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AC art-style-transformation-set

Transform a single reference photo into a grid of art-style portraits — Studio Ghibli, Pixar, Renaissance oil, 80s anime, LEGO, Polaroid — all with the same face and pose. How it works: 1. Locks the face from your reference photo 2. Picks 4 to 9 distinct art styles to render in parallel 3. Holds the same head-and-shoulders pose across every panel 4. Layers each panel's signature texture (watercolor, vinyl, oil, cel) 5. Outputs a single 1:1 grid ready for the social feed

genspark Agent Skills author: Genspark 1 file body ≈ 2 981 tokens Open the sourcegenspark.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

GeneratorAI and agentstype and topics are labelled automatically from the skill text
Runs in: Genspark 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
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: 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 57/100

    • 0Result and completion. Does not say what the result is
    • 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) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 39 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2981 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 475: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 39 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.