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CF build-polished-visual-frontends

构建、重做、诊断或提升最终由浏览器消费的高完成度视觉前端。用于 HTML/CSS/JS/TS、React/Vue/Svelte/Astro、SVG/Canvas/WebGL 或 web-to-print 的网站、产品界面、数据故事、图解、地图、仿真与游戏；负责真实标杆、表达强度、成熟框架与设计系统、专业 Web 引擎、代表画面和最终像素质量，并与一份主要领域 Skill 组合。不用于纯后端、原生应用或仅以网页作为临时查看器的非 Web 母版。

EverMind-AI/Raven Agent Skills author: EverMind-AI Apache-2.0 8 files body ≈ 5 100 tokens Open the sourcegithub.com↗ analyzed 5 d ago

构建、重做、诊断或提升最终由浏览器消费的高完成度视觉前端。用于 HTML/CSS/JS/TS、React/Vue/Svelte/Astro、SVG/Canvas/WebGL 或 web-to-print 的网站、产品界面、数据故事、图解、地图、仿真与游戏；负责真实标杆、表达强度、成熟框架与设计系统、专业 Web…

As a process F 34/100 · No process to follow — References files that are not bundled: references/design-precedents/page-1.jpg

GeneratorSoftware developmentData and analyticstype 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
C
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
60
Run on models
none yet
Process rating
F
34/100
No process to follow
References files that are not bundled: references/design-precedents/page-1.jpg
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5100 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/design-precedents/page-1.jpg

Process rating: all ten parameters 34/100

Will not run. References files that are not bundled: references/design-precedents/page-1.jpg
  • 0Tools and files. 1 referenced file(s) missing: references/design-precedents/page-1.jpg
  • 0Result and completion. Does not say what the result is
  • 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
  • 70Execution cost. Instruction body is 5100 tokens
  • 100Steps. 69 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations

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 223: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 69 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (6 of 6)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.