BC lesson-plan-eval
评估职业教育教案（中职/高职），基于布鲁姆分类学、加涅学习结果分类、马扎诺新分类学、5E模式和职教标准。逆向挑刺审计核心问题、目标可测性、问题链递进、活动真实性、学情分析、教法匹配、目标-内容-评价一致性。务必触发：教案评估、教案检查、教学设计改进、教案评分、教研员挑刺、帮我看教案、教案有问题吗、这份教案怎么样、教学设计好不好、**教法匹配**、**学习动机**、**元认知训练**。即使用户没说'评估'，只要提到教案质量问题就应激活。
评估职业教育教案（中职/高职），基于布鲁姆分类学、加涅学习结果分类、马扎诺新分类学、5E模式和职教标准。逆向挑刺审计核心问题、目标可测性、问题链递进、活动真实性、学情分析、教法匹配、目标-内容-评价一致性。务必触发：教案评估、教案检查、教学设计改进、教案评分、教研员挑刺、帮我看教案、教案有问题吗、这份教案怎么样、教学…
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.
- 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: 5. 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") - note
frontmatter-keyunknown frontmatter key "last_updated"
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. 90 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1508 tokens
- 100Running it twice. No mutating operations
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- -215 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 219: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 90 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.