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数据分析师自动化工作流。从数据加载、质量审计、数据清洗、探索性分析(EDA)、统计建模到可视化HTML报告生成，覆盖完整数据分析管线。支持CSV/Excel/JSON/SQLite多格式输入，内置4层数据防御体系。触发词：分析数据、数据分析、帮我分析数据、数据报告、EDA、data analysis、analyze data、生成数据报告、数据可视化、探索性分析。

ClawHub Agent Skills author: bettermen v1.0.0 MIT-0 11 files body ≈ 771 tokens Open the sourceclawhub.ai↗ analyzed 18 h ago

数据分析师自动化工作流。从数据加载、质量审计、数据清洗、探索性分析(EDA)、统计建模到可视化HTML报告生成，覆盖完整数据分析管线。支持CSV/Excel/JSON/SQLite多格式输入，内置4层数据防御体系。触发词：分析数据、数据分析、帮我分析数据、数据报告、EDA、data analysis、analyze…

As a process F 37/100 · Will not run — References files that are not bundled: templates/report.html

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
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
F
37/100
Will not run
References files that are not bundled: templates/report.html
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: templates/report.html
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 37/100

Will not run. References files that are not bundled: templates/report.html
  • 0Tools and files. 1 referenced file(s) missing: templates/report.html
  • 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
  • 40Consistency. Frontmatter name (data-analyst) differs from the folder (data-analyst-pipeline)
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 18 steps
  • 100Execution cost. Instruction body is 771 tokens
  • 100Running it twice. Mutating operations check current state

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 183: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 18 items
  • +4Has examples (3 code blocks)
  • +3All 7 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
This is a local data-analysis skill that reads user-selected datasets and creates reports; its risks are mainly privacy and output-handling cautions, not hidden or malicious behavior.
LLM: benign (high) · VirusTotal: · 13 Jun 2026