run-pipeline
De la autor
Run the full data science pipeline: validate raw data, preprocess, engineer features, train model, and evaluate. Use this when you want to execute the end-to-end ML pipeline or re-run it after data or code changes.
Instalare
npx skills add xvirobotics/metaskill --skill run-pipeline -a claude-code
Se instalează în .claude/skills/run-pipeline/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 75
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- data-engineeringcbrock84/headcountBuilds and operates data pipelines — ingestion, transformation, orchestration, quality testing, and reliability of data delivery. Use this to design or debug a pipeline, decide batch versus streaming, add data quality checks, handle late or duplicate data, or work out why a dashboard's numbers changed without anyone changing the dashboard.Date★ 2.1K
- Data Pipeline TestingPramodDutta/qaskillsTesting data pipelines including ETL validation, data quality checks, pipeline orchestration testing, and data lineage verification.Testare★ 240
- data-analysispedrohcgs/claude-code-my-workflowEnd-to-end R data analysis pipeline — exploration → cleaning → regression → publication-ready tables and figures. Use when user says "analyze this dataset", "run a regression on X", "explore this CSV", "full analysis workflow", "get me summary stats and a regression", or points at a `.csv`/`.rds`/`.dta` and asks for empirical results. Produces numbered R scripts in `scripts/R/` and outputs to `output/`.Date★ 1.7K
- ml-pipeline-workflowwshobson/agentsBuild end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.Productivitate★ 41.7K
- preprocessing-data-with-automated-pipelinesforyourhealth111-pixel/Vibe-SkillsDesign and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.Design★ 3.8K
- data-scientistborghei/Claude-SkillsData science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.Știință★ 978
Note și recenzii
—
nicio recenzie
Ai încercat acest skill? Notează-l ca să-i ajuți pe alții. Autentifică-te
Discuție
Intră în discuție. Autentifică-te