# Data Analysis

> Structured data analysis with statistical rigor, automated EDA, and publication-quality visualizations. Use when the user asks to analyze data, analyze a dataset, create charts, visualize data, compute statistics, or work with CSV and spreadsheet files.

- **Type:** Skill
- **Install:** `agentstack add skill-droxer-synapse-data-analysis`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [droxer](https://agentstack.voostack.com/s/droxer)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [droxer](https://github.com/droxer)
- **Source:** https://github.com/droxer/Synapse/tree/main/backend/agent/skills/bundled/data-analysis

## Install

```sh
agentstack add skill-droxer-synapse-data-analysis
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Data Analysis Methodology

Prioritize correctness over speed — a wrong insight is worse than no insight.

## Step 1: Data Ingestion

Uploaded files are located at `/home/user/uploads/`. Always list that directory first to discover available files:

```python
import os
for f in os.listdir('/home/user/uploads/'):
    print(f)
```

**Tool selection:** Use `code_run` as the primary execution tool — it is universally supported across all sandbox providers. Use `code_interpret` only when you need rich output capture (e.g., inline dataframes, rendered plots); note that `code_interpret` may not be available in all environments.

Examine the raw file (first 20-30 lines) to understand format, delimiter, encoding, headers, and obvious quality issues before loading.

Load by file type — always specify dtypes for known columns and parse_dates for date columns:

| Extension | Loader |
|---|---|
| `.csv` | `pd.read_csv('/home/user/uploads/file.csv', parse_dates=[...])` |
| `.tsv` | `pd.read_csv('/home/user/uploads/file.tsv', sep='\t', parse_dates=[...])` |
| `.xlsx` / `.xls` | `pd.read_excel('/home/user/uploads/file.xlsx', engine='openpyxl')` |
| `.json` | `pd.read_json('/home/user/uploads/file.json')` |
| `.parquet` | `pd.read_parquet('/home/user/uploads/file.parquet')` |

## Step 2: Mandatory EDA

Run this diagnostic block on every dataset before any analysis:

```python
print(f"Shape: {df.shape}")
print(f"\nDtypes:\n{df.dtypes}")
print(f"\nMissing values:\n{df.isnull().sum()[df.isnull().sum() > 0]}")
print(f"\nDuplicate rows: {df.duplicated().sum()}")
print(f"\nNumeric summary:\n{df.describe()}")

for col in df.select_dtypes(include='object').columns:
    n_unique = df[col].nunique()
    print(f"\n{col}: {n_unique} unique values")
    if n_unique 5 categories) |
| Relationship between two numeric variables | Scatter plot |
| Trend over time | Line chart |
| Part-of-whole | Stacked bar (NOT pie chart) |
| Correlation matrix | Heatmap |

Sandbox-specific rules:
- Use `matplotlib.use('Agg')` — there is no display
- Always call `plt.close()` after saving to free memory
- Save plots to `/home/user/output/` at 150 dpi — create the directory first: `os.makedirs('/home/user/output/', exist_ok=True)`
- When using `code_run`, pass saved file paths via the `output_files` parameter so artifacts are tracked. When using `code_interpret`, rich outputs (plots, dataframes) are auto-captured — but for file-based outputs (saved PNGs), prefer `code_run` with `output_files`
- Use colorblind-friendly palettes (`tab10`, `Set2`)

## Step 6: Report Structure

```
**Dataset Overview**: [rows × columns, date range if applicable]

**Key Findings**:
1. [Quantified finding — "Revenue increased 23% QoQ"]
2. [Finding with statistical backing — "Correlation r=0.82, p<0.001"]

**Data Quality Notes**: [Cleaning applied, missing data, caveats]

**Methodology**: [Tests/techniques used and why]
```

**Never say "X causes Y" from observational data alone.**

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [droxer](https://github.com/droxer)
- **Source:** [droxer/Synapse](https://github.com/droxer/Synapse)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** yes
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-droxer-synapse-data-analysis
- Seller: https://agentstack.voostack.com/s/droxer
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.