BC xiaozhi-teach-lesson-planner
用 UbD 逆向设计把"经验型备课"变成可观测的教学设计。当老师说"帮我设计一节《一次函数》新课"、"写一份物理教案"、"做一份分层教案"、"帮我设计一节讲评课"、"这节课的提问链草案"时，建议激活此 SKILL。工作流：预期结果 → 评估证据 → 核心素养目标 → 环节时间矩阵 → 提问链草案 → A/B/C 分层。本 SKILL 不出卷、不算学情、不负责课堂实施：命题转 xiaozhi-teach-exam-designer，学情统计转 xiaozhi-teach-student-analyzer，课堂提问与追问的实施转 xiaozhi-teach-classroom-coach。
用 UbD 逆向设计把"经验型备课"变成可观测的教学设计。当老师说"帮我设计一节《一次函数》新课"、"写一份物理教案"、"做一份分层教案"、"帮我设计一节讲评课"、"这节课的提问链草案"时，建议激活此 SKILL。工作流：预期结果 → 评估证据 → 核心素养目标 → 环节时间矩阵 → 提问链草案 → A/B/C…
As a process C 55/100 · 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: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 296 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 55/100
- 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
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 27 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3608 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- 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 5 example trigger phrases
- +3Description length 296: enough signal without eating the budget
- +4Structure: 36 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (19 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.