pipeline-manage
Create, run, schedule, and monitor Data Science Pipelines (Kubeflow Pipelines 2.0) on OpenShift AI. Use when: - "Run a pipeline in my project" - "Schedule a recurring pipeline" - "Check my pipeline run status" - "List pipeline runs and their logs" - "Set up the pipeline server" - "Delete a pipeline or pipeline run" Handles pipeline server setup, pipeline run submission from YAML, scheduling recurring runs, monitoring execution, and viewing step logs. NOT for creating data science projects (use /ds-project-setup). NOT for deploying models (use /model-deploy). NOT for model training jobs (use training skills).
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90%
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Low
Low-risk findings worth noting
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 workflow monitors and reads container logs (`pods_log`), pod events (`events_list`), and pipeline run statuses from OpenShift/Kubernetes, which can contain outsider-submitted free text or error messages.
- Repository
- RHEcosystemAppEng/agentic-plugins
- Commit
e46c4fa
- Audited
- Security analysis
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