SKILLEMALL.ai
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AF data-analysis

Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats. Use this skill whenever the user mentions: analyzing data, CSV files, data insights, generating reports, data cleaning, exploratory analysis, business metrics, sales analysis, user behavior analysis, data visualization, creating dashboards, or asks questions like "what does this data tell us?" or "analyze this dataset". Also trigger when the user provides a CSV file path and asks for any kind of analysis or summary. This skill provides a professional 7-step workflow with quality checks, interactive cleaning strategy selection, and multiple output formats (Markdown report, interactive HTML, or full dashboard).

ClawHub Agent Skills author: yz6214589-hash v1.0.0 MIT-0 3 files body ≈ 5 742 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 57/100 · Will not run — References files that are not bundled: {chart.name}

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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
F
57/100
Will not run
References files that are not bundled: {chart.name}
Tools and files w 18
0
Running it twice w 4
30
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  2. The text references files that are not there: add them or drop the references.
For the model run — optional
  • 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 body-long SKILL.md body ≈ 5742 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: {chart.name}

Process rating: all ten parameters 57/100

Will not run. References files that are not bundled: {chart.name}
  • 0Tools and files. 1 referenced file(s) missing: {chart.name}
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (data-analysis) differs from the folder (yz6214589-hash-data-analysis)
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Failures and branches. 4 branches
  • 70Execution cost. Instruction body is 5742 tokens
  • 100Steps. 67 steps
  • 100Progress reporting. Reports progress
  • low 14 top-level sections: this looks like several domains in one skill
  • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 722: enough signal without eating the budget
  • +4Structure: 40 headings
  • +3Step-by-step instructions: 67 items
  • +3Output format is stated explicitly
  • +4Has examples (34 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This CSV analysis skill is mostly coherent, but it automatically installs Python packages and writes analysis outputs without a clear consent gate.
LLM: suspicious (high) · VirusTotal: · 29 May 2026