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AC ui-ux-pro-max

Searchable UI/UX design databases: 50+ styles, 97 palettes, 57 font pairings, 99 UX rules, 25 chart types. CLI generates design systems from natural language. Data-driven complement to ui-design.

modbender/skill-library-mcp Agent Skills author: modbender MIT 45 files body ≈ 2 902 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Searchable UI/UX design databases: 50+ styles, 97 palettes, 57 font pairings, 99 UX rules, 25 chart types.

As a process C 55/100 · Has gaps — weak spots: when it triggers, failures and branches, consistency

ProcedureDesigntype 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
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
83
Run on models
none yet
Process rating
C
55/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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.

Exfiltration 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 Exfiltration net-credential-use data/stacks/nuxtjs.csv:53
      Credential used in a network call (verify the destination is the intended service)
      52,Environment,Use runtimeConfig for env vars,Access environment variables safely,runtimeConfig in nuxt.config,process.env directly,"runtimeConfig: { apiSecret: '', public: { apiBase: '' } }",process.

    Files scanned: 45. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "related"

    Process rating: all ten parameters 55/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (ui-ux-pro-max) differs from the folder (ui-ux)
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 68 steps
    • 100Execution cost. Instruction body is 2902 tokens
    • low 11 top-level sections: this looks like several domains in one skill

    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)
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 195: enough signal without eating the budget
    • +4Structure: 38 headings
    • +3Step-by-step instructions: 68 items
    • +3Output format is stated explicitly
    • +4Has examples (12 code blocks)

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