SKILLEMALL.ai
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BC ui-animation

Implement, review, and measure UI motion. Reconstruct easing, springs and gestures from a recording using bundled frame extraction, tracking and curve-fitting tools when available.

The skillemall take

The skill promises to reverse-engineer UI animations from video: extract frames, track motion, fit easing curves and spring parameters. Useful in theory for designers who need to understand how competitors animate.

Contains 24 files and three Python scripts. Security checks found no critical issues, quality score is 79/100. However, all three scripts failed in sandbox: extract_frames.py needs command-line arguments, fit_curves.py and track_motion.py are missing numpy, scipy, and opencv-python. These are dependency issues, not bugs, and there's no setup guide. The skill works as a code reference but not as a ready-to-use tool.

nexu-io/open-design Agent Skills author: nexu-io Apache-2.0 24 files · 3 scripts body ≈ 5 923 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Implement, review, and measure UI motion.

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerSoftware developmentData and analyticsDesigntype 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
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 24. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5923 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "en_name"
  • note frontmatter-key unknown frontmatter key "zh_name"
  • note frontmatter-key unknown frontmatter key "zh_description"
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "od"

Process rating: all ten parameters 50/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 11 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5923 tokens
  • 100Steps. 76 steps
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 16 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
  • +2Single-language instructions
  • +3Description length 180: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 76 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (18 of 18)
  • +3All 3 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

In the sandbox Скрипты не запустились

The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

Запущено 3 скрипта; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

Ни один не дошёл до работы — им не хватило зависимостей, аргументов или файлов. Это не отзыв о поведении: наблюдать было не за чем.

scripts/extract_frames.pyне запустился: extract_frames.py: error: the following arguments are required: video, outdir
scripts/fit_curves.pyне запустился: missing deps. Install with: pip install numpy scipy
scripts/track_motion.pyне запустился: missing deps. Install with: pip install opencv-python numpy

15 Oct 2026