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AC edit-figma-design

Create or update Figma designs directly from a written product or UI description using the Figma MCP authoring tools. Use when the user wants a mockup, wireframe, screen, component, flow, or concept designed in Figma from text, or wants to iterate on an existing Figma file from textual feedback. Despite the name, this skill can start from a new blank file or edit an existing one. Do not use for capture-based workflows that turn a running page into Figma; use `figma-generate-design` for those, and use `implement-design` for code implementation requests. Requires Figma MCP server connection.

warpdotdev/warp Agent Skills author: warpdotdev AGPL-3.0 1 file body ≈ 2 278 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Create or update Figma designs directly from a written product or UI description using the Figma MCP authoring tools.

As a process C 64/100 · Has gaps — weak spots: result and completion, running it twice

GeneratorFigmaDesigntype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Running it twice w 4
30
Tools and files w 18
60
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: 1. 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 64/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 14 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Steps. 68 steps, 4 vague phrases
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 9 branches
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2278 tokens
    • 100Progress reporting. Reports progress

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 596: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 68 items
    • +4Has examples (0 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.