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Apply professional design templates to any AI coding project. Supports 8 design styles (pixel retro, apple minimal, tech cyberpunk, corporate official, modern clean, glassmorphism, neumorphism, brutalist) and 6 frameworks (Tailwind CSS, CSS Variables, React Native, Flutter, SwiftUI, Jetpack Compose). Use when user wants to beautify their app, apply a design theme, or add polished icons. Works for web, mobile, and desktop projects.

ClawHub Agent Skills author: WilliamWang v1.0.0 MIT-0 22 files body ≈ 4 922 tokens Open the sourceclawhub.ai↗ analyzed 10 h ago

Apply professional design templates to any AI coding project.

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

TemplateGitHubSoftware developmenttype 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
85
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 Secrets in code secret-high-entropy-token data/icon-fallback.json:477
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "path": "M18 2h-3a5 5 0 0 0-5 5v3H…7a1 1 0 0 1 1-1h3z"
      quoted

    Files scanned: 22. 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 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 11 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, git) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4922 tokens
    • 85Steps. 62 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 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
    • +2Single-language instructions
    • +3Description length 434: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 62 items
    • +4Has examples (14 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    This design helper is mostly coherent, but it can import user-supplied GitHub templates into persistent local agent state and fetch remote SVG icons, so it should be reviewed before installation.
    LLM: suspicious (high) · VirusTotal: · 28 May 2026