SKILLEMALL.ai

BD xiaozhi-math-word-problem-coach

初中数学应用题的"文字→方程"专项:只处理列不出式子这一步。典型触发:"这道数学应用题读了三遍不知道怎么列方程""不知道设什么为x""行程/工程/浓度/利润/增长率题总是列错式子""条件之间的关系理不清"。核心方法:数量关系三步提取法(识别量→用中文说关系→翻译成等式)。不处理:方程列出来之后的解方程与计算(转 xiaozhi-math-problem-solving-coach)、纯几何/代数运算题、错题收录与统计(转 xiaozhi-correction-notebook)、概念本身没建立(转 xiaozhi-math-concept-explainer)、物理的受力/电路应用题(转物理 SKILL)。

ClawHub Hermes author: 小智伴学 v2.1.12 MIT-0 10 files body ≈ 1 377 tokens Open the sourceclawhub.ai analyzed 33 h ago

初中数学应用题的"文字→方程"专项:只处理列不出式子这一步。典型触发:"这道数学应用题读了三遍不知道怎么列方程""不知道设什么为x""行程/工程/浓度/利润/增长率题总是列错式子""条件之间的关系理不清"。核心方法:数量关系三步提取法(识别量→用中文说关系→翻译成等式)。不处理:方程列出来之后的解方程与计算(转…

As a process D 42/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

Proceduretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
42/100
Unfinished process
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: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 307 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 42/100

  • 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
  • 25Steps. 1 steps
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1377 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)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 307: enough signal without eating the budget
  • +4Structure: 17 headings
  • +4Has examples (16 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +1License stated

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

External checks

ClawHub: clean
This is a disclosed math word-problem coaching skill with proportionate memory/OCR use and no executable payloads or hidden high-impact behavior.
LLM: benign (high) · VirusTotal: · 7 Sept 2026