By tonone-ai
Audits, designs, and executes data cleaning pipelines — deduplication, missing value handling, outlier detection, schema validation, and ETL quality checks with statistical justification and audit trails.
Audit existing data cleaning code — find missing validation, silent data loss, and quality gaps.
Design a data cleaning and transformation pipeline — missing values, outliers, and deduplication.
Design a data validation pipeline — schema checks, range validation, and quality metrics.
Uses power tools
Uses Bash, Write, or Edit tools
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin cleanData engineer — databases, migrations, pipelines, data modeling
Automated data preprocessing and cleaning pipelines
Comprehensive data engineering toolkit combining ETL pipelines, data quality, and data architecture. Includes data architect agent for holistic data engineering decisions.
ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development
Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
Data engineering and ETL tools. Includes 3 specialized agents, 4 commands, and 19 skills.
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".