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BC data-visualization-analyst

首席数据智能官 — 多维数据分析与可视化洞察。当用户提供任何形式的业务数据（截图、表格、文本、聊天记录、碎片化信息）并希望获得深度数据分析、业务诊断、象限定位、ROI归因、决策建议时触发。

ClawHub Agent Skills author: Kelsey Hsiao v2.1.0 MIT-0 3 files body ≈ 2 795 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

首席数据智能官 — 多维数据分析与可视化洞察。当用户提供任何形式的业务数据（截图、表格、文本、聊天记录、碎片化信息）并希望获得深度数据分析、业务诊断、象限定位、ROI归因、决策建议时触发。

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

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
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
Inputs and preconditions w 11
0
Failures and branches w 10
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")
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "language"
  • note frontmatter-key unknown frontmatter key "language_detection"

Process rating: all ten parameters 53/100

  • 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. 1 mutating operations with no state check
  • 60Steps. 79 steps, 4 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2795 tokens

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)
  • +3Description length 94: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 79 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)

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

External checks

ClawHub: clean
This skill is a disclosed business data analysis and charting assistant with no evidence of hidden execution, credential access, persistence, or destructive behavior.
LLM: benign (high) · VirusTotal: · 10 Jun 2026