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BD hetzner-cloud

Manage Hetzner Cloud infrastructure with the `hcloud` CLI — servers, networks, firewalls, load balancers, volumes, DNS zones, SSH keys, primary/floating IPs, snapshots, certificates, placement groups, storage boxes. Use whenever the user mentions Hetzner, hcloud, VPS provisioning, or Hetzner location codes (fsn1, hel1, nbg1, ash, hil, sin) — even if they don't say "hcloud". CLI-only; does NOT cover Hetzner Robot (dedicated servers, separate product and API).

laguagu/claude-code-nextjs-skills Agent Skills author: laguagu MIT 1 file body ≈ 996 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Manage Hetzner Cloud infrastructure with the hcloud CLI — servers, networks, firewalls, load balancers, volumes, DNS zones, SSH keys, primary/floating IPs…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationSoftware developmentInfrastructureWriting and documentstype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
D
49/100
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 · 0

    ✓ No critical or high findings

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 49/100

    • 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. 4 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 996 tokens
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 462: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 12 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.