Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Covers the end-to-end pipeline for preparing time series datasets from Kaggle and uploading them to the Data Annotation platform at data.smlcrm.com. Handles CSV cleaning with pandas, metadata configuration, column type and unit assignment, and group tagging until the dataset reaches CLEAN status.
Combines dataset discovery, cleaning, and platform-specific annotation configuration into one repeatable workflow instead of handling each step ad hoc.
When to use it
- Preparing Kaggle energy datasets for ML annotation
- Configuring time/target/covariate columns in data.smlcrm.com
- Assigning group tags across all variables in a dataset
- Cleaning raw industrial CSVs before model training
- Uploading manufacturing or climate data with full metadata
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Prepare the steel industry energy consumption dataset from Kaggle for annotation
- 1Downloads CSV from kaggle.com/datasets/csafrit2/steel-industry-energy-consumption
- 2Runs scripts/clean_dataset.py to strip whitespace, remove duplicates, fill missing values, and parse timestamps
- 3Uploads the raw CSV to data.smlcrm.com with name, domain, source URL, and description
- 4Configures each column with its type (Time, Target, Covariate, Group) and units (kWh, kVarh, tCO2, ratio, seconds)
- 5Selects all target and covariate variables, applies all group tags (WeekStatus, Day_of_week, Load_Type), then uploads the cleaned file
Dataset status changes from RAW to CLEAN with all columns typed, units set, and group assignments complete
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.