SKILLEMALL.ai

BC xiaozhi-teach-math-lesson-planner

数学教师的备课工具:把一节数学课的概念建构路径、例题示范与变式训练排成可上的教案。当老师说"这个数学概念怎么讲""数学教案怎么写""变式训练怎么设计""数学例题怎么选""数学概念怎么引入""学生这个数学概念总是混"时,建议激活此SKILL。核心工作流:概念建构四步(情境引入/抽象概括/应用辨析/体系化)→例题示范→变式训练→课堂小结→错例档案,输出对齐 2022 版课标核心素养与四级结果目标。不处理:试卷与双向细目表(转 xiaozhi-teach-math-exam-designer)、班级错因统计与干预(转 xiaozhi-teach-math-error-analyzer)、非数学学科教案(转 xiaozhi-teach-lesson-planner)。

ClawHub Hermes author: 小智伴学 v2.1.12 MIT-0 17 files body ≈ 2 828 tokens Open the sourceclawhub.ai analyzed 2 d ago

数学教师的备课工具:把一节数学课的概念建构路径、例题示范与变式训练排成可上的教案。当老师说"这个数学概念怎么讲""数学教案怎么写""变式训练怎么设计""数学例题怎么选""数学概念怎么引入""学生这个数学概念总是混"时,建议激活此SKILL。核心工作流:概念建构四步(情境引入/抽象概括/应用辨析/体系化)→例题示范→变…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerLearningtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
For the model run — optional
  • 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: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 334 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "grade_bands"
  • note frontmatter-key unknown frontmatter key "depends_on"
  • note frontmatter-key unknown frontmatter key "id"
  • note frontmatter-key unknown frontmatter key "min_platform_version"
  • note frontmatter-key unknown frontmatter key "max_round_limit"
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown 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. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2828 tokens
  • 100Running it twice. No mutating operations
  • low 14 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 6 example trigger phrases
  • +3Description length 334: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (21 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: clean
This is a math lesson-planning skill with disclosed, purpose-aligned classroom data handling and no executable or hidden install behavior found.
LLM: benign (high) · VirusTotal: · 7 Sept 2026