deploy-to-vercel
Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
68
83%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Risky
Do not use without reviewing
Quality
Discovery
89%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 is a solid skill description with excellent trigger terms and completeness. The explicit 'Use when' clause with natural user phrases is a strong point. The main weakness is that the 'what' portion could be more specific about the range of deployment-related actions supported beyond just 'deploy applications and websites'.
Suggestions
Expand the capability list with more specific actions, e.g., 'Deploy applications and websites to Vercel, manage preview deployments, configure environment variables, and check deployment status.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (Vercel deployment) and a general action (deploy applications and websites), but doesn't list multiple specific concrete actions like configuring environment variables, managing domains, checking deployment status, or rolling back deployments. | 2 / 3 |
Completeness | Clearly answers both 'what' (deploy applications and websites to Vercel) and 'when' (explicit 'Use when' clause with multiple trigger phrases covering different ways users might request deployment). | 3 / 3 |
Trigger Term Quality | Includes excellent natural trigger phrases users would actually say: 'deploy my app', 'deploy and give me the link', 'push this live', 'create a preview deployment', plus the keyword 'Vercel'. These cover common variations of deployment requests. | 3 / 3 |
Distinctiveness Conflict Risk | The description is clearly scoped to Vercel specifically, which distinguishes it from other deployment tools (e.g., AWS, Netlify, Heroku). The trigger terms are deployment-specific and platform-specific, making conflicts unlikely. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a high-quality, well-structured deployment skill with excellent actionability and workflow clarity. The decision tree approach (gather state → choose method) is well-designed with concrete commands for every path. The main weakness is length — the document covers many scenarios (5+ deployment paths, 3 agent types, troubleshooting) which makes it somewhat long, though most content is justified by the complexity of the task.
Suggestions
Consider splitting agent-specific notes and fallback methods into separate referenced files to reduce the main SKILL.md length and improve progressive disclosure.
Reduce redundancy in team selection instructions — the logic is repeated across multiple methods and could be stated once with a back-reference.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly long (~250 lines) and includes some redundant explanations (e.g., re-explaining team selection logic multiple times across methods, explaining what .vercel/project.json vs .vercel/repo.json contain). However, most content is genuinely necessary given the complexity of the decision tree. Some tightening is possible but it's not egregiously verbose. | 2 / 3 |
Actionability | Every path includes concrete, executable bash commands with specific flags and arguments. The skill provides copy-paste ready commands for each scenario (git push, vercel deploy, vercel link, fallback scripts) with clear flag explanations like --no-wait, --scope, -y, and --repo. | 3 / 3 |
Workflow Clarity | The skill has an excellent decision tree structure: gather state first (4 explicit checks), then branch based on conditions. Each branch has clear numbered steps with validation (vercel inspect for status, explicit 'ask before pushing' checkpoints). The feedback loop of 'try CLI → fall back to script' is well-defined, and destructive actions (git push) require explicit user approval. | 3 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and logical sections, but it's a monolithic document that could benefit from splitting agent-specific notes or the fallback methods into separate files. The references to external scripts (deploy.sh, deploy-codex.sh) are clear, but the main file is quite long for a single SKILL.md. No bundle files were provided to verify referenced paths. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- databricks/devhub
- Commit
0b6297e
- Reviewed
Table of Contents
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