SKILLEMALL.ai

BC xiaozhi-physics-error-dna

初中物理错题的根因分析与档案系统,做物理五维(图景/概念/公式/过程/数学工具)子类型定位与弱项报告。触发语示例:"我为什么总在受力分析上出错""浮力题老是错在哪""电学题每次都算错""帮我分析物理错误规律""我物理是不是没天赋"。学科判别:错题涉及力、压强、浮力、电路、光路、物态变化等物理量时归本 SKILL;纯代数与几何错因转数学错误DNA。不处理:错题的初始收录与 28 天累计计数(由通用错题本唯一负责)、解题过程引导(转物理解题教练)、概念重建(转物理概念直觉器)。

ClawHub Hermes author: 小智伴学 v2.1.12 MIT-0 15 files body ≈ 2 683 tokens Open the sourceclawhub.ai analyzed 29 h ago

初中物理错题的根因分析与档案系统,做物理五维(图景/概念/公式/过程/数学工具)子类型定位与弱项报告。触发语示例:"我为什么总在受力分析上出错""浮力题老是错在哪""电学题每次都算错""帮我分析物理错误规律""我物理是不是没天赋"。学科判别:错题涉及力、压强、浮力、电路、光路、物态变化等物理量时归本…

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
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 239 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. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2683 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 239: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (14 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
The skill is a disclosed physics learning-profile assistant with consent gates and no executable or hidden behavior, though its bundled profile schema should be kept narrowly scoped.
LLM: benign (high) · VirusTotal: · 7 Sept 2026