data-quality-pipeline
CommunityOrchestrate data quality end-to-end.
Data & Analytics#manifest#data-quality#data-cleaning#data-provenance#outlier-detection#encoding-repair#tidy-check
Authorpeterbamuhigire
Version1.0.0
Installs0
System Documentation
What problem does it solve?
data-quality-pipeline provides a single-entry skill that encapsulates a disciplined, repeatable process to validate and enrich tabular data from encoding repair through to a provenance manifest, ensuring auditable outputs for research workloads.
Core Features & Use Cases
- End-to-end data quality pipeline covering encoding repair, tidying checks, cleaning, outlier detection, merge discipline, four-axis scoring, and manifest creation.
- Produces a provenance packet (dataset.parquet, dataset.profile.json, dataset.dq.json, dataset.manifest.json) for auditable ship artifacts.
- Use case: standardize incoming datasets for research cohorts, ensure non-hallucinated results, and automate gating in data-driven reports.
Quick Start
Run the data-quality-pipeline against an input dataset to generate the ship artifacts and provenance.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: data-quality-pipeline Download link: https://github.com/peterbamuhigire/digital-research-skills/archive/main.zip#data-quality-pipeline Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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