Use when gathering release notes, recent changes, or best-practice references. Trigger with "Nixtla updates", "what's new with TimeGPT", or "find time-series papers".

Generate production-ready TimeGPT forecasting pipeline code from requirements. Use when scaffolding a pipeline with validation, logging, visualization, and repeatable runs. Trigger with "create TimeGPT pipeline", "build TimeGPT integration", or "generate forecast code".

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".

Orchestrate 6-phase changelog generation workflow with AI synthesis, multi-source data fetching (GitHub/Slack/Git), quality validation, and automated PR creation. Use when automating release notes, weekly changelogs, or documentation updates. Trigger with "generate changelog", "weekly changelog", or "automate release notes".

Analyze and explain TimeGPT forecast results in plain English. Generates executive summaries with driver analysis. Use when stakeholders need forecast explanations, board presentations, or compliance documentation. Trigger with "explain forecast", "why is the forecast", "forecast narrative".

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".

Generate production-ready plugin structures from PRD documents with enterprise-compliant files. Use when scaffolding new plugins, converting PRDs to plugin skeletons, or initializing plugin projects. Trigger with 'scaffold plugin', 'create plugin from PRD', or 'initialize plugin structure'.

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'.

Produces verified datasets, verified evaluation results, and a deployable contract bundle for a workflow. Use when you need provable correctness at data and evaluation boundaries. Trigger with 'verify workflow', 'validate contract', or 'run verification pipeline'.

Generate comprehensive markdown benchmark reports from forecast accuracy metrics with model comparisons, statistical analysis, and regression detection. Use when analyzing baseline performance, comparing forecast models, or validating model quality. Trigger with 'generate benchmark report', 'analyze forecast metrics', or 'create performance summary'.

Generate production-ready Jupyter notebooks showcasing Nixtla forecasting workflows for statsforecast, mlforecast, and TimeGPT. Use when creating demos, building examples, or showcasing forecasting capabilities. Trigger with 'generate demo notebook', 'create Jupyter demo', or 'build forecasting example'.

Generate production-ready MCP server implementations from PRD tool specifications with schema validation, error handling, and testing infrastructure. Use when building MCP servers for Nixtla plugins, implementing tool handlers, or scaffolding server infrastructure. Trigger with 'build MCP server', 'generate MCP implementation', or 'scaffold MCP tools'.

Transform PRD documents into actionable implementation tasks with TodoWrite integration. Use when planning development work, converting requirements to tasks, or creating implementation roadmaps. Trigger with 'PRD to tasks', 'plan implementation from PRD', or 'create task list'.

Generate comprehensive pytest test suites from PRD functional requirements with fixtures, parameterization, and coverage tracking. Use when creating tests for new plugins, validating PRD requirements, or scaffolding test infrastructure. Trigger with 'generate tests from PRD', 'create test suite', or 'scaffold pytest tests'.

Validate Nixtla 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'.

Detect arbitrage opportunities between Polymarket and Kalshi using forecast analysis. Use when finding price discrepancies across platforms. Trigger with 'find arbitrage' or 'compare market prices'.

Transform prediction market data to Nixtla format (unique_id, ds, y). Use when preparing datasets for forecasting. Trigger with 'convert to Nixtla format' or 'schema mapping'.

Analyze causal impact of events on time series forecasts using TimeGPT. Use when quantifying promotion or disaster effects. Trigger with 'event impact analysis' or 'causal analysis'.

Incorporates external variables (holidays, weather, events) into TimeGPT forecasts to improve accuracy. Use when forecasts require external data, holidays impact sales, or weather affects demand. Trigger with 'include holidays', 'add weather data', 'integrate events'.

Generate production-ready forecasting experiments with StatsForecast and TimeGPT. Use when setting up model benchmarking or cross-validation. Trigger with 'scaffold experiment' or 'compare models'.

Validate forecast quality by comparing MASE and sMAPE against benchmarks. Use when detecting model degradation. Trigger with 'validate forecast' or 'check forecast quality'.

Analyze and forecast Polymarket contracts using TimeGPT with confidence intervals. Use when predicting contract prices. Trigger with 'Polymarket analysis' or 'forecast prediction market'.

Transform forecasting experiments into Airflow/Prefect pipelines with monitoring. Use when deploying forecasts to production. Trigger with 'generate pipeline' or 'create Airflow DAG'.

Transform data sources to Nixtla schema (unique_id, ds, y) with column inference. Use when preparing data for forecasting. Trigger with 'map to Nixtla schema' or 'transform data'.

Analyze and categorize installed Nixtla Skills for forecasting guidance. Use when seeing available skills or selecting the right one. Trigger with 'list nixtla skills' or 'what skills'.

Generate time series forecasts using TimeGPT, StatsForecast, and MLForecast. Use when forecasting, demand planning, or model comparison is needed. Trigger with 'forecast time series' or 'run Nixtla forecast'.

Analyze and transform code for TimeGPT-1 to TimeGPT-2 migration. Use when upgrading TimeGPT version. Trigger with 'migrate to TimeGPT-2' or 'upgrade TimeGPT'.

Analyze forecast uncertainty using conformal prediction. Use when risk assessment or scenario planning is required. Trigger with 'quantify uncertainty' or 'prediction intervals'.

Multi-phase release validation workflow for nixtla. Analyzes git changes, predicts test impact, assesses risk, runs pytest verification, provides go/no-go recommendation. Trigger: "validate release", "run release validation", "check release readiness"

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".

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".

Incorporates external variables (holidays, weather, events) into TimeGPT forecasts to improve accuracy. Use when forecasts require external data, holidays impact sales, or weather affects demand. Trigger with "include holidays", "add weather data", "integrate events".

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".

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".

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 production pipelines.

Enables TimeGPT model fine-tuning on custom datasets with Nixtla SDK. Guides dataset preparation, job submission, status monitoring, model comparison, and accuracy benchmarking. Activates when user needs TimeGPT fine-tuning, custom model training, domain-specific optimization, or zero-shot vs fine-tuned comparison.

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".