SKILLEMALL.ai

BC naming-expert

专业级命名创意引擎「灵犀取名官」,为品牌、产品、公司、人名、宠物、IP、项目等场景提供跨文化、多语言、高适配的名称方案。当用户需要取名、起名、命名、改名字、想名字、品牌取名、产品命名、公司取名、英文名、宝宝取名、宠物取名、网名、笔名、艺名时,使用此技能。即使用户只是提到"帮我想个名字"、"取个好名"等模糊表述,也应触发此技能。不适用于变量重命名、文件名修改、代码 refactoring、数据库字段改名等技术操作场景,也不适用于地址更名、行政区划改名等政务场景。

ClawHub Agent Skills author: qomob v0.1.1 MIT-0 2 files body ≈ 532 tokens Open the sourceclawhub.ai analyzed 23 h ago

专业级命名创意引擎「灵犀取名官」,为品牌、产品、公司、人名、宠物、IP、项目等场景提供跨文化、多语言、高适配的名称方案。当用户需要取名、起名、命名、改名字、想名字、品牌取名、产品命名、公司取名、英文名、宝宝取名、宠物取名、网名、笔名、艺名时,使用此技能。即使用户只是提到"帮我想个名字"、"取个好名"等模糊表述,也应触…

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 2. 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 54/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (naming-expert) differs from the folder (namingexpert)
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 44 steps
  • 100Execution cost. Instruction body is 532 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 232: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: clean
This is a simple naming-assistance skill with no executable code, sensitive access, or persistence; the only caveat is broad activation wording.
LLM: benign (high) · VirusTotal: · 12 Jul 2026