agent-data-ml-model

Agent skill for data-ml-model - invoke with $agent-data-ml-model

1.16x

Quality

13%

Does it follow best practices?

Impact

93%

1.16x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agents/skills/agent-data-ml-model/SKILL.md

Quality

Discovery

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

This is an extremely weak description that provides virtually no useful information for skill selection. It reads as a placeholder or auto-generated stub, containing only an invocation command without any explanation of capabilities, use cases, or trigger conditions. It would be nearly impossible for Claude to correctly select this skill from a pool of available skills.

Suggestions

Replace the entire description with concrete actions the skill performs, e.g., 'Trains machine learning models, performs data preprocessing, evaluates model performance, and generates predictions.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about training models, making predictions, data preprocessing, feature engineering, or ML pipelines.'

Remove the invocation command from the description (it belongs in usage instructions, not in the selection-oriented description) and replace with domain-specific keywords users would naturally use.

Dimension	Reasoning	Score
Specificity	The description contains no concrete actions whatsoever. 'Agent skill for data-ml-model' is entirely vague and abstract, providing no information about what the skill actually does.	1 / 3
Completeness	The description fails to answer both 'what does this do' and 'when should Claude use it'. It only provides an invocation command with no explanation of capabilities or usage triggers.	1 / 3
Trigger Term Quality	The only terms present are 'data-ml-model' and '$agent-data-ml-model', which are internal identifiers rather than natural keywords a user would say. No natural language trigger terms like 'machine learning', 'train model', 'predict', etc. are included.	1 / 3
Distinctiveness Conflict Risk	The description is so generic that 'data-ml-model' could overlap with any data processing, machine learning, or modeling skill. There are no distinct triggers or specific capabilities to differentiate it.	1 / 3
	Total	4 / 12 Passed

Implementation

27%

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

This skill is dominated by an extremely large YAML frontmatter block that consumes most of the file, while the actual instructional body is a shallow enumeration of ML concepts Claude already knows. The single code example is useful but insufficient for the breadth of topics claimed. The content would benefit greatly from being condensed, made more actionable with specific patterns for each workflow step, and split into referenced sub-documents.

Suggestions

Drastically reduce the YAML frontmatter and move the body focus to concrete, executable code patterns for each workflow phase (preprocessing, training, evaluation, deployment) rather than listing abstract bullet points.

Add validation checkpoints between workflow phases, e.g., 'Verify no data leakage: assert X_test not in training pipeline' or 'Check class balance before model selection'.

Split detailed content into referenced files (e.g., PREPROCESSING.md, EVALUATION.md, DEPLOYMENT.md) and keep SKILL.md as a concise overview with clear navigation links.

Remove explanations of concepts Claude already knows (e.g., what cross-validation is, what feature scaling means) and replace with project-specific conventions, preferred libraries, and concrete code templates.

Dimension	Reasoning	Score
Conciseness	The vast majority of the file is YAML frontmatter configuration that is not actionable skill content. The body content itself lists high-level responsibilities and workflow steps that Claude already knows (e.g., 'handle missing values', 'feature scaling', 'cross-validation setup'). The bullet lists are padded with obvious ML concepts that don't add value.	1 / 3
Actionability	There is one concrete code example showing a sklearn pipeline pattern, which is executable. However, most of the content is abstract bullet points ('Handle missing values', 'Encoding categorical variables', 'API endpoint creation') without specific commands, concrete examples, or copy-paste ready code for each step.	2 / 3
Workflow Clarity	The ML workflow is sequenced into 5 numbered phases, which provides some structure. However, there are no validation checkpoints, no error recovery feedback loops, and no explicit verification steps between phases (e.g., validate data quality before proceeding to model training). For a complex multi-step ML pipeline, this lacks the rigor needed.	2 / 3
Progressive Disclosure	The content is a monolithic file with no references to supporting documents. The massive YAML frontmatter and body content are all inline with no separation of concerns. There are no links to detailed guides for specific topics like preprocessing, evaluation metrics, or deployment patterns, despite the breadth of topics covered.	1 / 3
	Total	6 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: ruvnet/claude-flow
Commit: ca77f83

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

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