菜单
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'.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | nixtla-contract-schema-mapper |
| description | 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'. |
| version | 1.0.0 |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| license | MIT |
| allowed-tools | Read,Write,Bash(python:*),Glob,Grep |
Transforms prediction market data into Nixtla-compatible format (unique_id, ds, y).
Converts prediction market datasets with varying schemas into standardized Nixtla format. Maps arbitrary column names to required schema, validates date parsing and numeric types, produces clean CSV output ready for forecasting. Optional visualization and sample forecast generation.
Required:
pandas, matplotlib packagesOptional (for forecasting):
statsforecast for open-source modelsnixtla for TimeGPT (requires API key)Environment Variables:
NIXTLA_TIMEGPT_API_KEY: Required only if using --timegpt flagInstallation:
pip install pandas matplotlib statsforecast nixtla
Examine your input CSV to identify:
Execute the transformation script:
python {baseDir}/scripts/transform_data.py --input data.csv \
--id_col contract_id --date_col date --target_col price
Available options:
--input: Input CSV file path (required)--id_col: Column name for unique ID (required)--date_col: Column name for date (required)--target_col: Column name for target variable (required)--output: Output file path (default: nixtla_data.csv)--plot: Generate time series visualization--forecast: Run sample forecast after transform--timegpt: Use TimeGPT instead of StatsForecastCheck the transformed data:
head -5 nixtla_data.csv
Expected format:
unique_id,ds,y
contract_1,2024-01-01,0.75
contract_1,2024-01-02,0.78
--plot)Error: Input file not found: data.csv
Solution: Verify file path exists and is readable
Error: Column 'contract_id' not found. Available: [...]
Solution: Use exact column name from the available list
Error: Invalid date format in date column
Solution: Ensure dates use YYYY-MM-DD or standard parseable format
Error: Non-numeric data in target column
Solution: Clean non-numeric values from target column
Error: NIXTLA_TIMEGPT_API_KEY not set
Solution: export NIXTLA_TIMEGPT_API_KEY=your_key or omit --timegpt
python {baseDir}/scripts/transform_data.py \
--input polymarket_prices.csv \
--id_col market_id \
--date_col timestamp \
--target_col last_price
Output:
Transformed data saved to: nixtla_data.csv
Transformation Summary:
Series count: 15
Total rows: 4500
Date range: 2024-01-01 to 2024-06-30
Value range: 0.0100 to 0.9900
python {baseDir}/scripts/transform_data.py \
--input election_contracts.csv \
--id_col candidate_id \
--date_col date \
--target_col probability \
--plot \
--forecast
{baseDir}/scripts/transform_data.pyGenerate and configure Nixtla Skills using the CLI for forecasting workflows. Use when installing or updating skills. Trigger with 'install nixtla skills' or 'update nixtla'.
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