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BD vercel

Inspect Vercel projects, deployments, build logs and domains via the Vercel REST API. Use when the user mentions Vercel, a deployment that failed / is building, build or runtime logs, a preview URL, project domains, or wants to check / redeploy a Vercel project.

AceDataCloud/Skills Agent Skills author: AceDataCloud 1 file body ≈ 577 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Inspect Vercel projects, deployments, build logs and domains via the Vercel REST API.

As a process D 46/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
83
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration net-credential-use SKILL.md:20
      Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service; the skill's own vendor host)
      $VERCEL_ACCESS_TOKEN`. Base URL: `https://api.vercel.com`. If the resource is
      known servicevendor-host

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "connections"
    • note frontmatter-key unknown frontmatter key "allowed_tools"

    Process rating: all ten parameters 46/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 75Steps. 3 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 577 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 262: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (4 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.