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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'.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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".
| name | nixtla-experiment-architect |
| description | 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'. |
| allowed-tools | Read,Write,Glob,Grep,Edit |
| version | 1.0.0 |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| license | MIT |
Design and scaffold complete forecasting experiments using Nixtla's libraries.
This skill creates production-ready experiment harnesses:
Required:
statsforecast, utilsforecastOptional:
mlforecast: For ML modelsnixtla: For TimeGPTNIXTLA_API_KEY: TimeGPT accessInstallation:
pip install statsforecast mlforecast nixtla utilsforecast pyyaml
Collect experiment parameters:
python {baseDir}/scripts/generate_config.py \
--data data/sales.csv \
--target sales \
--horizon 14 \
--freq D \
--output forecasting/config.yml
python {baseDir}/scripts/scaffold_experiment.py \
--config forecasting/config.yml \
--output forecasting/experiments.py
python forecasting/experiments.py
cat forecasting/results/metrics_summary.csv
Error: Data file not found
Solution: Verify data source path in config
Error: Column not found
Solution: Check column names match your data
Error: Missing required package
Solution: Install missing dependencies with pip
Error: Cross-validation failed
Solution: Ensure enough data for n_windows
python {baseDir}/scripts/generate_config.py \
--data data/sales.csv \
--target revenue \
--horizon 30 \
--freq D \
--id_col store_id
Output config.yml:
data:
source: data/sales.csv
target: revenue
unique_id: store_id
forecasting:
horizon: 30
freq: D
models:
- SeasonalNaive
- AutoETS
- AutoARIMA
python {baseDir}/scripts/generate_config.py \
--data data/energy.csv \
--target consumption \
--horizon 24 \
--freq H
{baseDir}/scripts/{baseDir}/assets/templates/Related Skills:
nixtla-timegpt-lab: Core forecasting guidancenixtla-schema-mapper: Data transformationnixtla-prod-pipeline-generator: Production deployment