bigquery-ml-model-creator
Bigquery Ml Model Creator - Auto-activating skill for GCP Skills. Triggers on: bigquery ml model creator, bigquery ml model creator Part of the GCP Skills skill category.
34
3%
Does it follow best practices?
Impact
89%
0.98xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/14-gcp-skills/bigquery-ml-model-creator/SKILL.mdQuality
Discovery
7%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 placeholder that relies entirely on the skill name to convey meaning. It lacks any concrete actions, meaningful trigger terms, or explicit guidance on when to use the skill. The repeated trigger term and boilerplate 'auto-activating skill' framing provide no useful information for skill selection.
Suggestions
Add specific actions the skill performs, e.g., 'Creates, trains, and evaluates machine learning models in BigQuery using BigQuery ML (BQML), including classification, regression, and forecasting models.'
Add a 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about creating ML models in BigQuery, training BQML models, CREATE MODEL statements, or making predictions with BigQuery ML.'
Include common user-facing keywords and variations such as 'BigQuery ML', 'BQML', 'CREATE MODEL', 'machine learning', 'train model', 'predictions', and 'ML pipeline in GCP'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions. It only states the skill name 'Bigquery Ml Model Creator' and that it's 'auto-activating' as part of 'GCP Skills', but never describes what it actually does (e.g., create ML models, train models, evaluate predictions). | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is meaningfully answered. There is no 'Use when...' clause, and the description provides no information about the skill's capabilities beyond its name. | 1 / 3 |
Trigger Term Quality | The trigger terms are just the skill name repeated twice ('bigquery ml model creator, bigquery ml model creator'). There are no natural user keywords like 'machine learning', 'train model', 'BigQuery ML', 'BQML', 'CREATE MODEL', or 'predictions'. | 1 / 3 |
Distinctiveness Conflict Risk | The name 'Bigquery Ml Model Creator' is somewhat specific to a niche (BigQuery ML model creation), which provides some distinctiveness. However, the lack of detail means it could overlap with other GCP or ML-related skills without clear differentiation. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a placeholder with no substantive content. It contains no executable code, no BigQuery ML syntax, no concrete guidance, and no actual instructions for creating ML models in BigQuery. Every section is generic boilerplate that could apply to any topic by swapping the skill name.
Suggestions
Add concrete BigQuery ML examples with executable SQL, e.g., CREATE MODEL syntax for common model types (linear regression, logistic regression, k-means) with realistic dataset references.
Include a clear workflow: 1) prepare training data, 2) create model with specific SQL, 3) evaluate with ML.EVALUATE, 4) predict with ML.PREDICT, with validation at each step.
Remove all generic filler ('Provides step-by-step guidance', 'Follows industry best practices') and replace with actual domain-specific knowledge such as supported model types, required IAM permissions, and common pitfalls.
Add references to related resources or bundle files covering advanced topics like hyperparameter tuning, model export to Vertex AI, or feature engineering in BigQuery.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic filler that tells Claude nothing useful. Phrases like 'Provides step-by-step guidance' and 'Follows industry best practices' are vacuous. The entire file explains what the skill does rather than providing any actual instructions or knowledge. | 1 / 3 |
Actionability | There is zero concrete guidance—no SQL examples, no BigQuery ML CREATE MODEL syntax, no specific commands, no code snippets. The skill describes rather than instructs, offering only vague promises of capability. | 1 / 3 |
Workflow Clarity | No workflow, steps, or sequence of any kind is provided. There are no validation checkpoints, no error handling guidance, and no actual process to follow for creating a BigQuery ML model. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of generic text with no references to supporting files, no structured sections with real content, and no navigation to deeper resources. There are no bundle files to support it either. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 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 | 9 / 11 Passed | |
- Commit
3a2d27d
- Reviewed
Table of Contents
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