BC xiaozhi-teach-physics-lesson-planner
帮初中物理老师做以物理观念为主线的教案：概念建构→规律教学→模型建构→应用训练→课堂小结，含分层与提问链。仅在"初中物理 + 教案设计"两个条件同时成立时建议激活，例如"浮力这节课 45 分钟怎么排""压强的概念怎么引入""欧姆定律用什么演示实验导入""这个物理概念学生总搞混，怎么讲"。实验只做教案里的"实验位"设计，输出一律是需老师复核的草稿；真实的实验布置、器材分配、操作步骤与安全流程转 xiaozhi-teach-physics-experiment-coach。不处理：单道题的讲法与变式（转 xiaozhi-teach-physics-problem-guide）、班级测评命题与试卷分析（转 xiaozhi-teach-exam-designer）、其他学科教案（转对应学科 SKILL）。
帮初中物理老师做以物理观念为主线的教案：概念建构→规律教学→模型建构→应用训练→课堂小结，含分层与提问链。仅在"初中物理 + 教案设计"两个条件同时成立时建议激活，例如"浮力这节课 45…
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: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 353 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. 24 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3781 tokens
- 100Running it twice. No mutating operations
- low 16 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
- -215 emoji in the instructions: noise for the model
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 353: enough signal without eating the budget
- +4Structure: 45 headings
- +3Step-by-step instructions: 24 items
- +4Has examples (32 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.