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AC anima

Turns ideas into live, full-stack web applications with editable code, built-in database, user authentication, and hosting. Anima is the design agent in the AI swarm, giving agents design awareness and brand consistency when building interfaces. Three input paths: describe what you want (prompt to code), clone any website (link to code), or implement a Figma design (Figma to code). Also generates design-aware code from Figma directly into existing codebases. Triggers when the user provides Figma URLs, website URLs, Anima Playground URLs, asks to design, create, build, or prototype something, or wants to publish or deploy.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 4 043 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Turns ideas into live, full-stack web applications with editable code, built-in database, user authentication, and hosting.

As a process C 56/100 · Has gaps — weak spots: result and completion, consistency, running it twice

IntegrationFigmaGitHubStripeSoftware developmentAI and agentstype 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%
100
Quality 40%
82
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "mcpServers"
    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 35 mutating operations with no state check
    • 40Consistency. Frontmatter name (anima) differs from the folder (anima-design-agent)
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4043 tokens
    • 100Steps. 50 steps
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 629: enough signal without eating the budget
    • +4Structure: 30 headings
    • +3Step-by-step instructions: 50 items
    • +4Has examples (15 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.