AD create-music-web-wallpaper
Create, rebuild, or refine interactive music and album-player wallpapers for Wallpaper Engine using HTML, CSS, and JavaScript. Use when an agent needs to turn user-provided or authorized music by bands, solo singers, vocalists, producers, composers, or other creators—together with album covers, audio, LRC lyrics, logos, and visual direction—into a Web wallpaper; build library navigation, synchronized lyrics, themes, optional motion, settings, localization, performance modes, and credits; or iterate on the interface through user feedback and Wallpaper Engine testing. Prefer Web wallpapers over Scene wallpapers for full music-player behavior, seeking, dynamic playlists, and lyric parsing.
Create, rebuild, or refine interactive music and album-player wallpapers for Wallpaper Engine using HTML, CSS, and JavaScript.
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 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 49/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 65 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2632 tokens
- low 11 top-level sections: this looks like several domains in one skill
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 695: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 65 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 3 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.