SKILLEMALL.ai

BC ai-usage-notice

在我直接输出的成品(代码项目、网站、应用、脚本文档、文章、教程等)中默认添加「AI 使用说明」(AI 生成声明)的规范。依据《生成式人工智能服务管理暂行办法》《人工智能生成合成内容标识办法》,AI 生成内容应予以显式标识。适用范围:正式成品项目 + 单篇文档;对话内临时片段不强行加。除非用户明确说"不加",否则默认都加。触发词:AI使用说明、AI声明、AI生成、AI生成内容标识、生成式AI标识、人工智能生成声明。

ClawHub Agent Skills v1.0.0 4 files body ≈ 405 tokens Open the sourceclawhub.ai analyzed 2 d ago

在我直接输出的成品(代码项目、网站、应用、脚本文档、文章、教程等)中默认添加「AI 使用说明」(AI 生成声明)的规范。依据《生成式人工智能服务管理暂行办法》《人工智能生成合成内容标识办法》,AI 生成内容应予以显式标识。适用范围:正式成品项目 +…

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

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
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.
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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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. 19 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 405 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 208: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 19 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This skill is a disclosed policy for adding AI-use notices to AI-created deliverables, with no executable code or hidden data access.
LLM: benign (high) · VirusTotal: