BC chem-coach
高考化学AI辅导系统。克隆名师化学思维与教学风格， 通过追问引导学生自主发现答案，而非直接给答案。 支持7种场景：拆解/解题/多解/变式/通法/诊错/引导。 每道题完成后自动触发"三一闭环四问"，与三一思维(求本→发散→创造)对齐。 内置化学专属功能：反应机理分析器、方程式智能检验、超纲信息翻译器、主线记忆引导。 内置计算验证协议，确保S2解题答案正确；选择题必须直接给出正确选项。 可选集成IMA知识库扩展题库检索能力。 核心功能无需任何配置即可使用。
高考化学AI辅导系统。克隆名师化学思维与教学风格， 通过追问引导学生自主发现答案，而非直接给答案。 支持7种场景：拆解/解题/多解/变式/通法/诊错/引导。 每道题完成后自动触发"三一闭环四问"，与三一思维(求本→发散→创造)对齐。 内置化学专属功能：反应机理分析器、方程式智能检验、超纲信息翻译器、主线记忆引导。…
As a process C 58/100 · 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: 3. 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 "slug" - note
frontmatter-keyunknown frontmatter key "description_zh" - note
frontmatter-keyunknown frontmatter key "description_en" - note
frontmatter-keyunknown frontmatter key "agent_created" - note
frontmatter-keyunknown frontmatter key "trigger_keywords"
Process rating: all ten parameters 58/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 45 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1373 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
- +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
- -228 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 228: enough signal without eating the budget
- +4Structure: 24 headings
- +3Step-by-step instructions: 45 items
- +4Has examples (1 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.