ml-pipeline-workflow

Build 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.

Quality

75%

Does it follow best practices?

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Securityby

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Quality

Content

50%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is well-organized as an overview but padded with generic MLOps knowledge Claude already has, and its code examples are placeholders pointing to missing bundle files. Workflow steps lack validation checkpoints and the referenced reference/asset files are absent.

Suggestions

Create the referenced bundle files (references/data-preparation.md, model-training.md, model-validation.md, model-deployment.md and assets/pipeline-dag.yaml.template, training-config.yaml, validation-checklist.md) or remove the dangling references.

Replace placeholder code blocks (e.g. "# See assets/...") with complete, executable examples so the skill is actionable without the missing files.

Trim generic best-practice and integration-point lists that restate well-known MLOps knowledge, and add explicit validation checkpoints (e.g. validate data quality, validate model metrics before deploy) to the Production Workflow.

Dimension	Reasoning	Score
Conciseness	The body enumerates generic MLOps best practices and integration tool lists (e.g. "Separate training and serving infrastructure", "Use model registries (MLflow, Weights & Biases)") that Claude already knows, but it is structured rather than pure fluff.	2 / 3
Actionability	Code blocks are stubs with placeholder comments (e.g. "# See assets/pipeline-dag.yaml.template for full example") rather than executable, copy-paste-ready code, and most guidance is abstract bullet points.	2 / 3
Workflow Clarity	The Production Workflow sequences four phases, but there are no explicit validation checkpoints or feedback loops for the risky deployment step, which caps workflow clarity at 2.	2 / 3
Progressive Disclosure	References and assets are clearly signaled and one level deep, but the referenced files (references/.md, assets/.template) do not exist in the bundle, so navigation is broken.	2 / 3
	Total	8 / 12 Passed

Description

100%Weight 40%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description follows the good-example pattern: concrete capabilities, an explicit "Use when" trigger clause, third-person/active voice, and a clear niche. It is concise and complete.

Dimension	Reasoning	Score
Specificity	"Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment" names multiple specific concrete actions across the pipeline lifecycle, matching the level-3 anchor.	3 / 3
Completeness	It clearly answers what (build end-to-end MLOps pipelines through the named stages) and when via the explicit "Use when creating ML pipelines, implementing MLOps practices..." clause.	3 / 3
Trigger Term Quality	"creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows" covers natural terms a user would actually say, with good variation rather than jargon.	3 / 3
Distinctiveness Conflict Risk	The end-to-end MLOps pipeline orchestration niche with these triggers is clearly distinguishable and unlikely to fire for unrelated skills, though it may overlap with adjacent ML skills.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 4 missing	Warning

	Total	15 / 16 Passed

Repository: rmyndharis/antigravity-skills
Path: skills/ml-pipeline-workflow/SKILL.md
Commit: e63f7dd

Reviewed: 13 days ago

Table of Contents

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