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AB dotld

Search domain name availability and registration prices. Use when the user mentions domains, TLDs, domain registration, domain availability, or wants to find, check, or brainstorm domain names. Runs the dotld CLI to query the Dynadot API for real-time pricing and availability.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 922 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Search domain name availability and registration prices.

As a process B 79/100 · Nearly there — weak spots: failures and branches, progress reporting

IntegrationGitHubMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
84
Run on models
none yet
Process rating
B
79/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
Result and completion w 14
60
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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
    • medium Dangerous commands cmd-pipe-to-shell-known-host SKILL.md:27
      Pipe-to-shell installer from a well-known host (still executes remote code)
      curl -fsSL https://raw.githubusercontent.com/tedstonne/dotld/main/scripts/install.sh | bash

    Files scanned: 2. 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 79/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 6 steps
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 922 tokens
    • 100Running it twice. No mutating operations
    • 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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 277: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 6 items
    • +3Output format is stated explicitly
    • +4Has examples (15 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.