building-automl-pipelines
tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill building-automl-pipelinesBuild 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".
61%
Overall
Validation
Implementation
Activation
Validation
81%| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md line count is 71 (<= 500) | Pass |
frontmatter_valid | YAML frontmatter is valid | Pass |
name_field | 'name' field is valid: 'building-automl-pipelines' | Pass |
description_field | 'description' field is valid (310 chars) | Pass |
description_voice | 'description' uses third person voice | Pass |
description_trigger_hint | Description includes an explicit trigger hint | Pass |
compatibility_field | 'compatibility' field not present (optional) | Pass |
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
metadata_field | 'metadata' field not present (optional) | Pass |
license_field | 'license' field is present: MIT | Pass |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_present | SKILL.md body is present | Pass |
body_examples | Examples detected (code fence or 'Example' wording) | Pass |
body_output_format | Output/return/format terms detected | Pass |
body_steps | Step-by-step structure detected (ordered list) | Pass |
Total | 13 / 16 Passed |
Implementation
22%This skill content is essentially a skeleton that defers all substantive guidance to external reference files. It lacks any executable code examples, has poorly structured workflow steps (duplicate numbered lists), and provides no concrete implementation details. The content describes what an AutoML pipeline should do rather than instructing how to build one.
Suggestions
- Add a concrete, executable quick-start code example showing a minimal AutoML pipeline (e.g., using PyCaret or Auto-sklearn) that can be copy-pasted and run
- Fix the workflow structure - merge the two numbered lists into a single coherent sequence with explicit validation checkpoints (e.g., 'Verify data quality checks pass before proceeding')
- Include at least one inline example with specific code rather than deferring everything to external files
- Add validation/verification steps such as checking model performance thresholds before deployment and data quality gates
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content has some unnecessary padding (verbose prerequisites list, generic resource descriptions) but isn't excessively verbose. The numbered steps are reasonably concise but could be tighter. | 2 / 3 |
Actionability | No executable code provided despite being a coding skill. Steps are abstract ('Initialize AutoML pipeline with configuration') without concrete implementation. The actual guidance is deferred to external files. | 1 / 3 |
Workflow Clarity | Steps are listed but poorly organized (two separate numbered lists starting at 1), no validation checkpoints, no feedback loops for error recovery. Missing critical validation steps for ML pipelines like data validation results or model performance thresholds. | 1 / 3 |
Progressive Disclosure | References external files appropriately (implementation.md, errors.md, examples.md) but the main content is too sparse - it's essentially just a table of contents pointing elsewhere. The overview should contain more actionable quick-start content. | 2 / 3 |
Total | 6 / 12 Passed |
Activation
100%This is a well-crafted skill description that excels across all dimensions. It provides specific capabilities, explicit 'Use when' guidance, natural trigger phrases, and a clear niche that distinguishes it from general ML or data processing skills. The description follows best practices by using third person voice and providing concrete, actionable trigger terms.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'feature engineering, model selection, and hyperparameter tuning' along with the pipeline scope 'from data preparation through model deployment'. Uses third person voice correctly. | 3 / 3 |
Completeness | Clearly answers both what ('Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning') and when ('Use when automating ML workflows...') with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms users would say: 'build automl pipeline', 'automate ml workflow', 'create automated training pipeline', plus domain terms like 'ML workflows', 'model deployment'. These are realistic phrases users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on automated ML pipelines and AutoML workflows. The specific trigger phrases like 'automl pipeline' and 'automated training pipeline' distinguish it from general ML or data science skills. | 3 / 3 |
Total | 12 / 12 Passed |
Reviewed
Table of Contents
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