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AB landing-page-builder

Build a single-page landing page from a text prompt or design brief. USE WHEN: - User asks for a landing page, marketing page, product page, sales page, or startup website - User provides a business name/idea and wants a deployable web page - User sends an HTML template and wants it adapted with new content/branding - User asks for a "one-pager" or "single-page site" DON'T USE WHEN: - User wants a multi-page website (use a web framework instead) - User wants an email template or newsletter (different format/constraints) - User wants a blog, documentation site, or wiki - User wants to edit an existing live site (this builds new pages, not patches) - User wants a web app with backend logic, auth, or databases OUTPUTS: Single self-contained .html file (no external dependencies except Google Fonts). Ready to open in browser, deploy to any static host, or deliver to a client. INPUTS: Business name, what it does, target audience, desired tone. Optionally: a reference HTML template to adapt.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 851 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Build a single-page landing page from a text prompt or design brief.

As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

GeneratorMarketingtype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
66/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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: 3. 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 66/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 29 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 851 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

    • +3Description length 1003: 120–800 characters recommended
    • +4No input/output examples
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Description says when NOT to use the skill
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 29 items
    • +3Output format is stated explicitly

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