cas-bigdatalab
Official@cas-bigdatalab
A comprehensive library for high-throughput data ingestion, cleaning, validation, and transformation across structured, unstructured, and geospatial file formats.
Agent Skills by cas-bigdatalab
Showing 199 vetted skills indexed across 1 GitHub repositories.
cas-bigdatalabmissing_operator_stop
Inserts a placeholder node into a DAG when no matching operator exists.
cas-bigdatalabdataspace_file_sink_stop
Uploads upstream pipeline output files to a Dataspace directory as a DAG terminal node.
cas-bigdatalabgjf_editor
Creates and edits Gaussian input files with Link 0 directives, route keywords, and charge settings.
cas-bigdatalabopenBabel_skill
Convert molecular files between formats and generate 3D conformations using OpenBabel.
cas-bigdatalabmultiwfn_skill
Extract HOMO/LUMO energies, atomic charges, and molecular surface properties from Gaussian wavefunction files using Multiwfn.
cas-bigdatalabdataspace_file_source_stop
Downloads a file from a Dataspace source as a DAG workflow input node.
cas-bigdatalabgaussian_executor
Execute Gaussian 16 quantum chemistry calculations and package log, chk, and summary outputs into a zip archive.
cas-bigdatalabgaussian_chk_to_fchk
Convert Gaussian chk checkpoint files to fchk wavefunction text files using formchk.
cas-bigdatalabpiflow-skill-generator_planner
Generates PiFlow-compatible skill directories from JSON specs or validated workflow summaries.
cas-bigdatalabpdf_metadata_extract
Extract metadata from PDF files into structured JSON using pypdf.
cas-bigdatalabpdf_text_extract
Extract text from PDF files and save it as text files.
cas-bigdatalabcsv_formatter
Convert CSV files to JSONL format using the data_juicer library.
cas-bigdatalabDC1_Blank_Line_Clean
Remove blank lines from CSV, TSV, Excel, and SPSS files using Python.
cas-bigdatalabsink_stop
Terminate a Dataflow Graph at a specified node and save output to a path.
cas-bigdatalabllm_file_transform_stop
Transform text files using OpenAI compatible APIs.
cas-bigdatalabsource_stop
Configure the initial data source for a DAG workflow with a file path.
cas-bigdatalabstacked_symbol_cleaner
Remove decorative symbols and line separators from text data using Python regex.
cas-bigdatalabsystematic_sampler
Sample time-series data from JSONL files at specified intervals or counts.
cas-bigdatalabingest_archive_tar_extractor
Extract TAR archives from a directory and output a JSON report.
cas-bigdatalabpipeline-minus
Subtract 5 from an integer input and return the result.
cas-bigdatalabQC5_BatchThresholdCheck
Compare data points against predefined thresholds and flag outliers.
cas-bigdatalabtable_collector
Extract structured data from CSV, TSV, XLS, and XLSX files into JSONL, JSON, or CSV.
cas-bigdatalablocal_formatter
Convert local datasets from files or directories to JSONL format.
cas-bigdatalabpdf_crop
Trim unwanted margins from PDF pages using specified boundary coordinates.
Frequently Asked Questions About cas-bigdatalab
FAQPage SchemaWhat specific data processing tasks can I perform with these skills?▼
You can perform complex data ingestion, format conversion (CSV/TSV/XLSX to JSONL), document parsing (PDF/DOCX), deduplication, and rigorous quality control checks including threshold validation, anomaly detection, and referential integrity verification.
Which technical personas benefit most from this registry?▼
Data engineers, research scientists, and backend developers focused on large-scale data preparation, ETL pipeline construction, and scientific data normalization will find these modular components highly effective for building robust data processing architectures.
What are the primary dependencies for running these data processing components?▼
Most components rely on standard Python data science libraries, specifically pandas for tabular manipulation, pypdf for document handling, and specialized packages like data_juicer for text cleaning and geospatial libraries for coordinate transformations.