building-automl-pipelines

Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline".

Quality

78%

Does it follow best practices?

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tessl review fix ./plugins/ai-ml/automl-pipeline-builder/skills/building-automl-pipelines/SKILL.md

Quality

Content

57%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 skill body is a clean overview with good progressive disclosure, but its instructions are abstract and duplicated with the reference, and it lacks validation checkpoints for the batch/destructive training and deployment operations.

Suggestions

Add executable snippets or concrete commands to the Instructions (e.g., a minimal AutoML config or a sample framework call) so guidance is actionable rather than abstract.

Dedupe the Instructions against implementation.md — keep the body to a high-level sequence and let the reference hold the detailed steps.

Insert validation checkpoints and a validate->fix->retry feedback loop, especially around data validation and model export/deployment, to satisfy workflow clarity for batch/destructive operations.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean but the 11-step Instructions list substantially duplicates content from implementation.md (steps mirror the reference's Step 1/2), so it could be tightened by deferring detail to the reference.	2 / 3
Actionability	Steps are abstract directives ('Identify problem type', 'Initialize AutoML pipeline with configuration') with no executable code or commands in the body; concrete artifacts live only in referenced files, so guidance is incomplete rather than copy-paste ready.	2 / 3
Workflow Clarity	A numbered sequence exists, but there are no validation checkpoints or feedback loops for the batch training/deployment operations, which the rubric caps at 2 when validation is missing.	2 / 3
Progressive Disclosure	The body is a concise overview with clearly signaled, one-level-deep references to implementation.md, errors.md, and examples.md (all real files), matching the well-organized reference pattern.	3 / 3
	Total	9 / 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 is well-formed: it states concrete capabilities, gives explicit 'Use when' guidance, and supplies natural trigger phrases. It cleanly satisfies the what/when/trigger requirements with no fluff.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete capabilities — 'feature engineering, model selection, and hyperparameter tuning' — matching the anchor for listing several specific actions rather than vague language.	3 / 3
Completeness	Explicitly answers both what ('Build automated machine learning pipelines...') and when ('Use when automating ML workflows from data preparation through model deployment') with explicit triggers.	3 / 3
Trigger Term Quality	Provides natural trigger phrases a user would say ('build automl pipeline', 'automate ml workflow', 'create automated training pipeline'), giving good coverage of common phrasings.	3 / 3
Distinctiveness Conflict Risk	AutoML pipeline construction is a distinct niche with specific triggers, making it unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Path: plugins/ai-ml/automl-pipeline-builder/skills/building-automl-pipelines/SKILL.md
Commit: c4eafaf

Reviewed: 4 days ago

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