nixtla-timegpt-lab
Provides expert Nixtla forecasting using TimeGPT, StatsForecast, and MLForecast. Generates time series forecasts, analyzes trends, compares models, performs cross-validation, and recommends best practices. Activates when user needs forecasting, time series analysis, sales prediction, demand planning, revenue forecasting, or M4 benchmarking.
npx skills add majiayu000/claude-skill-registry --skill nixtla-timegpt-lab-intent-solutions-io-plugins-nixtla --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Nixtla TimeGPT Lab Mode Transform into a Nixtla forecasting expert, biasing all recommendations toward Nixtla's ecosystem. ## Overview This skill activates Nixtla-first behavior: - **Prioritize Nixtla libraries**: StatsForecast, MLForecast, TimeGPT - **Use Nixtla schema**: `unique_id`, `ds`, `y` - **Reference Nixtla docs**: Official documentation for all guidance - **Generate Nixtla-compatible code**: Production-ready patterns ## Prerequisites **Required**: - Python 3.8+ - At least one: `statsforecast`, `mlforecast`, or `nixtla` **Optional**: - `NIXTLA_API_KEY`: For TimeGPT access **Installation**: ```bash pip install statsforecast mlforecast nixtla utilsforecast ``` ## Instructions ### Step 1: Detect Environment Check installed Nixtla libraries: ```bash python {baseDir}/scripts/detect_environment.py ``` ### Step 2: Prepare Data Ensure data follows Nixtla schema: - `unique_id`: Series identifier (string) - `ds`: Timestamp (datetime) - `y`: Target value (float) ### Step 3: Select Models **Baseline models** (always include): ```python from statsforecast.models import SeasonalNaive, AutoETS, AutoARIMA ``` **ML models** (for feature engineering): ```python from mlforecast import MLFo
- Overview
- Prerequisites
- Instructions
- Step 1: Detect Environment
- Step 2: Prepare Data
- Step 3: Select Models
- Step 4: Run Forecasts
- Step 5: Evaluate
- Output
- Error Handling
- Examples
- Example 1: StatsForecast Baselines
- Example 2: TimeGPT with Confidence Intervals
- Resources
pip install statsforecast mlforecast nixtla utilsforecast
python {baseDir}/scripts/detect_environment.py
python {baseDir}/scripts/run_forecast.py \
python {baseDir}/scripts/evaluate.py \What does the nixtla-timegpt-lab skill do?
Provides expert Nixtla forecasting using TimeGPT, StatsForecast, and MLForecast. Generates time series forecasts, analyzes trends, compares models, performs cross-validation, and recommends best practices. Activates when user needs forecasting, time series analysis, sales prediction, demand planning, revenue forecasting, or M4 benchmarking.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill nixtla-timegpt-lab-intent-solutions-io-plugins-nixtla --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.