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AC macos-golden-gate-design

Design, implement, review, or refactor native macOS 27 Golden Gate interfaces in SwiftUI or AppKit. Use when a Mac app must match Apple's current macOS 27 UI kit and restrained Liquid Glass language: integrated windows, full-height sidebars, native toolbars, flat lists, tables, and forms, clear pane boundaries, accessible materials, and removal of unnecessary nested cards, framed panels, or glassmorphism. Also use for Figma macOS 27 design-to-code work and audits of interfaces that feel too boxed.

github/awesome-copilot Agent Skills author: github MIT 4 files body ≈ 1 980 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Design, implement, review, or refactor native macOS 27 Golden Gate interfaces in SwiftUI or AppKit.

As a process C 55/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

AnalyzerFigmaSoftware developmentDesigntype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
55/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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: 4. 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 55/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 56 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1980 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +2Single-language instructions
    • +3Description length 502: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 56 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.