vertex-ai-deployer
Vertex Ai Deployer - Auto-activating skill for ML Deployment. Triggers on: vertex ai deployer, vertex ai deployer Part of the ML Deployment skill category.
36
3%
Does it follow best practices?
Impact
96%
0.98xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/08-ml-deployment/vertex-ai-deployer/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 extremely weak—it is essentially auto-generated boilerplate that repeats the skill name as trigger terms and provides no substantive information about capabilities or usage conditions. It fails to describe any concrete actions, lacks natural trigger keywords users would employ, and provides no explicit guidance on when Claude should select this skill.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Deploys ML models to Vertex AI endpoints, configures prediction serving, manages model versions, and sets up autoscaling for inference.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about deploying models to Vertex AI, creating prediction endpoints, serving ML models on GCP, or configuring Google Cloud ML infrastructure.'
Include common keyword variations users might say, such as 'deploy model', 'GCP', 'Google Cloud', 'endpoint', 'prediction', 'model serving', 'inference', '.pkl', 'TensorFlow Serving', 'custom container'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions whatsoever. It only names itself ('Vertex Ai Deployer') and states it's for 'ML Deployment' without describing what it actually does—no mention of deploying models, creating endpoints, configuring pipelines, or any specific capabilities. | 1 / 3 |
Completeness | Both 'what' and 'when' are essentially missing. The description doesn't explain what the skill does beyond the vague category 'ML Deployment', and there is no 'Use when...' clause or equivalent explicit trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just the skill name repeated twice ('vertex ai deployer, vertex ai deployer'). There are no natural user keywords like 'deploy model', 'endpoint', 'prediction', 'GCP', 'Google Cloud', 'serving', or 'inference' that users would actually say. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Vertex AI' does provide some specificity to Google Cloud's ML platform, which helps distinguish it from generic ML deployment skills. However, the lack of concrete actions means it could still overlap with other Vertex AI or GCP-related skills. | 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 an empty template with no actual content. It contains only boilerplate descriptions that repeat the skill name without providing any actionable guidance for Vertex AI deployment—no code, no commands, no configurations, no workflows. It would provide zero value to Claude when attempting to help with ML deployment tasks.
Suggestions
Add concrete, executable code examples for common Vertex AI deployment tasks (e.g., deploying a model to an endpoint using the Vertex AI SDK, configuring autoscaling, setting up prediction routes).
Define a clear multi-step deployment workflow with validation checkpoints, such as: build container → push to Artifact Registry → create Model resource → deploy to Endpoint → validate predictions → configure monitoring.
Include specific gcloud CLI commands and Python SDK snippets for key operations like `gcloud ai endpoints deploy-model` and `aiplatform.Model.deploy()`.
Add references to separate files for advanced topics like custom prediction routines, traffic splitting, A/B testing, and monitoring setup.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler with no substantive information. It explains nothing Claude doesn't already know and repeats 'vertex ai deployer' excessively without providing any actual deployment instructions, commands, or configurations. | 1 / 3 |
Actionability | There is zero concrete, executable guidance. No code, no commands, no API calls, no configuration examples—just vague descriptions like 'provides step-by-step guidance' and 'generates production-ready code' without actually doing either. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequences, no validation checkpoints. For a deployment skill involving production ML systems, the complete absence of any process is a critical gap. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative page with no references to detailed materials, no links to configuration examples, API references, or related documentation. The structure exists (headers) but contains no meaningful content to organize. | 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
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- Reviewed
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