BC xiaozhi-teach-assignment-designer
把"全班同一份作业"变成分层、可批改、时长可控的任务卡。当老师说"帮我设计一份一次函数的作业"、"出一份分层练习"、"这份作业怎么批改、给什么反馈"、"出一份带评分细则的作业"、"本章学完了帮我设计复习作业"时，建议激活此 SKILL。工作流：知识点拆解 → 难度梯度 → A/B/C 任务卡（每题标预计用时）→ 评分标准 → 反馈模板 → 完成情况回写。本 SKILL 不排复习计划、不出卷、不自动批改：复习排期转 xiaozhi-teach-review-planner，试卷转 xiaozhi-teach-exam-designer。
把"全班同一份作业"变成分层、可批改、时长可控的任务卡。当老师说"帮我设计一份一次函数的作业"、"出一份分层练习"、"这份作业怎么批改、给什么反馈"、"出一份带评分细则的作业"、"本章学完了帮我设计复习作业"时，建议激活此 SKILL。工作流：知识点拆解 → 难度梯度 → A/B/C 任务卡（每题标预计用时）→…
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: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 270 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. 16 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3316 tokens
- 100Running it twice. No mutating operations
- 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
- +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 6 example trigger phrases
- +3Description length 270: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 16 items
- +4Has examples (20 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.