BC xiaozhi-teach-student-intake
把试听从"体验课"变成一次双向诊断，并按最小化原则给新学员建档。适用于老师说"新学员要试听""安排一节试听""试听课怎么上""学员档案怎么建""家长/孩子想学什么""试听完怎么记录""试听后怎么跟进"。流程：确认监护人同意 → 收最小必要信息 → 5W 需求访谈 → 5-10 分钟前测评 → 设计诊断式试讲 → 记录 5 维度观察 → 判断是否适配 → 建正式学员卡。范围到建档为止：不排课、不写课后记录、不登记作业、不做阶段报告、不谈续费与流失挽回，也不收集或存储任何联系方式。排课与课节状态转 schedule-manager，课后记录转 lesson-log，续费/阶段报告/流失跟进转 renewal-report。
把试听从"体验课"变成一次双向诊断，并按最小化原则给新学员建档。适用于老师说"新学员要试听""安排一节试听""试听课怎么上""学员档案怎么建""家长/孩子想学什么""试听完怎么记录""试听后怎么跟进"。流程：确认监护人同意 → 收最小必要信息 → 5W 需求访谈 → 5-10 分钟前测评 → 设计诊断式试讲 →…
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: 18. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 313 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 "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. 43 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3546 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 17 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
- -213 emoji in the instructions: noise for the model
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +3Description length 313: enough signal without eating the budget
- +4Structure: 44 headings
- +3Step-by-step instructions: 43 items
- +4Has examples (22 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.