SKILLEMALL.ai

CC xiaozhi-chemistry-problem-coach

初中到高中(必修与选择性必修)的化学解题教练,按四步法(定物质→三重表征→写式列式→守恒检验)陪学生走完当前这一道化学题。默认只在当前会话工作:不读写长期档案、不归档错题、不排提醒;这三项要学生(需要时含监护人)明确开启后才做。触发语示例(须带上具体题目):"这道推断题从哪下手""这个方程式计算怎么列""反应后溶液里的溶质是什么""这几种物质怎么鉴别"。不触发:泛泛说"化学好难"、只问概念不带题、要"化学提分方法"。学科判别:题干出现化学式、元素符号、化学方程式、溶液浓度、实验装置时按化学题处理;只涉及力、电路等物理量时转物理解题教练。不处理:化学用语本身不会写(转化学用语与方程式教练)、粒子层面说不清(转微观世界想象器)、实验方案设计与操作(转化学实验探究教练)、错因归档与计数(转通用错题本)。

ClawHub Hermes author: 小智伴学 v2.11.1 MIT-0 16 files body ≈ 1 869 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

初中到高中(必修与选择性必修)的化学解题教练,按四步法(定物质→三重表征→写式列式→守恒检验)陪学生走完当前这一道化学题。默认只在当前会话工作:不读写长期档案、不归档错题、不排提醒;这三项要学生(需要时含监护人)明确开启后才做。触发语示例(须带上具体题目):"这道推断题从哪下手""这个方程式计算怎么列""反应后溶液里…

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

Proceduretype and topics are labelled automatically from the skill text
JSON
Technical rating
C
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: 16. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 353 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. 12 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1869 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 5 example trigger phrases
  • +3Description length 353: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 12 items
  • +4Has examples (7 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 Chinese K12 chemistry tutoring skill with disclosed, consent-gated memory and handoff behavior.
LLM: benign (high) · VirusTotal: · 8 Oct 2026