AD leafer-js
为 Claude 提供 LeaferJS Canvas 图形引擎的全面支持。 当用户需要创建、开发或调试 LeaferJS 应用时，请立即使用此 Skill。 LeaferJS 是一个现代化的 Canvas 引擎，专注于图形编辑、复杂 UI 构建和交互应用开发。 支持瞬间创建100万个图形，性能卓越。 适用于以下场景： - 创建图形编辑器（如 Figma、Canva 类工具） - 开发互动应用、小游戏 - 构建可视化系统、数据大屏 - 开发 AI 无限画布应用 - 生成图片与短视频内容 - 创建组态软件、工业监控 当用户提到以下关键词时触发：LeaferJS、Leafer、Canvas 引擎、图形编辑、 画布应用、可视化、图形交互、动画系统、UI 布局、图形编辑器 即使用户没有明确说"Skill"，只要涉及 LeaferJS 相关开发，都应该使用此 Skill。
为 Claude 提供 LeaferJS Canvas 图形引擎的全面支持。 当用户需要创建、开发或调试 LeaferJS 应用时，请立即使用此 Skill。 LeaferJS 是一个现代化的 Canvas 引擎，专注于图形编辑、复杂 UI 构建和交互应用开发。 支持瞬间创建100万个图形，性能卓越。…
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 46/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (leafer-js) differs from the folder (leafer-js-skill)
- 100Tools and files. No external tools needed
- 100Steps. 160 steps
- 100Execution cost. Instruction body is 3490 tokens
- low 13 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
- +1No license
- +2Single-language instructions
- +3Description length 391: enough signal without eating the budget
- +4Structure: 52 headings
- +3Step-by-step instructions: 160 items
- +4Has examples (29 code blocks)
- +4Reference files are cited in the instructions (11 of 11)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.