githubnext · dsyme · Feb 22, 2026 · Feb 22, 2026
diff --git a/README.md b/README.md
@@ -20,6 +20,7 @@ A sample family of reusable [GitHub Agentic Workflows](https://github.github.com
 ### Research, Status & Planning Workflows
 
 - [📚 Weekly Research](docs/weekly-research.md) - Collect research updates and industry trends
+- [📊 Weekly Issue Summary](docs/weekly-issue-summary.md) - Weekly issue activity report with trend charts and recommendations
 - [👥 Daily Repo Status](docs/daily-repo-status.md) - Assess repository activity and create status reports
 - [👥 Daily Team Status](docs/daily-team-status.md) - Create upbeat daily team activity summaries with productivity insights
 - [📋 Daily Plan](docs/daily-plan.md) - Update planning issues for team coordination

diff --git a/docs/link-checker.md b/docs/link-checker.md
@@ -2,7 +2,7 @@
 
 **Workflow file:** [`.github/workflows/link-checker.md`](../.github/workflows/link-checker.md)
 
-## What it does
+## What It Does
 
 The Link Checker is an automated agentic workflow that:
 
@@ -12,7 +12,7 @@ The Link Checker is an automated agentic workflow that:
 4. **Remembers unfixable links** using cache memory to avoid repeated attempts
 5. **Creates pull requests** with the fixed links when changes are made
 
-## How it works
+## How It Works
 
 ````mermaid
 graph LR
@@ -66,7 +66,7 @@ The workflow maintains a persistent cache of unfixable broken links:
 
 This prevents the workflow from repeatedly attempting to fix links that are permanently broken.
 
-## When it runs
+## When It Runs
 
 - **Daily on weekdays** (automatic fuzzy scheduling)
 - **Manually** via workflow_dispatch (automatically enabled for fuzzy schedules)

diff --git a/docs/weekly-issue-summary.md b/docs/weekly-issue-summary.md
@@ -0,0 +1,73 @@
+# 📊 Weekly Issue Summary
+
+> For an overview of all available workflows, see the [main README](../README.md).
+
+The [Weekly Issue Summary workflow](../workflows/weekly-issue-summary.md?plain=1) generates a comprehensive weekly report on issue activity, including trend charts, resolution time analysis, and actionable recommendations. It runs automatically every Monday, giving maintainers a clear snapshot of repository health over the past week and the past 30 days.
+
+## Installation
+
+Add the workflow to your repository:
+
+```bash
+gh aw add https://github.com/githubnext/agentics/blob/main/workflows/weekly-issue-summary.md
+```
+
+Then compile:
+
+```bash
+gh aw compile
+```
+
+## How It Works
+
+````mermaid
+graph LR
+    A[Monday Schedule] --> B[Collect Issue Data]
+    B --> C[Generate Trend Charts]
+    C --> D[Upload Charts]
+    D --> E[Create Weekly Discussion]
+````
+
+Each Monday at 3 PM UTC, the workflow:
+
+1. **Collects issue data** — Queries issues opened and closed over the past 30 days, computing daily counts and resolution times
+2. **Generates trend charts** — Uses Python (pandas + matplotlib + seaborn) to produce two high-quality charts:
+   - **Issue Activity Trends** — Weekly opened vs. closed counts and running open total
+   - **Resolution Time Trends** — Average and median days-to-close over time
+3. **Uploads charts** — Stores charts as GitHub assets and collects their URLs
+4. **Creates a discussion** — Posts a `[Weekly Summary]` discussion in the **Audits** category with embedded charts, statistics, and recommendations
+
+Older `[Weekly Summary]` discussions are automatically closed when a new one is created, keeping the discussions list clean.
+
+## What You Get
+
+Each weekly discussion includes:
+
+- **Overview paragraph** comparing this week to last week
+- **Two embedded trend charts** showing activity and resolution patterns
+- **Key trends** highlighting common issue types, label distributions, and notable patterns
+- **Summary statistics table** with week-over-week comparisons
+- **Full issue list** in a collapsible section
+- **Recommendations** for the upcoming week
+
+## Configuration
+
+The workflow runs every Monday at 3 PM UTC. To change the schedule, edit the `cron` expression in the workflow frontmatter:
+
+```yaml
+on:
+  schedule:
+    - cron: "0 15 * * 1"  # Monday 3 PM UTC
+```
+
+## Requirements
+
+The workflow requires:
+
+- A **GitHub Discussions** category named `audits` (create it in your repository's Discussions settings)
+- Python 3 available on the Actions runner (standard on GitHub-hosted runners)
+- Network access to install Python packages (`pandas`, `matplotlib`, `seaborn`)
+
+## Permissions
+
+The workflow uses `issues: read` permission only — it reads issue data but never modifies issues.
diff --git a/workflows/weekly-issue-summary.md b/workflows/weekly-issue-summary.md
@@ -0,0 +1,151 @@
+---
+description: Creates weekly summary of issue activity including trends, charts, and insights every Monday
+timeout-minutes: 20
+strict: true
+on:
+  schedule:
+    - cron: "0 15 * * 1"  # Weekly on Mondays at 3 PM UTC
+  workflow_dispatch:
+permissions:
+  issues: read
+engine: copilot
+network:
+  allowed:
+    - defaults
+    - python
+tools:
+  edit:
+  bash:
+    - "*"
+  github:
+    lockdown: true
+    toolsets:
+      - issues
+safe-outputs:
+  upload-asset:
+  create-discussion:
+    title-prefix: "[Weekly Summary] "
+    category: "audits"
+    close-older-discussions: true
+steps:
+  - name: Setup Python environment
+    run: |
+      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
+
+```markdown
+### 📊 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)