SKILLEMALL.ai

AD workbench-builder

个人工作台搭建器。通过5-7个简单问题了解用户的职业、工作需求和手机型号,自动生成个性化单页PWA工作台(含任务管理、日程、记账、项目模块等),部署到CloudStudio获得HTTPS网址,手机电脑均可添加桌面。触发词:搭建工作台、做个工作台、帮我建个工作台、个人工作台、workbench、dashboard。

ClawHub Agent Skills author: Drabbit777 v1.0.0 MIT-0 8 files body ≈ 1 822 tokens Open the sourceclawhub.ai analyzed 2 d ago

个人工作台搭建器。通过5-7个简单问题了解用户的职业、工作需求和手机型号,自动生成个性化单页PWA工作台(含任务管理、日程、记账、项目模块等),部署到CloudStudio获得HTTPS网址,手机电脑均可添加桌面。触发词:搭建工作台、做个工作台、帮我建个工作台、个人工作台、workbench、dashboard。

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
46/100
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.
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: 8. 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")
  • note frontmatter-key unknown frontmatter key "source"

Process rating: all ten parameters 46/100

  • 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
  • 30Running it twice. 3 mutating operations with no state check
  • 40Consistency. Frontmatter name (workbench-builder) differs from the folder (drabbit-workbench-builder)
  • 100Tools and files. No external tools needed
  • 100Steps. 80 steps
  • 100Execution cost. Instruction body is 1822 tokens
  • low 11 top-level sections: this looks like several domains in one skill

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
  • -219 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 157: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 80 items
  • +4Has examples (4 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: 74.

External checks

ClawHub: suspicious
The skill’s workbench-building purpose is mostly coherent, but its generated app can sync personal work, finance, and image data to CloudBase with weakly scoped anonymous access and incomplete privacy controls.
LLM: suspicious (medium) · VirusTotal: · 18 Aug 2026