nixtla-timegpt-finetune-lab
Configure TimeGPT fine-tuning on custom datasets with Nixtla SDK. Use when training domain-specific forecast models. Trigger with 'fine-tune TimeGPT' or 'train custom model'.
npx skills add majiayu000/claude-skill-registry --skill nixtla-timegpt-finetune-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 Fine-Tuning Lab Guide users through production-ready TimeGPT fine-tuning workflows. ## Overview This skill manages TimeGPT fine-tuning: - **Dataset preparation**: Validate and format training data - **Job submission**: Submit fine-tuning jobs to TimeGPT API - **Status monitoring**: Track job progress until completion - **Model comparison**: Compare zero-shot vs fine-tuned performance ## Prerequisites **Required**: - Python 3.8+ - `nixtla` package - `NIXTLA_API_KEY` environment variable **Installation**: ```bash pip install nixtla pandas utilsforecast export NIXTLA_API_KEY='your-api-key' ``` **Get API Key**: https://dashboard.nixtla.io ## Instructions ### Step 1: Prepare Dataset Ensure data is in Nixtla schema: ```bash python {baseDir}/scripts/prepare_finetune_data.py \ --input data/sales.csv \ --output data/finetune_train.csv ``` ### Step 2: Configure Fine-Tuning ```bash python {baseDir}/scripts/configure_finetune.py \ --train data/finetune_train.csv \ --model_name "sales-model-v1" \ --horizon 14 \ --freq D ``` ### Step 3: Submit Job ```bash python {baseDir}/scripts/submit_finetune.py \ --config forecasting/finetune_config.yml ``` ### Step 4: Monitor Progress ```ba
- Overview
- Prerequisites
- Instructions
- Step 1: Prepare Dataset
- Step 2: Configure Fine-Tuning
- Step 3: Submit Job
- Step 4: Monitor Progress
- Step 5: Compare Models
- Output
- Error Handling
- Examples
- Example 1: Basic Fine-Tuning
- Example 2: Compare Zero-Shot vs Fine-Tuned
- Resources
pip install nixtla pandas utilsforecast
export NIXTLA_API_KEY='your-api-key'
python {baseDir}/scripts/prepare_finetune_data.py \
python {baseDir}/scripts/configure_finetune.py \
python {baseDir}/scripts/submit_finetune.py \
python {baseDir}/scripts/monitor_finetune.py \
python {baseDir}/scripts/compare_finetuned.py \
Prepare data
Submit jobWhat does the nixtla-timegpt-finetune-lab skill do?
Configure TimeGPT fine-tuning on custom datasets with Nixtla SDK. Use when training domain-specific forecast models. Trigger with 'fine-tune TimeGPT' or 'train custom model'.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill nixtla-timegpt-finetune-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.