Community / AGENT SKILL

data-analysis

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Analyze, transform, clean, and visualize structured datasets using Python REPL, pandas, numpy, and plotting libraries. Trigger with "analyze data", "process CSV", "EDA", "generate plots", "statistical summary", or when computing numerical results.

One new superpower.

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npx skillycli add https://github.com/ruskicoder/system-prompts --skill data-analysisView original source

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The full skill.

Original instructions from the publisher’s SKILL.md

<!-- Generated from skills/data-analysis.md by tools/generate_integrations.py. Edit the source file, not this one. -->

# Skill: Data Analysis & Processing

## Purpose
Analyze, transform, and visualize data using computational tools. _Source: Colab (Category A)_

## Tools Required
- Python REPL / code execution _Source: OpenAI (Category C)_
- Data processing libraries (numpy, pandas, scipy) _Source: Colab (Category A)_
- Visualization libraries (matplotlib, plotly, seaborn) _Source: Colab (Category A)_
- File I/O for various formats (CSV, JSON, Excel, Parquet) _Source: Colab (Category A)_

## General Principles
- Use step-by-step computation — never rely on memorized results _Source: Lumo (Category B)_
- For arithmetic: calculate digit by digit before answering _Source: Kiro (Category O)_
- Verify results independently when possible _Source: Anthropic (Category J)_
- Prefer Python REPL over mental calculation for anything non-trivial _Source: OpenAI (Category C)_

## Computation Environment
When using Python for analysis: _Source: Colab (Category A)_
- Available libraries: numpy, scipy, pandas, seaborn, plotly, sympy, mpmath, statsmodels _Source: Colab (Category A)_
- Plotting: use plotly for interactive, matplotlib/seaborn for static _Source: Colab (Category A)_
- REPL is stateful — variables persist between calls _Source: Manus (Category F)_
- Timeout limits apply (typically 45-60s) — break long computations into chunks _Source: Manus (Category P)_

## Data Loading

### File Formats
| Format | Preferred Library | Notes |
|--------|-----------------|-------|
| CSV | pandas `read_csv` | Handle encoding, delimiter detection _Source: Colab (Category A)_ |
| JSON | `json` / pandas `read_json` | Nested structures need normalization _Source: Colab (Category A)_ |
| Excel | `openpyxl` / pandas `read_excel` | Multiple sheets, cell formatting _Source: Colab (Category A)_ |
| Parquet | pandas `read_parquet` | Columnar, efficient for large data _Source: Colab (Category A)_ |
| SQL | pandas `read_sql` | Database connection required _Source: Colab (Category A)_ |

### Data Cleaning
1. Check for nulls/missing values _Source: Colab (Category A)_
2. Validate data types _Source: Amp (Category E)_
3. Handle outliers (flag, don't silently remove) _Source: Amp (Category E)_
4. Normalize formats (dates, strings, numbers) _Source: Amp (Category E)_
5. Deduplicate if appropriate _Source: Amp (Category E)_

## Analysis Patterns

### Exploratory Data Analysis (EDA)
- Shape, dtypes, head/tail _Source: Colab (Category A)_
- Summary statistics (describe) _Source: Colab (Category A)_
- Distribution plots for numeric columns _Source: Colab (Category A)_
- Value counts for categorical columns _Source: Colab (Category A)_
- Correlation matrix for relationships _Source: Colab (Category A)_
- Missing value heatmap _Source: Colab (Category A)_

### Statistical Analysis
- Descriptive statistics: mean, median, std, quartiles _Source: Colab (Category A)_
- Hypothesis testing: t-test, chi-square, ANOVA _Source: Colab (Category A)_
- Regression: linear, logistic _Source: Colab (Category A)_
- Time series: trends, seasonality, decomposition _Source: Colab (Category A)_

### Data Transformation
- Filter rows, select columns _Source: Colab (Category A)_
- Group by + aggregate _Source: Colab (Category A)_
- Pivot / melt _Source: Colab (Category A)_
- Join / merge datasets _Source: Colab (Category A)_
- Apply custom functions _Source: Colab (Category A)_

## Visualization
- Line plots: time series, trends _Source: Colab (Category A)_
- Bar plots: categorical comparisons _Source: Colab (Category A)_
- Scatter plots: relationships between variables _Source: Colab (Category A)_
- Histograms: distributions _Source: Colab (Category A)_
- Heatmaps: correlation, matrices _Source: Colab (Category A)_
- Box plots: distribution comparison _Source: Colab (Category A)_

## Results Communication
- Present key findings prominently _Source: Lumo (Category B)_
- Support with visualizations when helpful _Source: Gemini (Category B)_
- Include uncertainty/confidence where relevant _Source: Anthropic (Category J)_
- Don't over-interpret — let the data speak _Source: Lumo (Category B)_
- Flag data quality issues that affect conclusions _Source: Anthropic (Category J)_

## Reproducibility
- Log transformation steps _Source: Factory (Category O)_
- Document assumptions _Source: Factory (Category O)_
- Note any data filtering/exclusion decisions _Source: Factory (Category O)_
- Random seeds for reproducible sampling _Source: Factory (Category O)_
- Version data processing scripts _Source: Factory (Category O)_