159 lines
5.0 KiB
Markdown
159 lines
5.0 KiB
Markdown
---
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description: Creates weekly summary of issue activity including trends, charts, and insights every Monday
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timeout-minutes: 20
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on:
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schedule: weekly on monday
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workflow_dispatch:
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permissions:
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issues: read
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network:
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allowed:
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- defaults
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- python
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tools:
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edit:
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bash:
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- "*"
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github:
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lockdown: true
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toolsets:
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- issues
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min-integrity: none # This workflow is allowed to examine and comment on any issues or PRs
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safe-outputs:
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upload-asset:
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create-discussion:
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title-prefix: "[Weekly Summary] "
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category: "audits"
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close-older-discussions: true
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steps:
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- name: Setup Python environment
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run: |
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mkdir -p /tmp/charts /tmp/data
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pip install --user --quiet numpy pandas matplotlib seaborn scipy
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python3 -c "import pandas, matplotlib, seaborn; print('Python environment ready')"
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---
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# Weekly Issue Summary
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Create a comprehensive weekly summary of issue activity for repository ${{ github.repository }}.
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## Step 1: Collect Issue Data
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Use GitHub API tools to gather data for the past 30 days:
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1. **Issue Activity Data** - Count of issues opened per day, closed per day, and running open count
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2. **Issue Resolution Data** - Average time to close issues, distribution of issue lifespans, breakdown by label
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Fetch enough issues to compute weekly and daily trends over the past 30 days. Use the GitHub toolset to query issues filtered by `created` and `closed` dates.
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## Step 2: Generate Trend Charts
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Write Python scripts to create exactly 2 high-quality trend charts and execute them via bash.
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### Chart 1: Issue Activity Trends
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Save data to `/tmp/data/issue_activity.csv` with columns: `date,opened,closed,open_total`
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Generate a multi-line chart:
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- Issues opened per week (bar or line)
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- Issues closed per week (bar or line)
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- Running total of open issues (secondary line)
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- X-axis: last 12 weeks, Y-axis: count
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- Save as `/tmp/charts/issue_activity_trends.png` at 300 DPI, 12×7 inches
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- Use seaborn whitegrid style with a professional color palette
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### Chart 2: Issue Resolution Time Trends
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Save data to `/tmp/data/issue_resolution.csv` with columns: `date,avg_days,median_days`
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Generate a line chart with moving average overlay:
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- Average time to close (7-day moving average line)
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- Median time to close
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- Shaded variance band
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- X-axis: last 30 days, Y-axis: days to resolution
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- Save as `/tmp/charts/issue_resolution_trends.png` at 300 DPI, 12×7 inches
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Run your Python scripts via bash and verify the charts exist before proceeding.
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### Python Notes
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- Use pandas for data manipulation and datetime handling
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- Use `matplotlib.pyplot` and `seaborn` for visualization
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- Apply `plt.tight_layout()` before saving
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- Handle sparse data gracefully (use bar charts if fewer than 7 data points)
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- Set `matplotlib.use('Agg')` to avoid display errors in headless environments
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## Step 3: Upload Charts
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Upload both chart images using the `upload-asset` safe output tool. Collect the returned URLs to embed in the discussion.
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## Step 4: Create Weekly Discussion
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Create a discussion with the title format: `Weekly Summary - [YYYY-MM-DD]`
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### Formatting Guidelines
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- Use `###` for main sections, `####` for subsections (discussion title is the h1)
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- Wrap long lists in `<details><summary>` collapsible sections
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- Keep critical information (overview, trends, statistics, recommendations) always visible
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- Keep optional detail (full issue lists, verbose breakdowns) in collapsible sections
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### Discussion Structure
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```markdown
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### 📊 Weekly Overview
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[1–2 paragraphs: total issues opened and closed this week, how that compares to the previous week, key theme or pattern in the issues]
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### 📈 Issue Activity Trends
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#### Weekly Activity Patterns
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[2–3 sentences: describe the trend - are issues accumulating, being resolved quickly, or holding steady?]
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#### Resolution Time Analysis
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[2–3 sentences: how quickly are issues being resolved? improving or slowing down?]
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### 🔑 Key Trends
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[Bullet list of 3–5 notable patterns: common issue types, label distribution, new contributors filing issues, recurring topics, etc.]
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### 📋 Summary Statistics
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| Metric | This Week | Last Week | Trend |
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|--------|-----------|-----------|-------|
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| Issues Opened | X | X | ↑/↓/→ |
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| Issues Closed | X | X | ↑/↓/→ |
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| Currently Open | X | X | ↑/↓/→ |
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| Avg Close Time | X days | X days | ↑/↓/→ |
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<details>
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<summary><b>Full Issue List (This Week)</b></summary>
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[Numbered list of all issues opened this week with title, number, author, labels]
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</details>
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### 💡 Recommendations for Upcoming Week
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[3–5 actionable suggestions: which issues to prioritize, patterns that suggest backlog growth, labels that need attention, etc.]
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```
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## Step 5: Notes
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- If fewer than 7 days of data are available, generate charts with available data and note the limited range
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- If no issues exist this week, still create a discussion noting the quiet week
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- Always create the discussion even if charts fail to generate (omit chart sections and explain)
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