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data-visualization-expert

Manus에서 실행

스타850

포크98

업데이트2026년 3월 12일 04:53

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beita6969

beita6969/ScienceClaw

GitHub 저장소 열기 Creator 저장소 보기

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Manus에서 실행

Data Visualization Expert

A dedicated skill for transforming raw data (CSV, JSON, Excel) into compelling visual narratives. Specializes in statistical and scientific plotting.

When to Use

Reports: Summarizing key metrics or KPIs.
Exploration: Initial data analysis (EDA) to find trends/outliers.
Publication: Generating figures for papers or presentations.
Comparison: Comparing models, cohorts, or experimental groups.

Core Capabilities

Code Generation: Creates Python scripts (Matplotlib, Seaborn, Plotly) or R code (ggplot2).
Style Enforcement: Adheres to specific journal/company branding (fonts, colors).
Data Cleaning: Preprocesses data (handle missing values, normalize) for plotting.
Artifact Management: Saves plots as PNG/SVG/PDF files.

Workflow

Load Data: Read input file (pd.read_csv()) and inspect columns/types.
Clean & Transform: Filter, pivot, or aggregate data as needed.
Generate Plot: Write plotting script with strict aesthetic controls.
Save & Verify: Execute script, check output file existence/size.

Example Usage

# Agent prompt:
"Visualize the distribution of 'Age' vs 'Income' from customers.csv"
# Triggers generation of `plot_age_income.py` using Seaborn scatterplot.

Guardrails

Privacy: Avoid plotting PII (names, emails) directly.
Accuracy: Ensure axes are labeled correctly with units.
Readability: Use appropriate scales (log vs linear) and avoid clutter.

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Search arXiv for preprints in physics, math, CS, quantitative biology, quantitative finance, statistics, electrical engineering, economics. Use when: (1) finding preprints by topic, (2) searching by author, (3) browsing arXiv categories, (4) getting paper metadata/abstracts. NOT for: published journal articles (use crossref-search), biomedical (use pubmed-search).

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Screen papers for systematic reviews using ASReview active learning. Use when: user has a large set of papers to screen for inclusion/exclusion, wants to prioritize relevant papers, or needs to reduce manual screening workload. NOT for: searching papers (use literature-search) or meta-analysis (use meta-analysis).

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Analyzes astronomical observations and cosmological models including telescope data processing, celestial mechanics calculations, stellar evolution, galaxy classification, and cosmological parameter estimation; trigger when users discuss stars, galaxies, exoplanets, dark matter, or the universe's large-scale structure.

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"Astronomical computations via Astropy. Use when: user asks about celestial coordinates, FITS files, or cosmological calculations. NOT for: telescope control or real-time observation planning."

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bioinformatics

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Performs bioinformatics analyses including pathway enrichment, gene ontology analysis, protein-protein interaction networks, multi-omics integration, and biological sequence database querying; trigger when users discuss gene sets, biological pathways, functional annotation, or omics data integration.

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name	data-visualization-expert
description	COPYRIGHT NOTICE

name: data-visualization-expert description: Generate insightful, publication-quality visualizations from complex datasets. keywords:

charts
plots
analysis
pandas
matplotlib
seaborn measurable_outcome: Create 3 high-resolution (300dpi) statistical plots (volcano, heatmap, scatter) within 15 minutes. license: MIT metadata: author: AI Agentic Skills Team version: "2.0.0" compatibility:
system: linux, macos allowed-tools:
run_shell_command
write_file
read_file

Data Visualization Expert

A dedicated skill for transforming raw data (CSV, JSON, Excel) into compelling visual narratives. Specializes in statistical and scientific plotting.

When to Use

Reports: Summarizing key metrics or KPIs.
Exploration: Initial data analysis (EDA) to find trends/outliers.
Publication: Generating figures for papers or presentations.
Comparison: Comparing models, cohorts, or experimental groups.

Core Capabilities

Code Generation: Creates Python scripts (Matplotlib, Seaborn, Plotly) or R code (ggplot2).
Style Enforcement: Adheres to specific journal/company branding (fonts, colors).
Data Cleaning: Preprocesses data (handle missing values, normalize) for plotting.
Artifact Management: Saves plots as PNG/SVG/PDF files.

Workflow

Load Data: Read input file (pd.read_csv()) and inspect columns/types.
Clean & Transform: Filter, pivot, or aggregate data as needed.
Generate Plot: Write plotting script with strict aesthetic controls.
Save & Verify: Execute script, check output file existence/size.

Example Usage

# Agent prompt:
"Visualize the distribution of 'Age' vs 'Income' from customers.csv"
# Triggers generation of `plot_age_income.py` using Seaborn scatterplot.

Guardrails

Privacy: Avoid plotting PII (names, emails) directly.
Accuracy: Ensure axes are labeled correctly with units.
Readability: Use appropriate scales (log vs linear) and avoid clutter.