Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Data Cleaning & Annotation Workflow
Complete workflow for time series datasets (Energy, Manufacturing, Climate) on Kaggle to Data Annotation platform (data.smlcrm.com). Includes downloading, cl...
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Complete workflow for time series datasets (Energy, Manufacturing, Climate) on Kaggle to Data Annotation platform (data.smlcrm.com). Includes downloading, cl...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/platform_guide.md, scripts/clean_dataset.py, scripts/download_kaggle.sh
Validation
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- Review SKILL.md after the package is downloaded.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Deyashmukh
- Source page
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Documentation
Simulacrum Data Annotation Workflow
Complete end-to-end workflow for time series dataset preparation and annotation on the Data Annotation platform (data.smlcrm.com).
What This Skill Does
This skill captures the precise workflow for processing time series datasets (Energy, Manufacturing, Climate) from discovery to CLEAN status: Find Dataset: Search Kaggle for Energy/Manufacturing/Climate time series data Download: Get CSV files via browser or Kaggle CLI Clean: Run Python/pandas script to handle missing values, duplicates, formatting Upload RAW: Upload original CSV with metadata (name, domain, source URL, description) Configure Headers: Set column types (Time, Target, Covariate, Group) and units Assign Groups: Select ALL variables (target + covariates), apply ALL group tags Upload Cleaned: Final upload โ CLEAN status
Supported Domains
Energy: Power consumption, utilities, renewable energy, grid data Manufacturing: Industrial processes, steel production, emissions, equipment data Climate: CO2 emissions, environmental monitoring, weather correlation data
Quick Start
For the full pipeline from Kaggle to annotated dataset: 1. Find dataset on Kaggle 2. Download (browser or kaggle CLI) 3. Clean with scripts/clean_dataset.py 4. Upload RAW dataset to data.smlcrm.com (with metadata) 5. Click "Clean" and upload cleaned file 6. Configure column metadata (types, units) 7. Assign groups to variables 8. Upload cleaned dataset โ CLEAN status
Step 1: Find and Download Dataset
From Kaggle (Browser Method): Navigate to kaggle.com/datasets Search for relevant dataset (e.g., "steel industry energy consumption", "manufacturing emissions", "climate CO2") Review data description, file list, and preview Click "Download" button Extract CSV file from downloaded zip Alternative: Kaggle CLI # Install if needed: pip install kaggle # Configure: kaggle competitions list scripts/download_kaggle.sh <dataset-name> [output-dir] # Example: scripts/download_kaggle.sh csafrit2/steel-industry-energy-consumption
Step 2: Clean the Dataset
Always run the cleaning script before upload: python3 scripts/clean_dataset.py <input.csv> [-o <output.csv>] What the script does: Strips whitespace from column names Removes duplicate rows Fills missing numeric values with median Fills missing categorical values with mode or 'Unknown' Converts timestamp columns to datetime format Outputs column summary for metadata configuration Output: Cleaned CSV file ready for upload Column summary printed to console (save this for metadata config)
Step 3: Upload Raw Dataset to Platform
Navigate to data.smlcrm.com/dashboard Click "Upload Dataset" button Fill in metadata for the RAW dataset: Name: Descriptive dataset name Domain: Category (Energy, Manufacturing, Climate, etc.) Source URL: Kaggle or original source URL Description: Brief summary of the dataset Upload the original/raw CSV file (not cleaned yet) Click Upload Result: Dataset appears in list with RAW status
Step 4: Upload Cleaned File & Configure Metadata
Find the RAW dataset in the list Click "Clean" button Upload the cleaned CSV file (from Step 2) Configure headers for each column: SettingDescriptionNameColumn name (editable)UnitsMeasurement units (kWh, ยฐC, %, ratio, tCO2, etc.)TypeTime / Target / Covariate / Group Column Type Guide: Time: Timestamp/datetime columns (usually required) Target: Variable to predict (at least one required) Covariate: Input features/independent variables Group: Categorical segment variables (WeekStatus, Day_of_week, Load_Type, etc.) Bulk Configuration: Select multiple rows via checkboxes Use "Apply" dropdown to set type for selected columns Set units individually or in bulk Common Unit Patterns: Energy: kWh, MWh, MW Power: kVarh, kW Emissions: tCO2, kgCO2 Ratios: ratio, % Time: seconds, minutes, hours
Step 5: Assign Groups to Variables
Purpose: Group variables define how data is segmented for analysis. Exact Workflow: Select ALL variables by checking their checkboxes: Target variable(s) ALL covariate variables Apply ALL group tags to selected variables: Click first group tag (e.g., WeekStatus) โ all selected get this group Click second group tag (e.g., Day_of_week) โ all selected get this group Click third group tag (e.g., Load_Type) โ all selected get this group Continue for all available group tags Result: All variables have all groups assigned (e.g., "WeekStatus ร Day_of_week ร Load_Type") Important: Assign groups to BOTH target variables AND all covariates.
Step 6: Final Upload
Click "Upload Cleaned Dataset" button Wait for processing Dataset status changes from RAW โ CLEAN Verify data points count is correct
Example: Steel Industry Energy Dataset
Source: https://www.kaggle.com/datasets/csafrit2/steel-industry-energy-consumption Metadata: Name: Steel Industry Energy Consumption (South Korea) Domain: Energy Data Points: 350,400 Column Configuration: ColumnTypeUnitsTimestampsTime-Usage_kWhTargetkWhLagging_Current_Reactive.Power_kVarhCovariatekVarhLeading_Current_Reactive_Power_kVarhCovariatekVarhCO2(tCO2)CovariatetCO2Lagging_Current_Power_FactorCovariateratioLeading_Current_Power_FactorCovariateratioNSMCovariatesecondsWeekStatusGroup-Day_of_weekGroup-Load_TypeGroup- Group Assignment: Select: Usage_kWh, Lagging_Current_Reactive.Power_kVarh, Leading_Current_Reactive_Power_kVarh, CO2(tCO2), Lagging_Current_Power_Factor, Leading_Current_Power_Factor, NSM Click: WeekStatus โ all selected get WeekStatus Click: Day_of_week โ all selected get Day_of_week Click: Load_Type โ all selected get Load_Type Final: All variables show "WeekStatus ร Day_of_week ร Load_Type"
Reference Materials
For detailed platform configuration guidance, see references/platform_guide.md.
Troubleshooting
"Next" button disabled: Check at least one Time column is set Check at least one Target column is set Verify all columns have types assigned Groups not appearing: Columns must be marked as "Group" type first Proceed to next step after setting Group types Upload fails: Re-run cleaning script Check CSV format (comma-delimited) Verify no empty column names
Scripts
ScriptPurposescripts/clean_dataset.pyClean and prepare CSV for uploadscripts/download_kaggle.shDownload datasets via Kaggle CLI
Platform URL
Data Annotation Platform: https://data.smlcrm.com
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Scripts
- SKILL.md
- references/platform_guide.md
- scripts/clean_dataset.py
- scripts/download_kaggle.sh