BC xiaozhi-teach-english-speaking-designer
英语口语活动设计：把"读课文、背对话"变成有任务、有情境、有反馈的口语课。触发语："口语课怎么上"、"学员不敢开口"、"口语活动怎么设计"、"信息差任务怎么做"、"口语任务怎么分层"、"口语怎么纠错"、"口语评分表怎么用"。核心工作流：口语目标（流利/准确/得体）→ 输入准备 → 任务型活动 → 输出练习 → 反馈与纠正 → 写回班级工作空间。任务时长按班额与课时排。不处理：整卷四维测评的设计与能力画像（转英语综合测评）、听力材料选编与听法训练（转英语听力材料设计）、学员一对一的 AI 陪练对话（学生端英语口语陪练负责）。
英语口语活动设计：把"读课文、背对话"变成有任务、有情境、有反馈的口语课。触发语："口语课怎么上"、"学员不敢开口"、"口语活动怎么设计"、"信息差任务怎么做"、"口语任务怎么分层"、"口语怎么纠错"、"口语评分表怎么用"。核心工作流：口语目标（流利/准确/得体）→ 输入准备 → 任务型活动 → 输出练习 →…
As a process C 53/100 · Has gaps — 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.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 16. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 264 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "grade_bands" - note
frontmatter-keyunknown frontmatter key "depends_on" - note
frontmatter-keyunknown frontmatter key "id" - note
frontmatter-keyunknown frontmatter key "min_platform_version" - note
frontmatter-keyunknown frontmatter key "max_round_limit" - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary"
Process rating: all ten parameters 53/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 26 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3203 tokens
- 100Running it twice. No mutating operations
- low 14 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
- +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
- +5Description quotes 8 example trigger phrases
- +3Description length 264: enough signal without eating the budget
- +4Structure: 42 headings
- +3Step-by-step instructions: 26 items
- +4Has examples (27 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.