vertex-agent-builder
Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill vertex-agent-builder63
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
32%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description identifies a clear technical domain (Vertex AI/Gemini/Google Cloud AI agents) and lists relevant capabilities, but is severely undermined by placeholder text for the trigger guidance. The phrases 'Use when appropriate context detected' and 'Trigger with relevant phrases based on skill purpose' are meaningless boilerplate that provide no actual selection criteria for Claude.
Suggestions
Replace the placeholder 'Use when appropriate context detected' with specific trigger scenarios, e.g., 'Use when user mentions Vertex AI, Gemini API, Google Cloud AI, or building AI agents on GCP'
Add concrete user phrases that would trigger this skill, such as 'deploy agent to Vertex', 'Gemini function calling', 'RAG on Google Cloud', or 'multi-modal Gemini app'
Specify concrete actions beyond 'build and deploy', such as 'configure agent tools, implement grounding with Vertex AI Search, set up Gemini API endpoints'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Vertex AI, Gemini, Google Cloud) and lists some capabilities (RAG, function calling, multi-modal), but 'Build and deploy production-ready generative AI agents' is somewhat vague about concrete actions - doesn't specify what building/deploying actually entails. | 2 / 3 |
Completeness | The 'what' is partially addressed, but the 'when' is completely absent - 'Use when appropriate context detected' and 'Trigger with relevant phrases based on skill purpose' are meaningless placeholder text that provide zero guidance on when to use this skill. | 1 / 3 |
Trigger Term Quality | Includes relevant technical terms like 'Vertex AI', 'Gemini models', 'RAG', 'function calling', 'Google Cloud' that users might mention, but the generic 'Trigger with relevant phrases based on skill purpose' is meaningless filler and doesn't provide actual trigger terms users would naturally say. | 2 / 3 |
Distinctiveness Conflict Risk | The Google Cloud/Vertex AI/Gemini focus provides some distinctiveness from generic AI skills, but could overlap with other Google Cloud skills or general AI agent building skills. The vague trigger guidance increases conflict risk. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is well-structured and token-efficient, providing a solid high-level framework for building Vertex AI agents. Its main weakness is the lack of concrete, executable code examples - the instructions describe what to do but don't show how with specific commands or code snippets. The workflow would benefit from explicit validation checkpoints between steps.
Suggestions
Add executable code snippets for key operations (e.g., agent initialization, tool registration, deployment command with actual gcloud/SDK syntax)
Insert explicit validation gates in the workflow, such as '**Verify**: Run `gcloud ai agents describe` to confirm deployment before proceeding to ops setup'
Include a concrete example of a tool/function interface schema rather than just mentioning 'schemas, error contracts'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, avoiding explanations of concepts Claude already knows (like what Vertex AI or RAG is). Every section serves a purpose without padding or unnecessary context. | 3 / 3 |
Actionability | The skill provides structured guidance and clear steps, but lacks executable code examples. Instructions like 'Choose model + region' and 'Implement retrieval' are directional rather than copy-paste ready with specific commands or code snippets. | 2 / 3 |
Workflow Clarity | The 6-step workflow is clearly sequenced, but validation checkpoints are implicit rather than explicit. Step 4 mentions evaluation but doesn't specify when to validate before proceeding. Missing explicit 'stop and verify' gates between steps. | 2 / 3 |
Progressive Disclosure | Excellent structure with a concise overview pointing to a full detailed guide in references. Clear one-level-deep navigation to external resources and repo standards. Content is appropriately split between this overview and the referenced SKILL.full.md. | 3 / 3 |
Total | 10 / 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 — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 13 / 16 Passed | |
- Reviewed
Table of Contents
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