SKILLEMALL.ai

BC xiaozhi-math-error-dna

初中数学错题的根因深度分析:把错题本判定的通用四维,细化为数学子类型(B/C/R/M + 两位编码)并做跨维度关联。典型触发:"为什么我数学总在同一个地方错""帮我分析我的数学错误规律""帮我生成数学弱项月报""我数学太差了"。不处理:错题的收录与次数统计(由 xiaozhi-correction-notebook 唯一负责,本 SKILL 只接收它推送的交接)、单题当场讲解(转 xiaozhi-math-problem-solving-coach)、概念重建(转 xiaozhi-math-concept-explainer)、分层练习(转 xiaozhi-math-gradient-trainer)。未获同意时,不建立长期档案、不跨SKILL共享。

ClawHub Hermes author: 小智伴学 v2.1.12 MIT-0 12 files body ≈ 3 067 tokens Open the sourceclawhub.ai analyzed 2 d ago

初中数学错题的根因深度分析:把错题本判定的通用四维,细化为数学子类型(B/C/R/M + 两位编码)并做跨维度关联。典型触发:"为什么我数学总在同一个地方错""帮我分析我的数学错误规律""帮我生成数学弱项月报""我数学太差了"。不处理:错题的收录与次数统计(由 xiaozhi-correction-notebook…

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

ProcedurePersonal productivitytype 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: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 329 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 "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. 8 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3067 tokens
  • 100Running it twice. No mutating operations

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 4 example trigger phrases
  • +3Description length 329: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (17 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +1License stated

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

External checks

ClawHub: clean
This is a coherent math-learning skill that uses disclosed, consent-gated student error profiles and does not include executable or hidden behavior.
LLM: benign (high) · VirusTotal: · 7 Sept 2026