starter-workflows/agentic/weekly-issue-summary.md

description, timeout-minutes, on, permissions, network, tools, safe-outputs, steps

description	timeout-minutes	on	permissions	network	tools	safe-outputs	steps
Creates weekly summary of issue activity including trends, charts, and insights every Monday	20	schedule workflow_dispatch weekly on monday	issues read	allowed defaults python	edit bash github * lockdown toolsets min-integrity true issues none	upload-asset create-discussion title-prefix category close-older-discussions [Weekly Summary] audits true	name run Setup Python environment mkdir -p /tmp/charts /tmp/data pip install --user --quiet numpy pandas matplotlib seaborn scipy python3 -c "import pandas, matplotlib, seaborn; print('Python environment ready')"

Weekly Issue Summary

Create a comprehensive weekly summary of issue activity for repository ${{ github.repository }}.

Step 1: Collect Issue Data

Use GitHub API tools to gather data for the past 30 days:

1. Issue Activity Data - Count of issues opened per day, closed per day, and running open count
2. Issue Resolution Data - Average time to close issues, distribution of issue lifespans, breakdown by label

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.

Step 2: Generate Trend Charts

Write Python scripts to create exactly 2 high-quality trend charts and execute them via bash.

Chart 1: Issue Activity Trends

Save data to /tmp/data/issue_activity.csv with columns: date,opened,closed,open_total

Generate a multi-line chart:

• Issues opened per week (bar or line)
• Issues closed per week (bar or line)
• Running total of open issues (secondary line)
• X-axis: last 12 weeks, Y-axis: count
• Save as /tmp/charts/issue_activity_trends.png at 300 DPI, 12×7 inches
• Use seaborn whitegrid style with a professional color palette

Chart 2: Issue Resolution Time Trends

Save data to /tmp/data/issue_resolution.csv with columns: date,avg_days,median_days

Generate a line chart with moving average overlay:

• Average time to close (7-day moving average line)
• Median time to close
• Shaded variance band
• X-axis: last 30 days, Y-axis: days to resolution
• Save as /tmp/charts/issue_resolution_trends.png at 300 DPI, 12×7 inches

Run your Python scripts via bash and verify the charts exist before proceeding.

Python Notes

• Use pandas for data manipulation and datetime handling
• Use matplotlib.pyplot and seaborn for visualization
• Apply plt.tight_layout() before saving
• Handle sparse data gracefully (use bar charts if fewer than 7 data points)
• Set matplotlib.use('Agg') to avoid display errors in headless environments

Step 3: Upload Charts

Upload both chart images using the upload-asset safe output tool. Collect the returned URLs to embed in the discussion.

Step 4: Create Weekly Discussion

Create a discussion with the title format: Weekly Summary - [YYYY-MM-DD]

Formatting Guidelines

• Use ### for main sections, #### for subsections (discussion title is the h1)
• Wrap long lists in <details><summary> collapsible sections
• Keep critical information (overview, trends, statistics, recommendations) always visible
• Keep optional detail (full issue lists, verbose breakdowns) in collapsible sections

Discussion Structure

### 📊 Weekly Overview

[1–2 paragraphs: total issues opened and closed this week, how that compares to the previous week, key theme or pattern in the issues]

### 📈 Issue Activity Trends

#### Weekly Activity Patterns
![Issue Activity Trends]({chart_1_url})

[2–3 sentences: describe the trend  -  are issues accumulating, being resolved quickly, or holding steady?]

#### Resolution Time Analysis
![Issue Resolution Trends]({chart_2_url})

[2–3 sentences: how quickly are issues being resolved? improving or slowing down?]

### 🔑 Key Trends

[Bullet list of 3–5 notable patterns: common issue types, label distribution, new contributors filing issues, recurring topics, etc.]

### 📋 Summary Statistics

| Metric | This Week | Last Week | Trend |
|--------|-----------|-----------|-------|
| Issues Opened | X | X | ↑/↓/→ |
| Issues Closed | X | X | ↑/↓/→ |
| Currently Open | X | X | ↑/↓/→ |
| Avg Close Time | X days | X days | ↑/↓/→ |

<details>
<summary><b>Full Issue List (This Week)</b></summary>

[Numbered list of all issues opened this week with title, number, author, labels]

</details>

### 💡 Recommendations for Upcoming Week

[3–5 actionable suggestions: which issues to prioritize, patterns that suggest backlog growth, labels that need attention, etc.]

Step 5: Notes

• If fewer than 7 days of data are available, generate charts with available data and note the limited range
• If no issues exist this week, still create a discussion noting the quiet week
• Always create the discussion even if charts fail to generate (omit chart sections and explain)