SKILLEMALL.ai

BC dashboard-builder-pro

仪表盘构建工具专业版是一款面向团队的全功能本地仪表盘构建平台,在免费版基础上扩展多数据源聚合、高级图表库、模板管控系统、自发化可视化 QA、团队协作分享、告警规则与阈值通知等能力,适合中大型项目的数据可视化需求。核心能力:。第二步,描述多源看板需求: 第三步,配置凭据并运行抓取:。适用于独立开发者、企业团队和自动化工作流场景,提供结构化输出与错误处理机制,支持中文交互,即开即用 功能涵盖: dashboard, builder。

ClawHub Hermes author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 2 766 tokens Open the sourceclawhub.ai analyzed 2 d ago

仪表盘构建工具专业版是一款面向团队的全功能本地仪表盘构建平台,在免费版基础上扩展多数据源聚合、高级图表库、模板管控系统、自发化可视化 QA、团队协作分享、告警规则与阈值通知等能力,适合中大型项目的数据可视化需求。核心能力:。第二步,描述多源看板需求:…

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

IntegrationStripeResearchOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 216 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "pricing_tier"

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. Tools declared in frontmatter
  • 100Steps. 57 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2766 tokens
  • 100Running it twice. No mutating operations
  • low 20 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
  • +2Single-language instructions
  • +3Description length 216: enough signal without eating the budget
  • +4Structure: 60 headings
  • +3Step-by-step instructions: 57 items
  • +4Has examples (16 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
The skill is a coherent dashboard-building helper, but it asks for shell execution and sensitive data credentials without enough enforceable scoping or user-control safeguards.
LLM: suspicious (high) · 20 Aug 2026