data-labeling
claudeData labeling: annotation guidelines, platforms, inter-annotator agreement, active learning, weak supervision, and label error detection.
1 skill · 1 command · 1 agent
0
claude
data-pipelines
ML data pipelines: Apache Beam, Spark, dbt, orchestration, streaming features, validation, and backfills.
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Plugin data-pipelines from marketplace libre-mlops in HermeticOrmus/LibreMLOps-Claude-Code. Commands read from the repo's manifest — 1 month ago.
What it installs
Skills (1)
ml-pipeline-patternsML data pipeline patterns with code: Apache Beam DoFns, branching and fan-out, streaming features in Flink, dbt incremental feature models, Great Expectations validation, and backfill strategy. Use when building batch or streaming pipelines that feed models.
Slash commands (1)
/data-pipelines:ml-pipelineScaffold, test, backfill, or monitor an ML data pipeline built on Apache Beam, Spark, dbt, or an orchestrator.
Subagents (1)
ml-data-engineerUse this agent when building or debugging the data pipelines that feed ML: batch jobs in Apache Beam or Spark, dbt feature models, Airflow, Prefect, or Dagster orchestration, Kafka to Flink streaming, or Great Expectations validation. It makes pipelines observable, tested, and reproducible, because
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claude & codex plugins21 plugins