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 reads like an auto-generated stub with no substantive content. It fails to describe any concrete capabilities, lacks natural trigger terms users would use, and provides no guidance on when Claude should select this skill. The only redeeming quality is the mention of 'Vertex AI' which provides minimal domain specificity.
Suggestions
Add specific concrete actions such as 'Deploys ML models to Vertex AI endpoints, configures prediction serving, manages model versions, and sets up autoscaling for inference workloads.'
Add a 'Use when...' clause with natural trigger terms like 'Use when the user asks to deploy a model to Vertex AI, create a prediction endpoint, serve a model on GCP, or configure ML model serving on Google Cloud.'
Include common user-facing keywords and file/resource types such as 'model deployment', 'endpoint', 'prediction', 'GCP', 'Google Cloud', 'serving', 'inference', and 'model registry'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description provides no concrete actions. 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 | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause or equivalent trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'vertex ai deployer' repeated twice. There are no natural user keywords like 'deploy model', 'endpoint', 'serving', 'prediction', 'GCP', 'Google Cloud', or 'model deployment' that users would actually say. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Vertex AI' provides some specificity to Google Cloud's ML platform, which distinguishes it from generic ML deployment skills. However, the lack of concrete actions means it could still overlap with other Vertex AI or ML deployment 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 and trigger phrases but provides zero actionable guidance on deploying models to Vertex AI. It fails on every dimension because it teaches Claude nothing it doesn't already know and provides no concrete instructions, code, or workflows.
Suggestions
Add concrete, executable code examples for deploying a model to Vertex AI (e.g., using the google-cloud-aiplatform Python SDK to upload a model, create an endpoint, and deploy).
Define a clear multi-step workflow with validation checkpoints: e.g., 1) Build container image, 2) Upload model artifact, 3) Create endpoint, 4) Deploy model, 5) Validate with test prediction, 6) Configure autoscaling.
Remove all boilerplate sections (Purpose, When to Use, Example Triggers) that add no value and replace with actual technical content covering model serving configurations, monitoring setup, and production optimization patterns.
Add references to separate files for advanced topics like custom prediction routines, A/B testing deployments, or monitoring dashboards if the skill needs to cover multiple sub-topics.
| 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. The skill claims to provide 'step-by-step guidance' but contains none. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative page with no references to detailed materials, no links to related files, and no structured navigation. The sections present (Purpose, When to Use, Capabilities, etc.) contain no useful content. | 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
Table of Contents
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