📊 Data Visualization Report - Random Sample Data

[github-actions[bot]]

📊 Data Visualization Report

Generated on: October 31, 2024

Summary

This report contains data visualizations generated from randomly generated sample data using Python scientific computing libraries. The visualizations demonstrate various chart types and statistical analysis techniques commonly used in data science workflows.

Generated Visualizations

Chart 1: Product Performance Comparison (Bar Chart)

This bar chart displays the performance comparison across five different products (Product A through Product E). Each bar represents the sales value for a product, with values ranging from 50 to 200 units. The chart uses color-coded bars with value labels for easy comparison.

Chart 2: Sales Time Series Analysis (Line Chart)

This time series visualization shows daily sales data over a full year (365 days). The solid line represents actual sales with seasonal patterns and random variation, while the dashed line shows the underlying upward trend. The data exhibits both trend (increasing from 100 to 150) and seasonality (periodic oscillations).

Chart 3: Correlation Analysis (Scatter Plot)

This scatter plot demonstrates a strong positive correlation between two variables (X and Y) across 200 data points. The red dashed line represents the linear regression fit, showing the relationship y = 1.5x + constant. Points are color-coded by Y value using a viridis colormap, making it easy to identify patterns in the data distribution.

Chart 4: Distribution Comparison (Histogram with KDE)

This distribution plot compares two groups (Group A and Group B) using overlapping histograms and Kernel Density Estimation (KDE) curves. Group A is centered around 100 with a standard deviation of 15, while Group B is centered around 120 with a standard deviation of 20. The KDE curves (blue and red lines) show the smooth probability density functions for each group.

Data Information

• Data Generation: Random sample data using NumPy with controlled patterns
• Sample Sizes:
  • Time series: 365 daily observations
  • Categories: 5 product categories
  • Scatter plot: 200 data points
  • Distributions: 500 samples per group
• Variables:
  • Time series: date, sales, trend
  • Categories: category, value
  • Scatter: x, y (with correlation)
  • Distributions: group_a, group_b (normal distributions)
• Patterns:
  • Time series shows upward trend with seasonal oscillations
  • Strong positive correlation in scatter data (slope ≈ 1.5)
  • Distributions show two distinct normal populations with different means

Libraries Used

• NumPy: Array processing and numerical operations (random data generation)
• Pandas: Data manipulation and analysis (CSV I/O, data structuring)
• Matplotlib: Chart generation (base plotting functionality)
• Seaborn: Statistical data visualization (styling and color palettes)
• SciPy: Scientific computing (Kernel Density Estimation)

Technical Details

All visualizations were created with the following quality settings:

• Resolution: 300 DPI (publication quality)
• Figure Size: 10x6 to 12x6 inches
• Style: Seaborn whitegrid with husl color palette
• Format: PNG with tight bounding boxes

Workflow Run

• Repository: githubnext/gh-aw
• Run ID: 18987462255
• Run URL: https://github.com/githubnext/gh-aw/actions/runs/18987462255

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This report was automatically generated by the Python Data Visualization Generator workflow.

AI generated by Python Data Visualization Generator

[pelikhan]

/q investigate why the image URLs to raw GitHub content point to owner/repo instead of githubnext/gh-aw

Review safe outputs data in artifact to locate where the error occurred.

[github-actions[bot]]

Agentic Q triggered by this discussion comment.

[github-actions[bot]]

✅ Investigation Complete - Fix Implemented

I've successfully investigated the image URL issue and implemented a fix.

🔍 Root Cause

The safe outputs MCP server generates image URLs using GITHUB_REPOSITORY and GITHUB_SERVER_URL environment variables:

const repo = process.env.GITHUB_REPOSITORY || "owner/repo";
const url = `${githubServer.replace("github.com", "raw.githubusercontent.com")}/${repo}/${normalizedBranchName}/${targetFileName}`;

The fallback to "owner/repo" was triggering because these standard GitHub Actions environment variables weren't being passed through to the MCP server environment configuration.

Location: pkg/workflow/js/safe_outputs_mcp_server.cjs (lines 419-420)

🔧 Solution

Modified pkg/workflow/mcp-config.go to pass GITHUB_REPOSITORY and GITHUB_SERVER_URL to the safe outputs MCP server for all engines:

For Copilot (passthrough syntax):

"GITHUB_REPOSITORY": "\\${GITHUB_REPOSITORY}",
"GITHUB_SERVER_URL": "\\${GITHUB_SERVER_URL}"

For Claude/Custom (direct reference):

"GITHUB_REPOSITORY": "$GITHUB_REPOSITORY",
"GITHUB_SERVER_URL": "$GITHUB_SERVER_URL"

For Codex (TOML format):

"GITHUB_REPOSITORY" = "${{ github.repository }}",
"GITHUB_SERVER_URL" = "${{ github.server_url }}"

📊 Impact

✅ Image URLs will correctly use githubnext/gh-aw instead of owner/repo
✅ URLs will point to valid raw GitHub content locations
✅ No breaking changes - these are standard GitHub Actions variables
✅ Fix applies uniformly across all engines (Copilot, Claude, Codex)

📝 Pull Request

I've created a pull request with the fix. The changes are minimal and surgical, modifying only the MCP server environment configuration to include the missing variables.

📚 Investigation Details

Full investigation report saved to: /tmp/gh-aw/cache-memory/q-image-url-investigation.md

The fix will be validated by CI/CD and can be tested with any workflow using the upload_assets safe output feature.

Related Items

• Pull Request: #2915

AI generated by Q for discussion #2913

[github-actions[bot]]

This discussion was automatically closed because it was created by an agentic workflow more than 1 week ago.