ml-pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.
72
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
The skill's pipeline examples (e.g., load_data in references/pipeline-orchestration.md and the Kubeflow/Prefect/Airflow pipelines in SKILL.md) explicitly read datasets from external locations like s3://... and gs://... and then use those data to validate, train, and decide deployment actions, which means untrusted third‑party data can be ingested and materially influence tool behavior.
- Repository
- jeffallan/claude-skills
- Commit
e8be415
- Audited
- Security analysis
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