ml-cloud-deployment
ml-cloud-deployment is an MLOps & model serving skill for AI agents, published by JosiahSiegel in the claude-plugin-marketplace repository on GitHub. Skillspace rates ml-cloud-deployment D (57/100) and it passed the safety scan; the rating is based on static analysis only.
This skill should be used when the user asks to deploy, scale, or cost-optimize ML workloads on cloud platforms. PROACTIVELY activate for: (1) AWS SageMaker Studio, Training, Processing, Pipelines, Endpoints, Model Monitor, Feature Store, Clarify, Ground Truth, EC2 GPUs, EKS, Lambda, Inferentia, Trainium, (2) GCP Vertex AI Training, Pipelines, Endpoints, Feature Store, Model Monitoring, AutoML, Matching Engine, TPU, GKE, Cloud Run, (3) Azure ML workspaces, pipelines, managed endpoints, AutoML, Responsible ML, AKS/ACI, (4) Databricks, Modal, Replicate, RunPod, Lambda Labs, Anyscale. Provides: cloud ML architecture, autoscaling, hardware, security, and cost guidance.
- Grade
- D
- Quality
- 57 / 100
- Safety
- Passed
- GitHub stars
- 48
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 80 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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