Using AI assistants with skforecast

Skforecast provides machine-readable context files so that AI coding assistants (ChatGPT, Claude, Gemini, GitHub Copilot, Cursor, and others) can generate accurate, up-to-date code for time series forecasting.

Quick start: provide the context to an LLM

If your AI assistant can access web pages, paste the following URL into the chat:

https://skforecast.org/latest/llms-full.txt

If your assistant cannot browse URLs, open the file and paste or upload its contents instead.

This single file contains the core API reference, forecaster APIs, workflow examples, and best practices. It gives AI assistants enough context to help you with skforecast while reducing hallucinations about deprecated methods or wrong parameter names.

Example prompt:

Using the context from https://skforecast.org/latest/llms-full.txt, create a ForecasterRecursiveMultiSeries with LightGBM, fit it on a DataFrame with 3 series, and run backtesting with conformal prediction intervals.

IDE integration

If you clone or open the skforecast repository in an AI-enabled IDE that supports repository context files, the assistant can load the relevant context from the repo:

IDE / Tool	File loaded	How
VS Code + GitHub Copilot	.github/copilot-instructions.md	Used as repository instructions by Copilot
Claude Code	AGENTS.md	Read from the project root
OpenAI Codex / Aider	AGENTS.md	Read when the tool supports the AGENTS.md convention
Cursor	AGENTS.md	Can be used as project context when supported or configured

These files contain the same core content: project structure, all forecasters, code style, and testing conventions.

What's included in the context

The AI context covers:

• Choice of forecaster — when to use ForecasterRecursive vs ForecasterRecursiveMultiSeries vs ForecasterRnn vs ForecasterFoundation, etc.
• All 9 forecaster types — constructors, fit(), and predict() methods including parameters and defaults.
• Model selection — backtesting_forecaster, bayesian_search_forecaster and other hyperparameter optimization methods, TimeSeriesFold, OneStepAheadFold, and their multi-series variants.
• Statistical models — Arima, Sarimax, Ets, Arar wrapped by ForecasterStats.
• Deep learning — ForecasterRnn with create_and_compile_model, LSTM/GRU architectures.
• Foundation models (zero-shot) — FoundationModel + ForecasterFoundation with Chronos-2, TimesFM 2.5, Moirai-2, and TabICL backends.
• Feature engineering — RollingFeatures, feature_engine calendar/cyclical features, custom features, and exogenous variables.
• Feature selection — RFECV, SelectFromModel for lags, window features, and exogenous variables.
• Drift detection — RangeDriftDetector and PopulationDriftDetector for production monitoring.
• 13 specialized workflow skills — step-by-step guides for common tasks, loaded on-demand by advanced AI agents.

Workflow skills

Skforecast includes 13 modular skills — self-contained guides that AI agents can load on demand when a user asks about a specific topic. Each skill covers a complete workflow with decision trees, code examples, and common pitfalls.

Skill	What it covers
forecasting-single-series	End-to-end forecasting with ForecasterRecursive: data prep, fit, predict, backtest, intervals
forecasting-multiple-series	Global model with ForecasterRecursiveMultiSeries: encoding, dict input, multi-level predictions
statistical-models	ForecasterStats with Arima, Sarimax, Ets, Arar: auto-ARIMA, seasonal config
hyperparameter-optimization	Grid, random, and Bayesian search with TimeSeriesFold and OneStepAheadFold
prediction-intervals	Bootstrapping, conformal prediction, quantile regression, interval calibration
feature-engineering	RollingFeatures, feature_engine calendar/cyclical features, custom features, exogenous variables
feature-selection	RFECV, SelectFromModel: selecting lags, window features, and exog
drift-detection	RangeDriftDetector and PopulationDriftDetector for production monitoring
deep-learning-forecasting	ForecasterRnn with Keras: create_and_compile_model, LSTM/GRU architectures
foundation-forecasting	Zero-shot forecasting with ForecasterFoundation: Chronos-2, TimesFM 2.5, Moirai-2, TabICL
choosing-a-forecaster	Decision guide: "I have X situation → use Y forecaster"
troubleshooting-common-errors	Frequent mistakes AI assistants make with skforecast and their corrections
complete-api-reference	Full method signatures and availability matrix for all forecasters

These skills are bundled into llms-full.txt. AI agents that support the Agent Skills spec (such as GitHub Copilot in VS Code) can also load them individually from the skills/ directory.

Context files overview

File	Audience	Description
llms-full.txt	Any LLM user	Complete context: API + 13 workflow skills
llms.txt	LLMs with web search	Public index with links to all documentation sections
.github/copilot-instructions.md	Contributors (VS Code)	Auto-injected into GitHub Copilot
AGENTS.md	Contributors (Claude Code, Codex, Aider)	Standard agent context file

The context files are auto-generated from maintained source files (tools/ai/llms-base.txt, llms.txt, tools/ai/ai_context_header.md, and skills/) to ensure they stay in sync with the library. They are regenerated on every release.

Tips for better results

1. Always provide the context URL — Without it, LLMs may hallucinate methods that don't exist or use outdated API names (e.g., ForecasterAutoreg instead of ForecasterRecursive).
2. Be specific about your forecaster — Mention which forecaster you're using. Parameter names and defaults differ across forecasters.
3. Mention the version — Say "skforecast 0.22.0" so the LLM doesn't mix advice from older versions.
4. Validate the output — AI-generated code is a starting point. Use backtesting or an appropriate holdout evaluation to verify model performance.