| version | 1 |
| name | data-analysis-workflow |
| description | Gather, clean, analyze, and visualize data from the web or uploaded files. Use when user wants to scrape data, analyze datasets, create charts, find trends, compare metrics, build dashboards, or generate data-driven reports. Triggers include "analyze data", "scrape and analyze", "create dashboard", "find trends", "compare metrics", "data visualization", "statistical analysis", "correlation analysis", "build chart", "data report". |
Data Analysis Workflow
Gather data from the web or uploaded files, clean and analyze it, then deliver results as interactive charts, spreadsheets, or deployed dashboards.
Sub-Skills
rebyteai/data-scraper — Scrape websites and extract structured data using Apify actors. 1000+ pre-built scrapers for Amazon, LinkedIn, YouTube, TikTok, Google, and general web pages. 5-minute timeout per run.rebyteai/internet-search — Quick web search for finding data sources, current statistics, benchmarks, and context.rebyteai/deep-research — Comprehensive multi-source research when the analysis requires verified facts from 10+ sources. Produces structured reports with citations.rebyteai/echarts — Interactive charts via pyecharts: bar, line, pie, scatter, heatmap, candlestick, radar, treemap. Output as self-contained HTML.rebyteai/spreadsheet-builder — Create shareable online spreadsheets with formulas, formatting, and live URLs.rebyteai/rebyte-app-builder — Deploy interactive dashboards and analysis results as web apps on rebyte.pro.
Workflow
Step 1: Understand the Analysis
Parse what the user wants. Identify:
- Data source — Web scraping, uploaded file, or public APIs/databases?
- Analysis type — Comparison, trend, distribution, correlation, ranking, forecasting?
- Metrics — What specific numbers or dimensions matter?
- Output — Charts, spreadsheet, dashboard, or written report?
If the user uploads a file, the data source is the file. If they ask to "research" or "compare" something, you'll need to gather data first.
Step 2: Gather Data
Choose the right tool based on the data source:
From the web (structured scraping):
Use data-scraper when the user wants data from specific websites:
- Product listings (Amazon, eBay) → use appropriate Apify actor
- Job postings (LinkedIn, Indeed) → use job scraping actor
- Social media (YouTube, TikTok) → use platform-specific actor
- General websites → use web scraper actor with CSS selectors
Always save scraper output to a file (never print to stdout — too large).
From the web (research/stats):
Use internet-search for quick lookups — current market data, published statistics, benchmarks.
Use deep-research when the analysis needs verified facts from multiple sources (e.g., market size, industry trends, competitive landscapes).
From uploaded files:
Read the file directly. Supported: CSV, XLSX, JSON, TSV, text files.
- Use pandas for loading and initial inspection
- Check for missing values, data types, encoding issues
- Preview first rows to understand structure
Combining sources:
Many analyses need both scraped data and context. Example: scrape Amazon laptop prices (data-scraper), then look up expert review scores (internet-search) to cross-reference.
Step 3: Clean & Explore
Before analysis, clean the data:
- Inspect — Shape, columns, types, sample rows
- Clean — Handle missing values, fix types (dates, numbers), remove duplicates
- Standardize — Consistent naming, units, date formats
- Derive — Calculate new columns needed for analysis (ratios, growth rates, categories)
For large datasets, compute summary statistics first:
- Numeric: count, mean, median, std, min, max, percentiles
- Categorical: unique values, frequency distribution
- Temporal: date range, gaps, seasonality indicators
Step 4: Analyze
Choose analysis methods based on what the user wants:
| Goal | Method |
|---|
| Compare items | Ranking, side-by-side tables, normalized scores |
| Find trends | Time series, moving averages, growth rates, YoY change |
| Show distribution | Histograms, percentiles, box plots, frequency tables |
| Find correlation | Scatter plots, correlation matrix, regression |
| Identify outliers | IQR method, z-scores, visual inspection |
| Forecast | Linear regression, moving average projection |
| Segment/cluster | Group by categories, percentile buckets, cross-tabulation |
| Measure performance | KPIs, benchmarks, index comparisons |
For statistical analysis, use Python (pandas, numpy, scipy) inside the sandbox. Calculate:
- Descriptive statistics (mean, median, std, IQR)
- Correlation coefficients
- Growth rates (CAGR, MoM, YoY)
- Significance tests when comparing groups
Step 5: Visualize
Use echarts to create interactive charts. Match chart type to analysis:
| Analysis | Chart Type |
|---|
| Comparison (few items) | Horizontal bar chart |
| Comparison (many items) | Sorted bar chart with top-N highlight |
| Trend over time | Line chart with markers at key events |
| Distribution | Histogram or box plot |
| Composition | Pie chart (≤6 slices) or stacked bar |
| Correlation | Scatter plot with trend line |
| Ranking | Horizontal bar chart, sorted descending |
| Geographic | Map (if location data) |
| Multi-metric dashboard | Grid of small charts |
Always include:
- Clear title describing the insight (not just "Chart 1")
- Axis labels with units
- Tooltips with exact values
- Legend for multi-series
- Source attribution if data was scraped
Step 6: Deliver
Choose delivery format based on complexity:
Simple analysis (1-2 charts, one dataset):
- Create charts as HTML files via
echarts
- Create a spreadsheet with raw data + analysis via
spreadsheet-builder
- Write a text summary of key findings
Comprehensive analysis (multiple charts, insights):
- Deploy an interactive dashboard with
rebyte-app-builder
- Include charts, data tables, and narrative on one page
- Share the live URL
Data-heavy output (raw data for further use):
- Create a structured spreadsheet via
spreadsheet-builder
- Include raw data sheet + summary/analysis sheet
- Add formulas for recalculation
After delivering:
- Summarize the top 3-5 key findings
- Note data limitations (sample size, date range, source reliability)
- Suggest follow-up analyses if relevant
Decision Points
-
"data-scraper or internet-search?" — Use data-scraper when you need structured data from specific websites (product listings, job postings, profiles). Use internet-search when you need published facts, statistics, or to find data sources. Use deep-research when you need a comprehensive report with verified multi-source findings.
-
"echarts or spreadsheet-builder?" — Use echarts for visual storytelling (interactive charts as HTML). Use spreadsheet-builder for data you want the user to explore, filter, or modify. Most analyses benefit from both.
-
"Single charts or dashboard?" — If the analysis has one clear story (e.g., price comparison), use individual charts. If it has multiple dimensions (e.g., full market analysis with pricing, ratings, trends, and segmentation), deploy a dashboard with rebyte-app-builder.
-
"How much data to scrape?" — Match the analysis need. For ranking/comparison: 20-50 items is usually enough. For statistical analysis: aim for 100+ data points. For trend analysis: at least 12 time periods. Always respect the 5-minute scraper timeout.
-
"Uploaded file: analyze or just process?" — If the user says "analyze," provide insights, stats, and visualizations. If they say "clean up" or "restructure," focus on data transformation without adding analysis. Ask if unclear.
