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AB autoplot-skill

Automatically visualize any data file (CSV/Excel/JSON) with one command. No coding required. Keywords: data visualization, chart generator, CSV plot, CSV visualization, Excel chart, JSON visualization, automatic graph, no-code visualization, data analysis, plot generator, 数据可视化, 图表生成, CSV画图, Excel转图表 Use this skill when: - "Visualize this CSV file" - "Visualize CSV data" - "Create a chart from this data" - "Create chart from Excel" - "Plot these sales numbers" - "Plot my data without coding" - "Show me trends in this dataset" - "Make a bar chart from this Excel file" - "Generate an infographic from this JSON" - "Make a graph from JSON" - "Analyze and visualize this data" - "CSV转图表" - "数据可视化" - "Excel画图" Auto-detects data types, selects optimal chart type (line, bar, scatter, pie, histogram), and generates publication-ready visualizations in PNG, SVG, HTML, PDF formats.

ClawHub Agent Skills author: Zaosusu v0.1.0 MIT-0 5 files body ≈ 1 906 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 68/100 · Nearly there — weak spots: running it twice, progress reporting

AnalyzerPDFExcelData 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
B
68/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
When it triggers w 12
50
the three weakest of ten parameters · all ten

How to improve

    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: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 68/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 70 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1906 tokens
    • low 17 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

    • +3Description length 884: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 13 example trigger phrases
    • +4Description says when NOT to use the skill
    • +4Structure: 49 headings
    • +3Step-by-step instructions: 70 items
    • +3Output format is stated explicitly
    • +4Has examples (19 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: clean
    This is a local chart-generation skill that reads user-selected data files and writes visualization files, with no evidence of hidden network access or credential use.
    LLM: benign (high) · VirusTotal: benign · 28 May 2026