BC english-assessment
陪伴式英语水平测评助手。不是冰冷的出题机器，而是陪你一起成长的英语伙伴。基于历史数据动态调整难度（持续强项→提难度，薄弱项→多出题）， 大学英语水平（CEFR B1-C2），随机生成题卷（默认25-40题或快速21题，7-10种题型，总分100分）， 逐题作答，全程静默判分，最后输出得分与弱项分析。支持错题集、错题重测、查看错题讲解、全部考题深度解析、学习进度追踪、自适应难度、数据导入导出。 内容覆盖各专业领域。 触发词：开始英语测评 / 英语测试 / 测一下英语 / 英语水平测评 / 快速测评 / 错题重测 / 看错题 / 错题分析 / 考题分析 / 全部考题分析 / 学习进度 / 进步曲线 / 导出数据 / 导入数据 / 从飞书导入 NOT for：系统性英语课程、纯英语聊天、通用翻译工具
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 2. 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") - warning
body-longSKILL.md body ≈ 10712 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 52/100
- 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
- 40Execution cost. Instruction body is 10712 tokens: crowds the task out of the window
- 50When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 526 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 18 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (23 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
- +3Output format is not stated: the model decides each time
- -2localhost URLs: will not work for another user
- -2143 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 351: enough signal without eating the budget
- +4Structure: 67 headings
- +3Step-by-step instructions: 526 items
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.