agent-data-ml-model
Agent skill for data-ml-model - invoke with $agent-data-ml-model
43
13%
Does it follow best practices?
Impact
93%
1.16xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-data-ml-model/SKILL.mdQuality
Discovery
0%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is essentially a label with an invocation command, providing no useful information about what the skill does, when to use it, or what triggers should activate it. It fails on every dimension of the rubric and would be nearly impossible for Claude to correctly select from a pool of available skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Trains machine learning models, performs data preprocessing, evaluates model performance, generates predictions'.
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about training models, making predictions, data pipelines, feature engineering, or ML workflows'.
Remove the invocation command from the description (it's operational metadata, not descriptive) and replace with domain-specific keywords users would naturally use.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for data-ml-model' is entirely vague and abstract, providing no information about what the skill actually does. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no 'Use when...' clause or equivalent trigger guidance. | 1 / 3 |
Trigger Term Quality | The only terms present are 'data-ml-model' and the invocation command '$agent-data-ml-model', which are technical jargon rather than natural keywords a user would say. No natural trigger terms like 'train model', 'machine learning', 'predict', etc. are included. | 1 / 3 |
Distinctiveness Conflict Risk | The description is extremely generic — 'data-ml-model' could overlap with any data processing, machine learning, or modeling skill. There are no distinct triggers to differentiate it from other skills. | 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 extensive YAML frontmatter block that constitutes most of the file, while the actual instructional body is a shallow enumeration of ML concepts Claude already knows. The single code example is generic and the workflow lacks validation checkpoints. The content would benefit greatly from being trimmed to only novel, actionable guidance with concrete examples for each workflow phase.
Suggestions
Remove or drastically reduce the YAML frontmatter and eliminate bullet points listing concepts Claude already knows (e.g., 'Handle missing values', 'Feature scaling', 'Confusion matrices') — focus only on project-specific patterns and constraints.
Add concrete, executable code examples for each workflow phase (e.g., specific data validation checks, hyperparameter tuning with GridSearchCV, model serialization with joblib) instead of abstract descriptions.
Add explicit validation checkpoints between workflow steps, such as 'Verify no data leakage by checking train/test distributions' or 'Assert model performance exceeds baseline before proceeding to deployment prep'.
Split detailed content (e.g., preprocessing patterns, evaluation metrics code, deployment scripts) into separate referenced files and keep SKILL.md as a concise overview with clear navigation links.
| 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/normalization', 'Confusion matrices'). The bullet lists are padded with obvious ML concepts that add no new knowledge. | 1 / 3 |
Actionability | There is one concrete code example showing a sklearn pipeline pattern, which is somewhat useful but generic. Most of the content is abstract descriptions ('Algorithm selection', 'Hyperparameter tuning', 'API endpoint creation') without specific commands, concrete examples, or executable guidance for how to actually perform these tasks. | 2 / 3 |
Workflow Clarity | The ML workflow is listed as a numbered sequence of phases, which provides some structure. However, there are no validation checkpoints, no feedback loops for error recovery, and no explicit verification steps between phases. For a multi-step process involving data manipulation and model training, the lack of validation steps is a significant gap. | 2 / 3 |
Progressive Disclosure | The content is a monolithic wall of text with no references to external files, no navigation structure, and no separation of overview from detailed content. The massive YAML frontmatter dominates the file, and the body content mixes high-level overview with code examples without clear organization or pointers to deeper resources. | 1 / 3 |
Total | 6 / 12 Passed |
Validation
100%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/ruflo
- Commit
844f68d
- Reviewed
Table of Contents
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