Add charts and visualization to daily-code-metrics workflow

[github-actions]

Q Workflow Optimization Report

Context

This PR addresses @pelikhan's request in discussion #8467 to "add charts and visualization" to the daily-code-metrics workflow, following the pattern from the daily-issues-report workflow.

Issues Found (Analysis)

Daily Code Metrics Workflow

Current State:

• Uses shared/trending-charts-simple.md for basic chart support
• Generates text-based reports without visual charts
• No embedded images in discussion reports
• Missing asset upload capability for charts

Comparison with Daily Issues Report:

• Daily issues report uses shared/python-dataviz.md for full Python visualization
• Generates multiple high-quality charts with matplotlib/seaborn
• Uploads charts as assets and embeds them in discussions
• Professional 300 DPI charts with consistent styling

Changes Made

.github/workflows/daily-code-metrics.md

Import Updates:

• ✅ Replaced shared/trending-charts-simple.md with shared/python-dataviz.md
• ✅ Added shared/trends.md for trending analysis patterns

New Visualization Section:
Added comprehensive "Data Visualization with Python" section specifying 6 required charts:

1. loc_by_language.png - Bar chart of LOC distribution by programming language

   • Horizontal bars sorted by LOC
   • Percentage labels
   • Color-coded by language type

2. top_directories.png - Top 10 directories by LOC

   • Directory paths with LOC counts
   • Percentage of total codebase

3. quality_score_breakdown.png - Quality score component breakdown

   • Shows Test 30%, Organization 25%, Docs 20%, Churn 15%, Comments 10%
   • Current score vs target visualization

4. test_coverage.png - Test vs source code comparison

   • Test LOC vs source LOC
   • Test-to-source ratio visualization

5. code_churn.png - Top 10 most changed files (7 days)

   • Lines added/deleted per file
   • Net change highlighting

6. historical_trends.png - Multi-line time series

   • LOC, test coverage %, quality score over 30 days
   • Trend lines with multiple y-axes

Chart Quality Standards:

• DPI: 300 minimum for publication quality
• Figure size: 12x7 inches (consistent with daily-issues)
• Seaborn styling with professional color palettes
• Clear titles, labels, legends, grid lines
• Save with bbox_inches='tight' for proper cropping

Report Format Updates:

• Updated discussion structure to embed chart images
• Instructions for asset upload workflow
• Markdown image syntax for embedded charts
• Professional report layout with collapsible details

Python Script Structure:

• Template for data collection and analysis
• Chart generation workflow
• Historical data loading from repo-memory
• Metrics aggregation and storage

Expected Improvements

User Experience

• Visual insights: 6 embedded charts make trends immediately visible
• Professional reports: High-quality visualizations enhance credibility
• Better comprehension: Charts convey complex metrics at a glance
• Consistent branding: Matches style of daily-issues-report

Technical Benefits

• Reusable patterns: Uses established shared workflow imports
• Maintainable: Python scripts can be cached and reused
• Asset management: Charts uploaded as GitHub assets for permanent URLs
• Historical tracking: repo-memory integration for trend analysis

Specific Improvements

• ✅ Adds 6 high-quality visualization charts
• ✅ Enables asset upload for embedded images
• ✅ Provides Python script structure for data analysis
• ✅ Standardizes report format with visual components
• ✅ Maintains 15-minute timeout (efficient execution)
• ✅ Uses professional 300 DPI charts
• ✅ Follows patterns from daily-issues-report

Validation

Workflow compiled successfully:

✓ .github/workflows/daily-code-metrics.md (94.8 KB)
✓ Compiled 1 workflow(s): 0 error(s), 0 warning(s)

Changes:

• 1 file modified: .github/workflows/daily-code-metrics.md
• +179 lines added (chart specifications and documentation)
• -3 lines removed (replaced import)

Implementation Notes

The workflow now follows the same visualization pattern as daily-issues-report.md:

1. Shared imports: Uses python-dataviz.md for Python environment setup
2. Chart generation: Agent creates charts using matplotlib/seaborn
3. Asset upload: Charts uploaded using upload asset safe-output tool
4. Report embedding: Chart URLs embedded in markdown discussion

The agent will need to:

1. Collect code metrics using existing bash commands (cloc, git log, etc.)
2. Generate Python script to create 6 charts
3. Upload charts as assets
4. Create discussion with embedded chart images

References

• Related discussion: Daily Code Metrics Report - 2026-01-01 #8467 (Daily Code Metrics Report - 2026-01-01)
• Pattern source: .github/workflows/daily-issues-report.md
• Shared configs: shared/python-dataviz.md, shared/trends.md

Next Steps

After this PR is merged:

1. Next run of daily-code-metrics will generate visual charts
2. Discussion reports will include 6 embedded images
3. Historical trends will become visually trackable
4. Reports will match quality of daily-issues-report

---

Q Optimization: This PR adds the requested visualization capabilities while maintaining workflow efficiency and following established patterns from the repository.

AI generated by Q

---

Note

This was originally intended as a pull request, but the git push operation failed.

Workflow Run: View run details and download patch artifact

The patch file is available as an artifact (aw.patch) in the workflow run linked above.

To apply the patch locally:

# Download the artifact from the workflow run https://github.com/githubnext/gh-aw/actions/runs/20646646383
# (Use GitHub MCP tools if gh CLI is not available)
gh run download 20646646383 -n aw.patch

# Apply the patch
git am aw.patch

Show patch preview (242 of 242 lines)

From 4cce5a175f8fa04957eda188c936fa33978d54f0 Mon Sep 17 00:00:00 2001
From: Q Workflow Optimizer <q-optimizer@github.com>
Date: Thu, 1 Jan 2026 22:28:20 +0000
Subject: [PATCH] Add Python data visualization with 6 charts to
 daily-code-metrics workflow

- Replace trending-charts-simple with python-dataviz and trends imports
- Add 6 required charts: LOC by language, top directories, quality score breakdown, test coverage, code churn, historical trends
- Include detailed Python script structure and chart requirements
- Update report format to embed chart images via asset upload
- All charts use 300 DPI, 12x7 inches, seaborn styling for professional quality
- Follows pattern from daily-issues-report workflow
---
 .github/workflows/daily-code-metrics.md | 182 +++++++++++++++++++++++-
 1 file changed, 179 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/daily-code-metrics.md b/.github/workflows/daily-code-metrics.md
index a78042e..08869dd 100644
--- a/.github/workflows/daily-code-metrics.md
+++ b/.github/workflows/daily-code-metrics.md
@@ -26,7 +26,8 @@ timeout-minutes: 15
 strict: true
 imports:
   - shared/reporting.md
-  - shared/trending-charts-simple.md
+  - shared/python-dataviz.md
+  - shared/trends.md
 ---
 
 {{#runtime-import? .github/shared-instructions.md}}
@@ -66,20 +67,194 @@ Store as JSON Lines in `/tmp/gh-aw/repo-memory/default/history.jsonl`:
 
 For each metric: current value, 7-day % change, 30-day % change, trend indicator (⬆️/➡️/⬇️)
 
+## Data Visualization with Python
+
+Generate **6 high-quality charts** using Python (matplotlib, seaborn) and save to `/tmp/gh-aw/python/charts/`:
+
+### Chart Requirements
+
+All charts must:
+- Use DPI 300 minimum for quality
+- Figure size: 12x7 inches
+- Include clear titles, labels, legends
+- Use seaborn styling for professional appearance
+- Save as PNG with `bbox_inches='tight'`
+
+### Required Charts
+
+1. **loc_by_language.png** - Bar chart showing LOC distribution by programming language
+   - Ho
... (truncated)